Temperature-based weather derivatives as a technique for maize production hedging

i
TEMPERATURE-BASED WEATHER DERIVATIVES AS A TECHNIQUE
FOR MAIZE PRODUCTION HEDGING
by
SAGE JARROW YOUNG
MINOR DISSERTATION
Submitted in partial fulfilment of the requirements for the degree
MAGISTER COMMERCII
in
FINANCIAL MANAGEMENT
in the
Faculty of Economic and Financial Sciences
at the
UNIVERSITY OF JOHANNESBURG
Supervisor: Mr. A. Kruger
Co - Supervisor: Mr. R. van der Walt
April 2013

i
Acknowledgements
Firstly, I would like to thank my supervisor Mr Ricky van der Walt for all the
guidance and patience shown during the development of this research paper.
From an academic perspective his suggestions and advice was invaluable
and without it I would still be floundering in the duldrums of an academic
wasteland trying to understand the difference between qualitative and
quantitative.
Secondly, Mr Michael John Young, my Dad who will back me no matter what
– even when he doesn’t quite understand what it is that I do. I can’t blame
him, sometimes I don’t. His tireless enthusiasim for my personal development
and his work ethic is a huge part of what inspires me. Our mantra ‘Middle
Wicket’, has most certainly paid off.
Finally, Dr Charmaigne March, my Mom, my academic wonderwoman. Who is
always ready to encourage, always willing to debate and is my biggest critic
and fan. A relentless thirst for knowledge and a brilliant academic role model
who was able to push me to the conclusion of this paper. I am fortunate to
have this academic giant in my corner, upon whose shoulders I am able to
stand.

ii
Abstract
This paper investigates the use of weather derivatives in the maize production
industry of South Africa. The history, users and mechanics of weather
derivatives and maize production are presented in the study. This study
examines, by using experiential design, the potential revenue for a control and
a test group of farmers using monthly, actual maize production and weather
observations for the period 2000 - 2010. This study suggests, with reference
to the results, an option strategy that ultimately results in the hedging of maize
output risk for the farms investigated. Limitations of the study are basis risk,
liquidity, the difficulties in pricing of the weather derivative and finally the
reticence of agricultural business to explore these hedging instruments in
practise. In conclusion the study presents suggestions for further research
into the wider application of weather derivatives into other industries, the
exploration of the effects of weather on changes in crop yield and the effects
of a hybrid maize crop and its possible resilience to weather changes. This
study also demonstrates the weather effects on maize output and suggests a
hedging solution to yield.
Key Words
Weather, Derivatives, Maize, Risk, Hedging, Weather Risk, Weather
Derivatives, Free State Province, South Africa, Maize, Maize production,
Black and Scholes, South African Weather Bureau, GrainSA, Agriculture, Risk
Management

iii
Table of contents
Acknowledgements...........................................................................................................i
Abstract..............................................................................................................................ii
Key Words .........................................................................................................................ii
Table of contents.............................................................................................................iii
List of tables ...................................................................................................................viii
List of figures....................................................................................................................ix
List of abbreviations .........................................................................................................x
Chapter 1............................................................................................................................10
1.1 Introduction and background to the study....................................................1
1.2 Factors identified from the literature ..............................................................3
1.2.1 The weather and risk........................................................................................3
1.2.2 Weather types and its effects on industry .....................................................4
1.2.3 Derivatives..........................................................................................................6
1.2.4 Basic temperature derivative instruments .....................................................6
1.2.5 South African maize industry ..........................................................................7
1.3 The purpose of this study ..................................................................................8
1.4 Research problem................................................................................................9
1.5 Research methodology.......................................................................................9
1.6 Collecting and analysing the information....................................................10
1.7 Limitations of this study...................................................................................10
1.8 Chapter outline....................................................................................................12
Chapter 2............................................................................................................................14
Chapter 2.............................................................................Error! Bookmark not defined.
2 Literature review ........................................................................................................14

iv
2.1 Introduction to weather derivatives...............................................................14
2.1.1 The mechanics of weather derivatives ........................................................15
2.1.1.1 The contract period..................................................................................15
2.1.1.2 A measurement station ...........................................................................15
2.1.1.3 A weather variable ...................................................................................15
2.1.1.4 An index.....................................................................................................16
2.1.1.5 A structure .................................................................................................16
2.1.1.6 An option premium ...................................................................................17
2.1.1.7 Payoff structures from weather derivatives ..........................................17
2.2 History of weather derivatives ........................................................................20
2.3 Weather derivatives vs. traditional derivatives ..........................................21
2.4 Weather derivatives vs. weather insurance ................................................22
2.4.1 Nature of the risk.............................................................................................22
2.4.2 Payout...............................................................................................................22
2.4.3 Performance monitoring.................................................................................23
2.4.4 Cost...................................................................................................................23
2.4.5 Counterparties.................................................................................................24
2.4.6 Speculation ......................................................................................................24
2.5 The users of the weather derivative market................................................24
2.5.1 Theme parks and sporting events ................................................................24
2.5.2 Construction.....................................................................................................25
2.5.3 Clothing.............................................................................................................25
2.5.4 Agriculture ........................................................................................................25
2.5.5 Energy Companies .........................................................................................25
2.6 Companies that offer weather derivative services ....................................27
2.7 The growth in the weather derivative market..............................................28
2.8 Value of the weather derivative market ........................................................29
2.9 Introduction to temperature derivatives.......................................................31
2.10 The structure of a typical temperature derivative ...................................32
2.11 The test temperature derivative ...................................................................32

v
2.11.1 Hot Year derivative .........................................................................................33
2.11.2 A cold year derivative .....................................................................................34
2.12 Physical marketplace for weather derivatives .........................................34
2.13 Pricing weather derivatives ...........................................................................35
2.13.1 The benchmark approach..............................................................................36
2.13.2 Burn Analysis for pricing weather derivatives.............................................37
2.13.3 Temperature-based valuation models .........................................................38
2.13.4 Monte Carlo Simulation..................................................................................39
2.13.5 Black and Scholes temperature modelling..................................................40
2.14 Examples of weather derivatives .................................................................41
2.14.1 The International Finance Corporation (IFC)..............................................41
2.14.2 Electricity forwards, futures and swaps .......................................................42
2.14.3 Californian wine production ...........................................................................42
2.14.4 Weather Derivatives in Malawi......................................................................42
2.14.5 Ice wine production in Ontario, Canada ......................................................43
2.14.6 The World Food Programme (WFP)............................................................44
2.14.7 Electricity producer: KeySpan Corp .............................................................44
2.14.7.1 Terms of the Key Span Corporation’s weather derivative contract
45
2.15 Temperature modelling challenges.............................................................46
2.15.1 Data errors .......................................................................................................46
2.15.2 Weather station local ......................................................................................46
2.15.3 Length of time series data .............................................................................46
2.15.4 Urban island effect ..........................................................................................47
2.16 Maize production and climate change........................................................48
2.17 Maize production in South Africa ................................................................49
2.18 Risk associated with maize production .....................................................50
2.18.1 Price risk...........................................................................................................50
2.18.2 Event risk..........................................................................................................50
2.18.3 Output/yield risk...............................................................................................51

vi
2.19 Variables that affect maize production.......................................................51
2.19.1.1 Temperature ..........................................................................................51
2.19.1.2 Water ......................................................................................................52
2.19.1.3 Soil requirements..................................................................................52
2.19.1.4 Planting date..........................................................................................53
2.19.1.5 Planting depth and plant technique ...................................................53
2.19.1.6 Plant population and row width...........................................................53
2.19.1.7 Maize cultivar planning ........................................................................54
2.20 Maize production and climate change........................................................55
2.21 Summary.............................................................................................................56
Chapter 3............................................................................................................................57
3 Research methodology............................................................................................57
3.1 Research design.................................................................................................57
3.2 Research Population .........................................................................................57
3.3 Research instruments .......................................................................................58
3.3.1 Test experiment...............................................................................................58
3.3.2 Test instrument................................................................................................58
3.3.3 The use of the test instrument ......................................................................60
3.3.4 Data used in the experiment .........................................................................60
3.3.4.1 Temperature observations......................................................................60
3.3.4.2 Maize production data .............................................................................61
3.3.5 Temperature option strategy analysis model..............................................62
3.4 Summary...............................................................................................................63
Chapter 4............................................................................................................................64
4 Empirical analysis .....................................................................................................64
4.1 Presentation of results......................................................................................64
4.2 Statement of results...........................................................................................68
4.3 Summary...............................................................................................................69

vii
Chapter5.............................................................................................................................70
5 Summary of research findings...............................................................................70
5.1 Summary of research objective and major findings.................................70
5.2 Limitations of this study...................................................................................71
5.2.1 Basis risk..........................................................................................................71
5.2.2 Liquidity.............................................................................................................72
5.2.3 Traditional Arbitrage Free Pricing Models...................................................73
5.2.4 Modelling and pricing issues .........................................................................73
5.2.5 Agribusiness is reticent to weather derivatives ..........................................74
5.2.6 Spatial considerations ....................................................................................75
5.3 Suggestions for further research...................................................................75
5.4 Conclusion ...........................................................................................................76
List of references .............................................................................................................79

viii
List of tables
Table 1: Industry use of weather anomalies................................................................................. 5
Table 2: Type of risk observed Industry classification and weather derivative market share ...........27
Table 3: Terms of test temperature derivative.............................................................................33
Table 4: The terms of the test temperature derivative..................................................................59
Table 5: Temperature and maize output.....................................................................................64
Table 6: Average annual temperature analysis and option exercise function.................................65
Table 7: Revenue profile of the control and test famers over the sample time period.....................67
Table 8: Revenue summary ......................................................................................................68

ix
List of figures
FIGURE 2 Payout diagram of a long put HDD option..................................................19
FIGURE 3: Chicago Mercantile Exchange (CME) weather market: evolution
timeline (1999–2005).........................................................................................................21
FIGURE 4: Growth in the users of the weather derivative market and their industry
origin.....................................................................................................................................26
FIGURE 5: The growth in the weather derivatives market from January 2002 –
October 2008. .....................................................................................................................29
FIGURE 6: Notional Values for Weather Derivative contracts ...................................30
FIGURE 7 Example of Monte Carlo temperature simulations...................................40
FIGURE 8 Average temperature trend for Vlissingen..................................................48
Figure 9 Average annual temperature in the Free State Province from 2000 – 2010
..............................................................................................................................................61
Figure 10 Average maize output in the Free State province in Tonnes from 2000-
2010 .....................................................................................................................................62
Figure 11: Time series revenue comparison of the farmers from 2000 to 2010......69

x
List of abbreviations
CAPMCapital Asset Pricing Model
CDD Cooling Degree Day
CME Chicago Mercantile Exchange
DFID Department for International Development
ETDC Exchange-traded Derivative Contracts
GOP Growth Optimal Portfolio
HDD Heating Degree Day
IFC International Finance Corporation
IPCC Intergovernmental Panel on Climate Change
OTC Over the counter
PWC Price Waterhouse Coopers
SADC Southern African Development Community
UK United Kingdom
USA United States of America
WFP World Food Programme
WMO World Meteorological Organization
WRMA Weather Risk Management Association
LLN Law of Large Numbers
Chapter 1 INTRODUCTION TO
WEATHER DERIVATIVES
Relieve us of the humiliations of the weather - Lord Casey speaking at Cloud
physics congress. 14 June 1961 (Cohen, 1977)

1
1.1 Introduction and background to the study
Energy companies, fashion houses, breweries, ice-cream manufacturers,
construction companies and manufacturers may have different business models
and production processes; however they all have one thing in common:their
financial wellbeing is largely influenced by the weather. Business owners not only
need to compete against other competitors, supplier challenges and demand
issues for their products, more and more businesses are faced with the challenge
of managing the weather in addition to all the other production issues. The
dynamisim of weather observations and their ultimate effects on production
introduce an element of risk to the production process. Derivatives may be the tool
to manage this risk.
According to Mc Donald(2006):“A derivative instrument takes its value from the
value of something else called the ‘underlying’’. The basic premise of a derivative
instrument is to hedge against an adverse financial effect and/or to take advantage
of a fortuitous situation/event that may result in an increase in the shareholder
wealth of a company. An alternative way to see them is that derivative instruments
provide an opportunity to hedge against ‘unknown unknowns’, and these ‘unknown
unknowns’ may be considered the most dangerous of all unknowns, given that in
business the downside risk is ever present. One of the features of the risk
landscape that is becoming more prominent due to its perceived unpredictability is
the weather. The risk posed by weather to business activities is significant.
According to Brockett (2005), during2005, company revenues affected by climate
in the United States of America represented $1000 billion, $1250 billion in Europe
and $700 billion in Japan. This paper explores a product that may be used to
hedge this weather risk, namely weather derivatives.

2
Weather derivatives differ from traditional derivatives in two major aspects.Firstly
there is no underlying traded instrument on which weather derivatives are based,
whereas equity, bonds or foreign exchange derivatives, for example, have their
counterparts in their respective spot markets. Secondly, weather is not traded as
an underlying instrument in a spot market. This means that unlike other derivatives,
weather derivatives are not used to hedge the price of the underlying instrument,
as the weather itself cannot be priced. They are used rather, as a tool to hedge
against other risks affected by weather conditions (Zeng & Perry, 2002).This
studyinvestigates the factors that affect maize production with a particular focus on
temperature.Temperature isone ofthe many variablesaffecting maize production
that weather derivativescan be based on. Other variables include humidity,
sunshine, rain or even snowfall, however these variables fall outside the scope of
this study.
Having a full grasp of the intricacies of a weather derivative and the South African
maize market, this study evaluates the impact of implementing a weather derivative
hedging strategy over 10 years (2000 – 2010), on two theoretical farmers.
Revenue from the option strategy is calculated and the findings presented in
Chapter 5.
 Before any calculation is performed and before any data are presented it is
important to have a firm grasp of the factors identified
intheresearchliterature. The following factors were identified and discussed
below:The weather and risk
 Derivatives
 Weather derivatives in general
 Temperature derivatives
 Maize production in South Africa

3
1.2Factors identified from the literature
1.2.1 The weather and risk
The report by World Meteorological Organization (WMO) (1996) revealed that the
overall global warming is expected to add in one way or another to the difficulties of
food production and scarcity. The report stated that reduced availability of water
resources would pose one of the greatest problems to agriculture and food
production, especially in the developing countries. Also Katz and Brown (1992)
reported that climate variability is likely to increase under global warming both in
absolute and relative terms.
It is estimated that one-seventh of the U.S. economy is weather sensitive (Challis,
1999) and (Hanley, 1999). According to Brockett (2005), during2005, company
revenues affected by climate in the United States of America represented $1000
billion, $1250 billion in Europe and $700 billion in Japan. The authors have all
explored the risk that the weather poses to business activities. It would stand to
infer that the South African businesses are also not far behind in terms of their
exposure/reliance on the weather.
An investigation into first world countries research has indicated about 20 per cent
of the industrial produce in Europe was concerned with climate risk, and 35 per
cent of the industrial produce in the US (Triana, 2006).
From a global perspective the US Department of Commmerce suggests that, more
than 80 per cent of the business activity in the world is weather dependent,
(Barrieu &El Karoui, 2002). It is safe to say that weather risk is a very important
factor in our local and global economy. Research has also found that the various
types of weather have differing effects on industry. This study presents this below.

4
1.2.2 Weather types and its effects on industry
Weather affects various industries in different ways. Some of the more common
industries affected are: sport, tourism and agriculture. For instance a golf club may
want to reduce revenue and green fee fluctuations that are caused by rainy days.
The management of a skiresort may want to hedge against the consequences of
low snowfall and a wine producer might find it fortuitous to protect income against
frost damage in the grapes' flowering season. A snowstorm, for example, may
disrupt air travel and drive up heating costs while in another scenario boosting
subsequent attendance at ski resorts.
Table 1 highlights the various sectors and weather observation that may be
hedged using weather derivatives.

5
Table 1: Industry use of weather anomalies
Sector Main climate related risks to hedge
Energy industry Temperature
Agriculture industry
Temperature
Precipitation
Frost
Agribusiness
Sun
Temperature,
Distribution (clothing,
tyres, furniture)
General meteorological conditions
Tourism industry
Snow
Sunshine hours,
Temperature
Health industry
Very cold winters,
very hot summers
Transportation industry
Wind
Rain
Snow
Frost
Icing
Building and construction
industry
Wind
Rain
Snow
Frost
Icing
Source: Considine, (1999)
Weather anomalies affect industries in various ways. The table above is presented
to highlight the possible weather observations that could affect business in one

6
way or another. This list is not exhaustive but does provide a wide enough reach to
introduce the use of derivatives as a possible risk mitigation tool.
1.2.3 Derivatives
A derivative instrument takes its value from the value of something else called the
‘underlying’ (Mc Donald, 2006). The basic premise of a derivative instrument is to
hedge against an adverse financial effect and/or to take advantage of a fortuitous
situation / event that may result in an increase in the shareholder wealth of a
company.The statistics provided by the Bank for International Settlements (BIS,
2006) suggest that the combined turnover in the world's standard derivatives
exchanges totalled $344 trillion during Quarter 4 of 2005. Derivatives play a major
role in the hedging of various financial risk factors. Below this study will introduce
the basic premise of a weather derivative.
1.2.4 Basic temperature derivative instruments
The basic functioning of a derivative is described below as an introduction to this
complex financial contract. A basic temperature derivative consists of an index,
called either a heating degree day (HDD) or cooling degree day (CDD) measured
over a calendar month or a season.
There are five basic elements in a contract according to Cao and Wei (2004):
1. The underlying variable: HDD or CDD;
2. the accumulation period;
3. the strike temperature;
4. a specific weather station reporting daily temperatures for a particular city;

7
5. the tick size: the dollar amount attached to each 1 degree movement from
the strike temperature.
A hot year temperature derivative is presented as an example:
 Hot Year derivative
 Under this Derivative, the client will receive a fixed degree amount for
each degree the annual average temperature is above the strike
temperature up to a maximum amount during the contract period. A
‘Hot Year’, is defined as a year in which the yearly average
temperature measured by the independent national meteorological
station exceeds a national minimum. In this context the South African
Weather Service is an independent national meteorological station.
1.2.5 South African maize industry
South Africa is a country with a large proportion of arable land that is used for the
growing of crops such as maize and corn, these crops are used for human
consumption and maize is considered to be the staple food of the larger
percentage of South Africans living in rural areas(Mc Cann, 2001).
According to Mc Cann (2001), maize comprises over 60 per cent of all land planted
for cereals and represents 40 per cent of total calories consumed in South Africa.
The maize plant reacts to a lack of water, sunlight and nitrogen and can rot easily.
Research suggests that even a few days of drought at the tasseling can ruin a crop
and maize monocultures are extremely vulnerable to environmental shocks, (Mc
Cann, 2001).
The importance of the maize industry in South Africa is without question and any
changes to the production factors that make up the maize crop will be felt not only
in big business but also by the lower income groups of South Africa.

8
In South Africa the maize industry is largely split between large production facilities
and subsistence farmers. Both are equally as vulnerable to the weather conditions.
It is only the scale at which the exposure differs. Dischel and Barrieu 2002, suggest
that the variables involved in successful agricultural production are dynamic and
need to be monitored constantly. Thus the importance of the crop and its fragility to
changing environmental conditions creates the context in which the risk associated
with maize production in South Africa should be assessed and hopefully mitigated
by temperature derivatives.
1.3 The purpose of this study
The purpose of this research study is to investigate the possibility of hedging maize
production against weather exposure riskby providing maize farmers with a
weather derivativeoption strategy that could be used to hedge their risk.
This method of hedging weather risk is important because the Intergovernmental
Panel on Climate Change (IPCC, 2007) regional assessments of Climate Change
Impacts for Africa imply that, declining grain yields are likely in the future, (IPCC,
2007). Another study by (BokoNiang, Nyong Vogel, Githeko, Medany, Osman-
Elasha, Tabo and Yanda, 2007) suggests that:
”agricultural production and food security in sub-Saharan Africa will be negatively
affected particularly due to increased drought intensity and frequency linked to
greater annual rainfall variability.”
A clear indication that the maize industry in South Africa is just as affected by the
weather as those first world industries, highlighted by Brockett (2005), is cited
above.

9
1.4 Research problem
Farmers and production yields are vulnerable to losses due to the impact of the
weather. Research has proven that there are limited ways of dealing with this issue
this paper may prove that the use of weather derivatives may be an option.
In light of this, the research problem is thus: Can temperature based weather
derivatives be used to hedge the production risk of maize faced by farmers in the
Free State Province of South Africa?
1.5 Research methodology
This study uses a dual paradigm approach, with the use of qualitative and
quantitative data to predict the outcome/revenue from the use, of temperature
based weather derivatives, by using experiential descriptive design.
The entire study is an empirically positivisitic study and will collect quantitative data
from secondary sources to be used in the experiment. There is a high level of
control over the test variables as the derivatives are imposed on actual data and
the results presented are calculated mathematically.
The temperature and maize production data will be used to experiment on the
revenue figures of two theoretical farmers located in the Free State province. For
the purposes of this study, we assume they are exposed equally to temperature
fluctuations and by default exposed equally to maize production risk. The first
farmer will have no access to temperature derivatives for the duration of the study
and the other test farmer will have his entire production hedged with the use of two
temperature derivatives, otherwise known as an option strategy.

10
This study then continues by analysing the results of the income received by the
two farmers during the study period and draws a conclusion on the revenue
outcome from the use of temperature derivatives. The level of the revenue from the
option strategy will determine the success of the strategy.
1.6 Collecting and analysing the information
Information was collected from the South African Weather Service and also from
GrainSA. The South African Weather Service has an academic arm which
facilitiates the requests from students for time series data on the weather. This
channel was used to obatin the temperature data.
GrainSA publishes data on a monthly basis on their website. The information was
downloaded from this website and cleaned. It was then aggregated to match the
time series of the data that was obtained by the South African Weather Service.
Once both data sets were obtained the time series of the data was matched for the
period 2000-2010. This exercise involved obtaining an average monthly maize
production and an average monthly temperature observed by the South African
Weather Service for the regions in which the maize was produced. The data is
presented in tabular form in chapter three of this study.
1.7 Limitations of this study
The limitations of this study may be found in the collection of the time series data.
This study has used 11 years of temperature data in order to calculate revenue
figures forthe option strategy. As with all time series analysis, this study may have
more relevance if a wider time series of temperature and maize data is used.

11
This study has collected data from three weather stations in the Free State
province. This would mean that the outcomes of the temperature derivative
strategy is localised to those three weather stations. The results may/may not
translate to other regions within South Africa. This may be due to the changing
exposure to the weather that are experienced in other regions within South Africa.
This study has also isolated the weather variable, namely, temperature.This study
may have more relevance if a combination of weather variables that affect maize
production were hedged, for example, a combination of:
 Rain fall,
 Sunshine hours,
 Temperature,
 Hail,
 Frost
 Snowfall and
 Soil composition
A wider selection of variables may also produce a more relevant result on the
success or failure of the use of weather derivatives as a whole.
Finally, the study has also focused on only a single maize producer and a single
region. This further adds to the localisation of the results in that adaptations in the
genetics of the maize strains may be affected differently by temperature. A wider
geographical dispersement of data points may result in an even greater
assessment of the potential revenue from weather derivatives.

12
1.8 Chapter outline
Chapter one is the introduction that sets the tone for the paper referring to the
changing weather patterns, risk, maize production and the introduction to weather
derivatives. The ground work is laid for the literature review in Chapter two which
unpacks in great detail the information used to conclude in Chapter five.
Chapter two is the literature review that presents the details behind various aspects
of weather derivatives like comparisons between weather derivatives and ordinary
derivative contracts and weather derivatives and traditional insurance contracts.
Further details in the weather derivative sections are the history, geography,
companies involved in weather derivative products, the recent growth in the market
for weather derivatives and the function of brokers. This chapter also suggests four
traditional approaches for pricing of weather derivatives, empirical examples of
weather derivatives highlighting Californian wine production and weather
derivatives in Malawi amongst others. Finally chapter two also analyses the maize
production in terms of the changing climate, localised South African production of
maize, risks associated with maize production and a detailed analysis into the
factors affecting maize production.
Chapter three presents the research methodology with references to the research:
 Design;
 population; and
 instruments.
Empirical analysis is performed in chapter four, the time series and temperature
data are shown in tabular form along with the summary of the option strategy and
its execution for each of the observation years. In addition to showing the data that
underpin the study the results of the empirical analysis from a revenue perspective

13
are presented. This shows the ultimate success or failure of the temperature option
strategy.
Chapter five concludes the study with a summary of the research findings from the
empirical analysis, limitations of this study are also expanded and suggestions for
further research are shown in order to allow for a more detailed analysis of the
weather and maize industry in South Africa. The study concludes with tangible
results from the hedging strategy employed by the maize farmers.

14
Chapter 2
2 Literature review
It is estimated that one-seventh of the U.S economy is weather sensitive (Challis,
1999) and (Hanley, 1999). With one seventh of the world’s economy being affected
by weather it would lead us to assume that that there is a strong link between
economic prosperity in South Africa and the weather. A method by which this link
can be understood and manipulated is through the use of weather derivatives. This
study will address this link. No study on weather derivatives would be complete
without an exploration into the origins of the weather derivative market. Once the
origins of the weather derivative market are discussed the components of the
weather derivative are presented allowing the study to investigate the facts around
those components and their relevance to the maize production industry and thus
this study.
2.1 Introduction to weather derivatives
A brief history into weather derivatives is suggested by Alaton et al (2003), 'The
first transaction in the weather derivatives market took place in the United States of
America in 1997'. Weather derivatives are simply designed as a “bet” on weather
conditions with the only requirement being an observable objective variable agreed
upon by both parties, (Richards, Manfredo and Sanders, 2004). A key word taken
from the above citation is ‘bet’. This bet involves having a vested interest and a
view on something that will happen in the future. We will unpack this vested
interest after a thorough analysis of the mechanics of weather derivatives and an
analysis of the South African maize industry.

15
2.1.1 The mechanics of weather derivatives
Lee & Oren (2008) describe the components of a standard weather derivative
contract as consisting of the contract period, measurement station, weather
variable, index, structure, option premium and a payoff.
2.1.1.1 The contract period
All contracts have a defined start date and end date that constrain the period over
which the underlying index is calculated. The weather derivative terms in US
market are November 1 through March 31 for winter season contracts and May 1
through September 30 for summer contracts. Some contracts also specify variable
index calculation procedures within the overall term - such as exclusion of
weekends or double weighting on specific days this is done to account for
individual business exposures.
2.1.1.2 A measurement station
All weather contracts are based on the actual observations of weather at one or
more specific weather stations. Transactions are based on a single station,
although some contracts are based on a weighted combination of readings from
multiple stations and others on the calculated difference in observations at two
stations.
2.1.1.3 A weather variable
A weather variable that affects maize production is the actual weather
observation that will be measured by the weather station. These include:
 Temperature
 Rain fall
 Snow Fall
 Frost

16
 Sunlight
 Snow
 Wind direction
 Wind speed
2.1.1.4 An index
The underlying index of a weather derivative defines the measure of weather,
which governs when and how payouts on the contract will occur. Some common
indexes in the market are Heating Degree Days (HDDs) and Cooling Degree Days
(CDDs) within the temperature derivative context. These indices measure the
cumulative variation of average daily temperature from 18 oC over a contract
period and are known to be standard indexes in the energy industry that correlate
with maize production. A wide range of other indexes are also used to structure
transactions that provide the most appropriate hedging mechanisms for weather
derivative users in various industries. Average temperature is another index for
agricultural applications, and some transactions are based on so-called event
indexes. Event indexes count the number of times that temperature exceeds or
falls below a defined threshold over the contract period. Similar indexes are also
used for other variables; for example the numbers of days on which average
snowfall exceeds a defined level or cumulative rainfall.
2.1.1.5 A structure
Weather derivatives are based on traditional derivatives such as calls, puts, collars,
swaps, strangles and straddles. For the purposes of this study it is not important to
have an understanding of these derivative structures, however the key
characteristics of these are explained to help the reader with a general
understanding:

17
 The strike observation (the value of the underlying index at which the
contract starts to pay out);
 The tick size (the payout amount per unit in the index above/below the
strike);
 The limit (the maximum financial payout of the derivative contract).
2.1.1.6 An option premium
The buyer of a weather derivative option pays a premium to the seller that is could
be between 10 per cent and 20 per cent of the value of the contract, however this
can vary significantly depending on the risk profile of the contract. All of the above
components are similar to a basic derivative instrument. However, the underlying
asset is different.
2.1.1.7 Payoff structures from weather derivatives
With the above components explained we are now able to produce the payoff
diagrams for a generic set of weather derivatives. These payoff diagrams indicate
the profit potential and also the cost of a traditional weather derivative contract.
Further to the cost and profit potential is the break-even point for the derivative.
Traditional option derivatives have the following payoff structures:

18
FIGURE 1: Payoff structure from a typical put option
Source: Garcia (2012)
Dutton (2002b) gives an illustrative example (see figure 1) on the payoff structure
of a temperature put option bought by a company named Jefferson Gas. This
option was used for protection against a warm winter. The option was bought for
the price or premium of $100,000 and had the following attributes:
 Variable index – HDD
 Cap - $500,000
 Strike – 1,330 HDD
 Rate of payment – $1,500/HDD

19
FIGURE 2 Payout diagram of a long put HDD option
Source: Dutton (2002b)
Consequently, the company would be paid up to the amount of $500,000 (the net
revenue would reach $400,000) in the case of warm winter. Alternatively, if the
cumulative number of HDD index was higher than 1,330, the company would not
be paid from the option and thus its net loss would be - $100,000 due to the
premium payment.

20
2.2 History of weather derivatives
According to Smith (2000), the concept of weather derivatives was first introduced
in the USA in 1997: According to Smith (2000), the first weather-based derivative
contracts were offered in September 1997 between Enron and Koch (now Entergy-
Koch).The purpose of the weather derivatives used within this context was to offset
the output risk of the energy company with the demand for energy from the
consumers. Given its start in 1997 the weather derivative market is thus relatively
new when compared to other commodities, bond and interest rate markets.
A time line for the evolution of the weather derivative market from 2000 to 2006 is
presented in FIGURE 3. The OTC weather derivative market started when the
Chicago Mercantile Exchange (CME) launched the first weather derivatives in the
summer of 1999. By the 3rd of October 2003 five monthly and seasonal European
derivatives opened to the market signalling the expansion from the American to the
European market and following that, two Japanese cities entered the market on the
20 July 2005, CME (2005).

21
FIGURE 1: Chicago Mercantile Exchange (CME) weather market: evolution
timeline (1999–2005).
Source: http://www.levow.com/wp-
content/uploads/SG/Commodities/CMEWeatherproducts.pdf (Accessed on
3rd March 2013)
2.3 Weather derivatives vs. traditional derivatives
Weather derivatives differ from traditional derivatives in two major aspects, firstly:
There is no underlying traded instrument on which weather derivatives are based,

22
whereas equity, bonds or foreign exchange derivatives, for example, have their
counterparts in the spot markets. Secondly, weather is not traded as an underlying
instrument in a spot market.
This means that unlike other derivatives, weather derivatives are not used to hedge
the price of the underlying instrument, as the weather itself cannot be priced.
Weather derivatives are used, rather as a tool to hedge against other risks affected
by weather conditions according to Zeng & Perry, (2002). This other risk would be
the output risk experienced by the farmer when weather observations are falling
outside of what is expected.
2.4 Weather derivatives vs. weather insurance
It is important to define the difference between weather insurance and weather
derivatives. A fundamental point is that weather derivatives do not replace
insurance contracts. The reason for this is due to a number of significant
differences, which will be discussed below.
2.4.1 Nature of the risk
Insurance contracts cover high risk or extreme weather events and low probability
events such as typhoons, hurricanes and earthquakes, whereas weather
derivatives cover low risk, high probability scenarios. These high, insured risks, low
probability events do not work well with the uncertainties in normal weather,
(Alaton et al, 2003).
2.4.2 Payout
The payout from weather derivatives is designed to be in proportion to the
magnitude of the phenomena and therefore can be constructed to have payouts in

23
any weather condition, whereas weather insurance pays a once-off lump sum that
may or may not be proportional to the loss realised by the farmer. This is an
instance where the flexibility of weather derivatives may aid the farmer and his
production processes.
Insurance companies, after assessing damage will pay out only when the holder of
the insurance contract has actually suffered the damage or loss due to weather the
weather conditions and if he is not able to show this, the insurance company will
not pay him for any loss or damage. Payouts for weather derivatives are only
based on a predetermined weather variable index value that is based only on the
actual outcome of the weather, regardless of how it affects the holder of the
derivative financially.
2.4.3 Performance monitoring
It is possible to monitor the performance of the hedge during the life of the contract.
Additional shorter-term forecasting towards the end of the contract might mean that
the buyer wishes to release him/herself from the derivative because it is a traded
security, there will always be a price at which market participants can sell or buy
back the contract (Geyser, 2004).
2.4.4 Cost
Traditional weather insurance can become expensive, as traditionally insurance
contracts would require a loss to have occurred before paying out. Due to the
indexing nature of weather derivatives they require no loss to have actually
occurred in order to provide a payout if the variable measured moves adversely
during the contract period.

24
2.4.5 Counterparties
Weather derivatives require two counterparties with the effect being the offsetting
of risks. In the insurance market the risk is not offset as the contract is between the
insured and the insurer.
2.4.6 Speculation
A speculator can enter into a weather derivative contract without actually having
any weather exposure. This would open the door for speculators to benefit from the
movements in weather.
2.5 The users of the weather derivative market
'In 2000 the majority of weather derivative deals were located in the United States
of America (USA), the United Kingdom (UK) and Japan involve energy companies.
Between 70 per cent and 80 per cent of all weather derivative deals have an
energy company on one side of the contract' (Gautam & Foster, 2000); this may be
due to their high dependence on the predictability of the weather for forecasting
consumer energy needs.
A study conducted by Ladbury, (2000a) proposes several industries that may
benefit from weather derivatives:
2.5.1 Theme parks and sporting events
The busiest periods for theme parks and sporting events are the summer months.
Unfortunately, these are the same months, during which most of South Africa
receives its highest rain fall. Attendance figures are intuitively closely correlated
with weather conditions and drizzle can cause people to avoid outdoor activities.

25
2.5.2 Construction
In this industry, heavy financial penalties can be imposed for work that runs past its
completion date. The delay caused by the weather can also cause projects to run
over budget.
2.5.3 Clothing
Although fashion determines the clothing lines that retailers stock in their stores,
weather conditions strongly influence what customers buy. During a mild winter,
jacket and jersey manufacturers' products may experience slow sales.
2.5.4 Agriculture
Weather is a major source of risk in agriculture (the temperature variable is the
focus of this study). Temperature, rainfall and wind can all affect the quality and
quantity of a crop. The relationship between weather and crop yield is complex. For
example, drought badly affects water-dependent crops. The timing of rainfall is also
a crucial factor. Temperature also plays a vital role in the ripening process for
many crops produced in South Africa.
2.5.5 Energy Companies
Based on empirical data from Randals (2010) the study finds that the energy
companies use weather derivatives more pervasively. FIGURE 2 below presents
an industry analysis of weather derivative users and the growth for this asset class
over time.

26
FIGURE 2: Growth in the users of the weather derivative market and their
industry origin (X axis in per cent and Y axis in industry, source not labeled)
Source: Randals (2010)
A study cited in Randals (2010) suggests that temperature is a common
underlying weather anomaly traded in the weather derivatives market. From this
study we see that 75 per cent of the transactions are based upon temperatures on
the CME and 95 per cent on the OTC market, and 10 per cent and 3 per cent
respectively on rainfall products. Temperature is related to the notion of ‘Degree
Day’ which is expressed as the difference between a reference level / strike
temperature (65°F or 18°C) and the average observed temperature (T). The
average is computed between the maximum and minimum recorded temperature
over a particular day (Randall S 2010). Table 2 presents weather phenomena and
their related weather derivative products that are offered in the financial market

27
currently. Table 2 shows the various indexing techniques and measurement criteria
that can be used to measure weather movements and thus transferring them into
weather indexed derivatives.
Table 2: Type of risk observed Industry classification and weather derivative
market share
Source: Climetrix, Risk Management Solutions Inc. (2001)
2.6 Companies that offer weather derivative services
Examples of companies who offer weather derivatives are: Climetrix and Galileo
Weather.

28
Climetrix is a dedicated resource for weather risk professionals. Climetrix provides
a comprehensive range of weather data and state-of-the-art modelling capabilities
for analysing and tracking individual weather risk contracts and portfolios.
Climetrix.com is a dedicated resource for weather risk professionals and is also the
point of access for Climetrix, MDA Information Systems, Inc.'s market-leading,
Internet-based weather derivatives trading and risk management system. Climetrix
provides a comprehensive range of weather data and state-of-the-art modeling
capabilities for analyzing and tracking individual weather risk contracts and
portfolios.’ (MDA Information Systems, 2010).
Galileo Weather Risk Management
(http://www.propertycasualty360.com/2012/11/20/endurance-buys-galileo-weather-
risk-management-for) was formed in December 2005 and creates tailored financial
weather-related risk management products to energy companies, utilities, and
construction companies wanting to mitigate their financial exposure to variations in
weather conditions and commodity prices. The products that Galileo’s offers can
be indexed to weather variables including temperature, rainfall, snowfall, humidity,
sunshine hours and wind speed as well as commodities such as natural gas and
power, and can be delivered on a global basis as either derivatives or reinsurance.
Galileo has offices in New York, London, and Bermuda.
2.7 The growth in the weather derivative market
Below is a graphical representation of the growth in the use of weather derivatives
from 2002 – 2008. The FIGURE 5 shows a dramatic increase in the open interest
and total volume of weather derivatives traded on the CME. It is important to note
that these are all the exchange traded contracts, this does not include the ‘over the
counter’ or ‘tailored' contracts across the weather market hence the actual market
size may be understated in this example.

29
FIGURE 3: The growth in the weather derivatives market from January 2002 –
October 2008.
Source: Chicago Merchantile Exchange Analysis (2008)
2.8 Value of the weather derivative market
PWC commissioned a study into the notional value of the weather derivative
contracts traded on the CME and the results can be seen in FIGURE 6 below. It
indicates the entire market of OTC and exchange traded contracts. Both types of
weather derivative products have shown aggressive growth since 2002.

30
FIGURE 4: Notional Values for Weather Derivative contracts
Source: Price Waterhouse Coopers (2006).
In 2010/2011 a total of 998 OTC contracts were recorded through the Chicago
Mercantile Exchange (CME) which is almost triple the number reported in the
2009/10 year survey by the Weather Risk Management Association in 2010
(WRMA, 2010). The number of contracts traded on the CME increased by 18 per
cent to 466,000.
‘The recent growth in weather derivative arrangements from years 2009 to 2011, is
also being fuelled by hedge funds which are beginning to include weather contracts

31
in their investment strategies’ (Ceniceros, 2006). In the 12 months prior to 31
March 2011, the market for over the counter (OTC) weather derivatives grew 29
per cent to $2.4 billion, up from $1.9 billion in the previous year (Weather Risk
Management Association, 2011). Given the research suggested by the WRMA
there is evidence that will speak to the fact that market participants who believe
that weather risk needs to be managed. The increased activity in the weather
derivative market highlights the awareness of the impact that weather has on
financial activities and the subsequent use of weather derivatives to manage that
risk.
At this point it is necessary to isolate one of the types of the many weather
derivatives products. This study will focus on Temperature Derivatives. What
follows is a discussion of the nature and functioning of this special type of
derivative.
2.9 Introduction to temperature derivatives
The basic temperature derivative contracts are offered to farmers who would see
an adverse effect on yield if the temperature was either too hot or too cold during
their production process. This study will present both a hot year and cold year
certificate. This will investigate if the derivatives, when used together, can be used
in maize production hedging.
This study uses temperature derivatives, as research suggested that of all weather
derivatives, 80 per cent are temperature related. According to the Weather Risk
Management Association (Cao and Wei 2004) a total of 3,937 temperature
derivative transactions were completed in the weather risk industry for the period
from April 1, 2001 to March 31, 2002. This represents a 43 per cent increase from
the previous year, which recorded a total of 2,759 transactions, (Caio and Wei
2004). Temperature weather derivatives are a form of financial security that can

32
provide firms with the opportunity to hedge against adverse temperatures or take
advantage of favourable temperature conditions in the face of their daily
operational activities. The point is that temperature derivatives offer market
participants the opportunity to create an avenue for revenue when it comes to
changes in the temperature. Dischel & Barrieu (2002) suggest that: 'Buying a
weather derivative involves embarking on a financial “balancing act” where some of
the higher revenues in good times are bargained away in return for compensation
in bad (low income) times.'
2.10 The structure of a typical temperature derivative
Temperature contracts are written on the accumulation of heating degree days
(HDD) or cooling degree days (CDD) over a calendar month or a season. There
are four basic elements in a temperature contract according to Cao and Wei
(2004):
1. The underlying variable: HDD or CDD;
2. the accumulation period;
3. a specific weather station reporting daily temperatures for a particular city;
4. the tick size: the dollar amount attached to each 1 degree movement from
the strike temperature.
2.11The test temperature derivative
This research paper will rely on those variables selected by Cao and Wei (2004) to
ensure comparability between actual derivative instruments offered on the market
today and the test derivative instruments that have been isolated for this research
paper. Those fictitious variables according to Cao and Wei (2004) are contained in
Table 3:

33
Table 3: Terms of test temperature derivative
Hot Year
Derivative
Cold Year
Derivative
Location Free State
Province
Free State
Province
Accumulation Period Annual Annual
Tick Size [Payout per
Degree Celsius]
R35,000 R35,000
Strike Temperature (in
Celsius)
24.1 23.1
Maximum payout R350,000 R350,000
Option Premium R200,000 R200,000
Source: Adapted from Cao and Wei (2004) / Authors deductions
2.11.1 Hot Year derivative
Under this derivative, the farmer will receive a fixed amount of R35,000 for each
degree the annual average temperature is above 24.1 °C up to a maximum of
R350,000 during the period from 1/01/2000 until 31/12/2010. A Hot Year is defined
as a year in which the yearly average temperature measured by the independent
national meteorological station, exceeds 24.1 °C.

34
2.11.2 A cold year derivative
Under this derivative, the farmer will receive a fixed amount of R35,000 for each
degree the annual average temperature is below 23.1 °C up to a maximum of
R350,000 during the period from 1/01/2000 until 31/12/2010. A Cold Year is
defined as a year in which the yearly average temperature measured by the
independent national meteorological station remains below 23.1 °C.
2.12 Physical marketplace for weather derivatives
There are currently 39 cities in the world that offer weather derivatives through the
Chicago Mercantile Exchange (2008):
 18 in the United States
 9 in Europe
 6 in Canada
 2 in Japan
 3 in Australia.
The majority of the contracts traded on the CME are heating degree days and
cooling degree days. There are contracts traded that give market participants the
opportunity to hedge hurricane, frost and snow fall. These can be found in the
cities Boston and New York and Philadelphia. We observe that the physical
locations of the markets are mostly situated in first world countries. This speaks to
a lot of factors that make this market possible in those geographical locations. Due
to the localised nature of this study these factors may play a role in the viability of
these products in South Africa.

35
2.13 Pricing weather derivatives
The emergence of insightful financial solutions in the weather risk management
space has led to the questioning of the validity of existing pricing methods that are
based on hedging arguments, like the Black and Scholes model, Black & Scholes
(1973).
Weather derivatives, like insurance or catastrophic bonds are traded in markets
where the pricing methodology of weather derivatives is not standardised.
Therefore, they have challenged the traditional no-arbitrage and actuarial pricing
methods used generally in the financial world for derivative products with a more
tangible underlying asset. The markets for weather derivatives are incomplete
simply because there has yet to be found a reliable method for pricing weather
derivative contracts. It is important to note that the underlying asset traded by
weather derivatives is not open to manipulation i.e. weather observations, and
hence this theoretical market is, according to the CAPM model, ‘perfect’.
A study that has looked at pricing of derivatives in incomplete markets are by
Heath, Platen & Schweizer (2001). This has led to pricing concepts based on
expected value calculations as concluded by Heath, Platen & Schweizer (2001).
Several attempts have been made to price weather derivatives using the traditional
financial or no-arbitrage pricing approach.
Given the varied pricing approaches it appears that a reliable methodology for the
pricing of weather derivatives is yet to be found. The challenge is that the
underlying asset on which the weather derivative is based is not tradable. A study
conducted by Cao & Wei (2001) concluded that any contingent claim can be
valued by discounting its payoff at the risk-free rate. In South Africa the risk free
rate is quoted from returns on government bonds. This implies that the return from
a weather derivative should exceed the return seen in the risk free markets.

36
Cao and Wei (2000) use an equilibrium approach to draw the conclusion that the
appropriate price for weather options is the expected pay-off. Also, the Capital
Asset Pricing Model (CAPM) does not apply particularly well to the real weather
market (with reference to the perfect market theory). A reason for this is that
weather derivatives are not considered as an investment class by the majority of
investors, and thus there is less demand for weather derivatives as investments
than the low correlation and CAPM might suggest therefore, the assumptions that
are observed in the CAPM (and other equilibrium models, such as that of Cao and
Wei, 2000) do not apply in practice. So what is the appropriate way to price
weather derivatives? We examine the various approaches to pricing weather
derivatives in the following sections.
2.13.1 The benchmark approach
Further exploration from Platen and West (2004) has also suggested a benchmark
approach that generalises different pricing methodologies and uses the concept of
Growth Optimal Portfolio (GOP). If a benchmarked price process is a martingale or
is part of a martingale series1, then this price process is called “fair”. The
benchmark approach also covers models where an equivalent risk neutral
martingale measure does not exist. Platen and West (2004) has shown a
generalized actuarial price is obtained as the fair derivative price when the payoff is
independent of the GOP. This relates to the conclusions found by Cao & Wei
(2001). The GOP can be interpreted to be equivalent to the absence of weather
risk premium, which means that the market price for weather risk is zero. This is
1 In probability theory, a martingale is a stochastic process (i.e., a sequence of random
variables) such that the conditional expected value of an observation at some time t, given
all the observations up to some earlier time s, is equal to the observation at that earlier
time. (Balsara, 1992)

37
consistent with other literature in that the common observation is that weather risk
is geographically and temporally diversifiable, temporarily as a result of the
transient nature of the underlying asset.
2.13.2 Burn Analysis for pricing weather derivatives
The insurance industry uses an approach called burn analysis that is useful in the
weather markets.
There are four steps in the Burn analysis process:
 First, analysts must collect the historical weather data, convert them to
degree days (heating degree days [HDD] or cooling degree days [CDD]);
 For every year in the past, those analysts must determine what the weather
option would have paid out;
 Find the average of these payout amounts;
 Discount back to the settlement date.
The discounted value calculated from the Burn analysis is the cost of the derivative
contract; this cost could then be translated into a weather product and sold over
the counter.
When looking at traditional derivative valuation methods researchers could ask,
what is the strike of a zero-cost swap? Using Burn-rate analysis, the answer to this
question depends on the maximal payout. This is counterintuitive, since we would
expect that the strike of a zero-cost swap to be the same regardless of the maximal
payout amount. For example, consider a swap in which we are long (purchased)
Heating Degree Days (HDD’s) in South Africa. The contact period is November 1,
1999, to March 31, 2000. Assume that there is an R10,000.00 payment per HDD
and that the maximal payout is R10 million (the maximum payout is known as the
cap). Taking the last 10 years of data without trending or adjusting for leap years,
we suppose that the average HDD level is R5,018.75 (Based on time value of

38
money calculations outside the scope of this research paper.). Therefore a swap
with a strike of R5,018.75 would be a zero-cost swap. On the other hand, assume
the maximum amount that can be paid out under the swap is only R1.7 million. In
this case, the burn analysis gives a totally different result. A swap with a strike of
R5,018.75 would actually show an average payout of negative R20,200.00
Collecting historical data can be a challenge. While there are Internet sites with
downloadable historical weather information for the United States, obtaining
historical weather data for the Africa is quite costly.
2.13.3 Temperature-based valuation models
Temperature based valuation models have been shown to have the following steps
Cao and Wei (2000):
1 Collection of the historical weather data.
2 Corrections being made to data to clean it.
3 Create a statistical model of the weather.
4 Simulate possible weather patterns in the future. (i.e. Forecast the
weather)
5 For each weather pattern, calculate the payout of the option.
6 Find the average of these payout amounts.
7 Discount back to the settlement date.
The fundamental difference between this approach and the Burn analysis is that
one is building a model for the weather, not the degree days/temperature. The
simulation done in step 4 could be performed using a Monte Carlo3 algorithm. Such
algorithms generate random numbers. These random numbers are then used to
simulate the behaviour of the phenomena we are trying to model.

39
2.13.4 Monte Carlo Simulation
Monte Carlo is a simulation (Anderson, 1986) tool for considering a number of
plausible combinations. This is one method to statistically construct weather
scenarios. The premise is to simulate a lot of directions of the process and then
estimate the expected value with the arithmetic average. When a simulation of
temperature trajectories for a given period of time is run, the analysis could either
start the simulation at T = 0, and use today’s observed temperature as the initial
value. I could start the simulation at a future date near the first day of the period we
are hedging, T = X. The expected mean temperature for that day is the initial value
of the derivative contract. This expected mean temperature is thus the strike price
for the derivative contract, and deviation from this expected temperature is hedged.
Below is a run 50,000 such simulations, for each one calculating the daily HDD and
as a result the cumulative HDD and payoff at its expiry date. I have found the mean
of these payoffs from the weather derivatives and discounted the average to give a
value at time t = 0. FIGURE 7 shows five of these simulations.

40
FIGURE 5 Example of Monte Carlo temperature simulations
Source: Harris, 2003
2.13.5 Black and Scholes temperature modelling
The Black & Scholes model for option pricing methodology is based on continuous
hedging, Jewson and Zervos (2003). Consider the problem of pricing weather
derivatives based on linear temperature indices. Anticipating the development of a
liquid weather swap market, they addressed the issue of pricing weather derivative
options using weather swaps as hedging instruments. They have provided
formulae for the no-arbitrage prices of weather derivative options. The results
derived include the modifications of the Black & Scholes and Black formulae that
are appropriate for weather derivative pricing.

41
The Black and Scholes model Black & Scholes (1973), does not work for weather
derivatives because:
 The market for weather derivatives is incomplete and not liquid.
 The temperature follows a mean reverting process, which is not supposed in
the Black Scholes model.
 Temperature is not a tradable asset.
2.14 Examples of weather derivatives
Weather derivatives can be used as a tool to transfer risk from emerging
economies like South Africa into the world market. Stoppa & Hess (2003) suggest
that weather derivatives, ‘catalyze alternative risk transfer out of emerging markets
into financial markets.' This risk transfer can go a long way in changing, amongst
others, the risk taking ability of an enterprise using weather derivatives. There
would also be far reaching implications on the lending practises of banks who knew
that the agricultural clients, in the developed world, its financing was able to ensure
that some of the output risk was hedged from the clients side. Below is a brief
empirical description of how weather derivatives have played a in the transfer of
risk within emerging economies.
2.14.1 The International Finance Corporation (IFC)
In the context of emerging economies, the International Finance Corporation
(1987) has attempted to create an avenue for the sustainable financial market
development through the use of weather derivatives. An investment vehicle was
managed by the Aquila Weather Desk and had the capacity to underwrite at least
$70 million of weather risk experienced by Aquila's weather exposed client base.
IFC’s involvement helped to launch weather derivatives into emerging markets
albeit unsuccessfully. The varied reasons for the failure of this attempt at weather

42
futures could be explored to ensure a greater understanding of the mistakes made
by the Aquila Weather Desk.
2.14.2 Electricity forwards, futures and swaps
The simplest form of electricity derivatives are forwards, futures and swaps. Being
traded either on the exchanges or over the counter, these power contracts play the
primary roles in offering future price’s and price certainty to electricity producers.
An electricity producer is able to lock in a fixed price in an unregulated market and
hence will be able to forecast their revenue based on volume output alone. This
takes the guesswork away from the future prices of electricity leaving the electricity
company the ability to focus on the output of supply. Weather derivatives play a
role in the hedging of volumetric risks and could play a role in smoothing the output
demands from a power supplier, this smoothing would be, as explained above,
done by the revenue generated should the weather move against the energy
supplier from a weather derivative.
2.14.3 Californian wine production
In 1998, California’s production of wine grapes fell by almost 30 per cent due to a
cold and rainy spring, followed by a very hot July and August. Higher than average
rainfall during the summer months can be very expensive for winemakers as this
leads to the grapes rotting on the vines and delays in the harvest. In this instance
weather derivatives have been set up to mitigate against the excessive rainfall and
also to safeguard against the cold temperatures, this cost of the rainfall and cold
temperatures could be offset by the income from a weather derivative structure
implemented by the Californian wine producer.
2.14.4 Weather Derivatives in Malawi
Following a boom in cereal production capability, Malawi was offered an
opportunity to hedge its exposure to inclement weather that was disrupting its good

43
fortune by using a financial derivative to offset agricultural risk. The thresholds
underlying the rainfall index are based on a national maize yield assessment model
used by the Malawi Meteorological Office since 1992 for forecasting maize
production in the country (IRIN News, 2008). In June 2008 the World Bank agreed
to create a new weather derivatives product, allowing Malawi to use the financial
markets to offset risks from drought. According to the World Bank, Malawi's
weather derivatives transaction will test the market with a small contract that is
expected to pay out a maximum of about US $3 million if severe weather
conditions prevail. As an additional aide feature, the premium for the initiative was
paid by the United Kingdom's Department for International Development (DFID).
2.14.5 Ice wine production in Ontario, Canada
Cyr and Kusy (2006) explored the potential use of weather derivatives for hedging
the risks inherent in ice-wine production in the Niagara region of Ontario, Canada
due to temperature fluctuations. In particular their study attempted to model a
temperature variable based on daily observations and subsequent prices of options
(puts) that could be employed for hedging ice wine production using a Monte Carlo
method to price the ‘put’ options. Some interesting aspects about ice wine
production, which may reveal why a weather derivative was used, are: it is
generally recognized in the industry that the optimal temperature for harvesting
grapes destined for ice-wine is between -8 and -12 °C.
At temperatures below -12 °C, a greatly reduced quantity of wine occurs according
to Cyr and Kusy (2006). The major risk is that a mild winter with relatively high
temperatures could result in the grapes not being harvested at all for example the
El Nino winter in 1997/1998. In this article, they consider the modeling and
valuation of a ‘put’ option with an underlying being, the temperature. In this case
the payoff of the ‘put’ option would be contingent on a cumulative number of hours
of optimal ice wine production hours. If the amount of hours was not reached the

44
product was structured in such a way that they got cash flow from the product to
offset the loss in the amount of production hours.
2.14.6 The World Food Programme (WFP)
‘Weather derivatives can help manage human catastrophic risk in Ethiopia’, Great
Transactions from Weather Risk Management Association, (WRMA), (2010). The
World Food Programme entered into Rainfall Derivatives to cover adverse effects
of drought with a company called Axa Re (a reinsurer). This was a form of
emergency cover that, with the positive margining effects of the derivatives, the
cash flow from the drought ‘cover’ was able to offset the losses seen as a result of
the drought. This is where the concept of securing food security would play a role.
The cash injections provided by the instrument when there is drought cover in the
form of a derivative would be able to provide the communities with much needed
aid in the form of a margin payment. The development program has a link to this
research report in that some of the information gleaned from this study will also
contribute to the knowledge on South Africa's position in securing a possible
sustainable food security program.
The use of weather derivatives are vast, some of the examples are included in the
text above. The industry in contention for this research report is the maize industry
in South Africa. Which also presents its own unique set of variables that is
discussed below.
2.14.7 Electricity producer: KeySpan Corporation
Electricity firms can use weather derivatives in various scenarios. The literature
has suggested the following example from KeySpan Corporation’s 2006 (Key Span
Corporation 2006) annual report:

45
“n 2006, Keyspan Corp entered into heating-degree day put options to mitigate the
effect of fluctuations from normal weather on KeySpan Corp’s financial position
and cash flows for the 2006/2007 winter season - November 2006 through March
2007. These put options will pay KeySpan Corp $37,500 per heating degree day
when the actual temperature is below 4,159 heating degree days, or approximately
5 per cent warmer than normal, based on the most recent 20-year average for
normal weather. The maximum amount KeySpan will receive on these purchased
put options is $15 million. The net premium cost for these options is $1.7 million
and will be amortized over the heating season. Since weather was warmer than
normal during the fourth quarter of 2006, KeySpan recorded a $9.1 million benefit
to earnings associated with the weather derivative.
2.14.7.1 Terms of the Key Span Corporation’s weather derivative contract
Terms of the Key Span Corporation’s contract were the following:
 The weather variable is temperature in the form of an HDD,
 The accumulation period is November 2006 to March 2007,
 The tick size is $37,500, and the seasonal strike price is 4,159.
With this transaction, the firm obtains protection against low heating demand
scenarios. Specifically, if winter temperatures are 5 per cent milder than normal
(the cumulative HDD values are below 4,159) then the company will receive
$37,500 per HDD below this threshold. The realized HDD value for the year was
3,916, which gave the contract a payoff at maturity date of $9.1 million. This means
that the Key Span Corporation was paid out a total of $9.1 million in cash as a
result of the changed observed in the temperature for the contract period.

46
2.15 Temperature modelling challenges
A study by Nelken (2000) suggested that there are some general issues with the
collection and analysis of historical temperature data, and their application to
historical temperature data analysis: Nelken (2000) suggests that there are some
general issues with the collection and analysis of historical temperature data as
follows:
2.15.1 Data errors
In practice, even when data is available, there may be missing numbers or errors.
The historical data may therefore have to be ‘cleansed’. Our data source has been
cleaned for obvious anomalies and hence this is a serious consideration for other
studies using the South African Weather Service data services. The data used in
this study was aggregated and validated against other sources to ensure an
accurate as possible analysis.
2.15.2 Weather station local
The weather station may have been moved due to construction, or may have been
subject to other external factors (for example it may have originally been in the sun
and now be in the shade, or vice versa). This was not apparent from our data
source as confirmed by the South African Weather Service, who had not moved
any of the weather stations that were used in this study.
2.15.3 Length of time series data
It is not clear how many years of historical data should be considered. Dischel, et
al (2002) states that fifty years is preferred (as per our analysis in the previous
section), but in other cases only ten or twenty years of data are used. 10 years was

47
used for this study to mitigate against the effects of weather anomalies that may
show a 5 - 10 year cyclicality.
2.15.4 Urban island effect
Many cities exhibit the ‘urban island effect’, where, due to heavy industrial activity,
the weather gradually becomes warmer in that area. The global warming effect is
also apparent all over the world. We have graphed the average temperature for the
contract period for the last fifty years (see FIGURE 8), and observed that an
increase of almost 2 °C (from 4. 1 °C to 5. 9 °C) appears to have occurred. It may
therefore be reasonable to linearly shift the earlier temperature data upwards by
1−2 °C. Since this linear shift is very subjective, we have not attempted to alter our
data in such a way, but note that this is a factor that may lead to inaccuracies in
our contract valuation. We see below how the average temperature trend for
Vlissingen, Holland is increasing over time. One of the contributing factors may be
the rapid industrialization in this area.

48
FIGURE 6 Average temperature trend for Vlissingen, Holland
Source: Harris 2003
There are extreme weather patterns that occur in some years, most notably the El
Niño and La Niña, which, although directly affecting the water temperatures in the
eastern and central equatorial Pacific Ocean, also have important consequences
for weather and climate around the globe. Since these occur fairly frequently (El
Niño approximately every two to seven years) and their impact on Europe is very
difficult to quantify, we have not adjusted our historical data for these events.
2.16 Maize production and climate change
Studies have indicated that precipitation and temperature have opposite effects of
output levels and variability of maize output, (Chi-Chung, Mc Carl &
Schimmelpfennig, 2004).
Anderson & Hazell (1987) have also argued that adoption of common high yielding
varieties, uniform planting practises and common timing of field operations have

49
cause yields of many crops to become more influenced by weather patterns,
especially in developing countries like South Africa.
A studies by (IPCC, 2007), has pinpointed Africa to be one of the most exposed
continents to suffer the devastating effects of climate change and climate
variability, with colossal economic impacts because it often lacks adaptive
capacity. The African rain-fed agriculture is viewed by many observers to be the
most vulnerable sector to climate variability. The potential impacts of climate
change on agriculture are highly uncertain.
The maize industry in South Africa is the focus of this research paper. This industry
presents its own unique set of maize production variables that need to be
understood before we offer a temperature derivative strategy. These factors are
discussed below.
2.17 Maize production in South Africa
Maize constitutes about 70 percent of grain and cereal production in South Africa
and covers about 60 percent of the crop area. It is a summer grain and mostly
grown in semiarid regions of the country (Durand, 2006; Benhin 2006). More
importantly for this study maize is highly susceptible to changes in precipitation and
temperature, (Durand, 2006; Benhin 2006). Furthermore, ‘although the maize plant
is fairly hardy and to an extent adaptable to variable unfavourable conditions, a
drier or hotter climate and reduced rain may have detrimental effects on its yield as
stated in BFAP, (2007). In addition, maize is the main staple in Southern Africa,
and maize production in the country constitutes about 50 percent of the output
within the Southern African Development Community (SADC) region (Durand,
2006). As a result, the cost of maize production is one of the key drivers of food
inflation in South Africa (BFAP, 2007) our chosen emerging economy.

50
2.18 Risk associated with maize production
Farmers face three main sources of risk: price risk, event risk and output risk
(Parihar, 2003). According to Parihar (2003), farmers face three main sources of
risk, namely: price risk, event risk and output risk.
2.18.1 Price risk
Price risk can be defined as the probability of an adverse movement in the price of
an agricultural commodity. Traditionally, price risk management was not the
responsibility of South African maize producers, however with the deregulation of
the maize market it became their responsibility. During 1996, futures were
introduced to mitigate the adverse effects of maize price movements. These
contracts help to hedge against price risk, but do not provide protection against any
further identified risks.
2.18.2 Event risk
Event risk can be defined as the probability of the occurrence of an exceptional
event (catastrophe) that would have a negative effect on agricultural yields. Event
risk by definition implies high risk with associated low probability of the actual even
occurring. Examples of event risk would include floods or hail damage. South
African farmers could hedge against this risk category by means of agricultural
insurance.
To qualify as an event risk below normal or above normal, the temperature
observed should not fall into the heat wave or cold spell and parameters. This
discourages the provision of insurance products to the agricultural sector, since
even slight deviations from normal, average temperature patterns (even one
standard deviation from the average temperature value) can affect agricultural
yields negatively. Insurance products do not cover such risk and only pay out on
the occurrence of an exceptional event that leads to extreme loss (Roberts, 2002).

51
2.18.3 Output/yield risk
Output risk refers to the possibility of obtaining a less than normal output on inputs.
Yield risk, in contrast to event risk, implies low risk with associated high probability
of occurrence (Parihar, 2003). One of the contributors to yield risk is the ground
temperature as an input to the maize production process. It is on this yield risk that
we are able to suggest a possible solution. We assume that maize production is
negatively affected given two temperature data points.
2.19 Variables that affect maize production
Maize production is affected by various factors and homeostasis among these
factors is crucial for the proper development of the maize plant. Below are factors
that come into play when dealing with climatic and non-climatic specific factors of
maize production.
According to Du Plessis (2004), from the variables affecting maize production are
as follows:
 Temperature
 Water / Rain Fall
 Soil Requirements
 Planting Date
 Planting Depth
 Plant Population and Row Width
 Maize cultivar planning
2.19.1.1 Temperature
Global maize yields are forecast to decline in response to increasing temperature,
particularly as the upper range of growing season temperatures become hotter
(Lobell, D. B., Banziger, 2009). Further to the decline in global maize production is

52
that the sensitivity of crop yields to increased temperature is often estimated
through analysis of variability in annual yield and growing season temperature,
(Lobell, Banziger, Magorokosho, and Vivek, B (2011). The conclusion can be
drawn that temperature is a variable related specifically to the climatic conditions
that play a fundamental role in the life cycle of the maize production. Du Plessis,
(2004), suggests that, ‘Maize is a warm weather crop and is not grown in areas
where the mean daily temperature is less than 19 ºC or where the mean of the
summer months is less than 23 ºC’. The minimum temperature for maize
germination is 10 ºC, however the optimal soil temperature for large scale
production is between 16 ºC and 18 ºC. Du Plessis (2004), also states that the
‘critical temperature affecting maize production is 32 ºC’. This variable is the focus
of the study and may present a hedging opportunity in maize production.
2.19.1.2 Water
Approximately 10 to 16 kg of grain are produced for every millimetre of water used
(Du Plessis, 2004). A yield of 3152 kilograms per hectar requires between 350 and
450 mm of rain per annum according to Du Plessis, (2004). At maturity, each
maize plant will have used 250 litres of water in the absence of moisture stress.
Weather derivatives can be put in place to mitigate the negative effects of too
much or too little rain. See section on “World Food Program” cited in this study for
an example from literature.
2.19.1.3 Soil requirements
The soil requirements may vary by location however the soil is controlled by the
farming process of the previous growing season. The best soil for maize production
has favourable morphological properties, optimal moisture, adequate internal
drainage, balanced quantities of plant nutrients and chemical properties, (Du
Plessis, 2004).

53
The weather of the previous seasons may also have an effect on the soil properties
as large-scale maize production takes place on soils with a clay content of less
than 10 per cent (sandy soils) or in excess of 30 per cent (clay and clay-loam
soils) the level of rain the growing area will have an effect on the clay composition
of the soil, too much rain and the clay would naturally drain away due to erosion,
and too little rain and the clay could dry provide a hard barrier for an effective root
system of the maize plants to grow and develop naturally, (Du Plessis, 2004).
2.19.1.4 Planting date
The planting date of the maize seed also plays a critical role in the production; the
weather on this planting date may also have an impact on the ultimate production
potential and directly creates a level of output risk. A minimum air temperature of
10 to 15 ºC should be maintained for seven successive days, germination should
take place. Almost no germination or growth takes place below 10 ºC. Planting
should be scheduled such that the most heat and water sensitive growth stage of
maize (i.e. the flowering stage) does not coincide with midsummer droughts, (Du
Plessis, 2004).
2.19.1.5 Planting depth and plant technique
Planting depth of maize is linked directly to the soil content and thus is also linked
to the weather, should the ground be water saturated the planting depth of the
maize seed will alter. In general planting depth may vary from 5 to 10 cm,
depending on the soil type and planting date. As a rule, planting should be
shallower in heavier soils than in sandy soils, (Du Plessis, 2004).
2.19.1.6 Plant population and row width
An aspect not directly linked to weather is the plant population per unit area and
the row width. These two aspects are controlled by the farmer and are included to

54
highlight the aspects of the maize production that the farmer can control. Row
widths under dry (rain fall linked) land conditions can vary from 0,91 m to 2,1 or 2-
3m, depending on mechanical equipment available and type of soil tillage system
used. Some of the factors affecting maize production are production systems, seed
varieties, quality of production inputs and research and innovation into production
systems and innovation in production inputs, (Du Plessis, 2004).
Production potential appears to be higher in temperate environments than in
tropical environments. As ‘an example of differences in production systems, the
average white maize yield in Zimbabwe on large-scale commercial farms averages
over 4 tons per hectare’ (Du Plessis, 2004) this is in comparison to 1 ton per
hectare in the small-scale commercial and subsistence farming concerns. Much of
that difference is the result of differences in moisture regime and soil quality, but
part would remain even if these factors were controlled. These have all been
addressed above.
South Africa is described as being unique with regard to crop production. The
unique characteristics are: Erratic rainfall, distance from world market and scarcity
of high potential / nutrient rich soil.
2.19.1.7 Maize cultivar planning
At the end of each growing season a maize producer decides which cultivars are to
be planted during the following growing season. Research has shown that a
correctly planned cultivar choice can contribute greatly to reduce output risk and
constitutes an important part of the producer’s production planning. This
phenomenon could be categorised as a systematic risk to maize production. This
relates to the maize growing process and does not relate specifically to the
weather experienced during the maize production process. Maize cultivars differ in
one or more of a number of characteristics. Each cultivar has a particular
adaptability and yield potential. In this study the various cultivars have been

55
presented to allow for a understanding of which cultivars grow in the various
regions. The adaptation is a modification of the maize plant to deal with
temperature variation, (Du Plessis, 2004).
2.20 Maize production and climate change
Studies have indicated that precipitation and temperature have opposite effects of
output levels and variability of maize output (Chi-Chung et al, 2004). Anderson &
Hazell (1987) have also argued that adoption of common high yielding varieties,
uniform planting practises and common timing of field operations have caused the
yields of many crops to become more influenced by weather patterns, especially in
developing countries like South Africa.
A study by the IPCC (2007), has pinpointed Africa to be one of the most exposed
continents to suffer the devastating effects of climate change and climate
variability, with colossal economic impacts because it often lacks adaptive capacity
IPCC (2007). Many market observers view the African rain-fed agriculture to be the
most vulnerable sector to climate variability. The potential impacts of climate
change on agriculture are highly uncertain.
The report by World Meteorological Organization (WMO) (1996) revealed that the
overall climate change is expected to add in one way or another to the difficulties of
food production and scarcity. The report also stated that reduced availability of
water resources would pose one of the greatest problems to agriculture and food
production, especially in the developing countries. Also Katz and Brown (1992)
reported that climate variability is likely to increase under global warming both in
absolute and relative terms.

56
2.21 Summary
The literature review started with an explanation of the financial instrument that is
the weather derivative. The mechanics of the weather derivatives were presented
along with the individual components that go into making a weather derivative. The
topics necessary for the exploration of weather derivatives were:
 Physical marketplace for weather derivatives
 Pricing of weather derivatives
 Empirical examples of weather derivatives
 Modelling of temperature and its challenges
Once weather derivatives were introduced and unpacked the literature review then
moved on explain Maize and Maize production issues within a South Africa
context. The following topical areas were reviewed:
 Maize production in South Africa
 Maize production and climate change
 Risks associated with maize production and finally,
 Variables that affect maize production

57
Chapter 3
3 Research methodology
3.1 Research design
The research design is a quantitative investigation of a temperature based weather
derivative and its effects on maize output risk created by temperature fluctuations
in the changing climate. This will be done using a control and test group of farmers
exposed to the same weather observations. Both control and test farmers will be
exposed to the same temperature variations and the same time period.A
comparison of the revenue potential of each group will be conducted. The revenue
calculated based on the test farmer’s automatic use of the weather derivatives will
be proposed. The set of farmers who in theory produce the highest revenue will
determine the success of maize production hedging with weather derivatives.
3.2 Research Population
Maize is produced in all nine provinces of South Africa, (Geyser, 2004). However,
the Free State province produces an average of 34.6 per cent of total South
African production. As cited in RSA, (1996, 2000).The research population will
include maize production in tonnes and temperature in degrees Celsius.
Maize output has been collected from the website called www.grainSA.co.za, this
is a website that publishes maize data on a regular basis and is also the authority
for organising maize industry across South Africa. Maize was also identified based
on the gross annual crop value.

58
Temperature observations were collected from the South African National Weather
Service. The South African National Weather Service has stations that are located
in a close proximity to the test farm used in this study, on which the maize is
produced.
3.3 Research instruments
3.3.1 Test experiment
This study will analyse the revenue potential of two theoretical farmers. The first
farmer will be named the control group who will not have any access to derivative
instruments hedging revenue and will be exposed to temperature fluctuations. The
second group of theoretical farmers would be able to hedge their revenue
fluctuations using two temperature derivatives based on the contract terms
identified in the literature review of this study.
3.3.2 Test instrument
The test instrument will be selected from the derivatives found in the literature
review as identified by Cao and Wei (2004). They are presented in Table 4 below.

59
Table 4: The terms of the test temperature derivative
Hot Year
Derivative
Cold Year
Derivative
Location
Free State
Province4
Free State
Province5
Accumulation Period
Annual Annual
Tick Size [Payout per
Degree Celsius]
R35,000 R35,000
Strike Temperature (in
Celsius)
24.16 23.17
Maximum payout R350,000 R350,000
Option Premium R200,000 R200,000
Source: Cao and Wei (2004)
4Adapted for this study
5Adapted for this study
6 Average temperature for the data set. Taken from Data obtained from the National Weather
Service.
7 Average temperature for the data set. Taken from Data obtained from the National Weather
Service.

Temperature-based weather derivatives as a technique for maize production hedging

Temperature-based weather derivatives as a technique for maize production hedging

Recommended

Recommended

More Related Content

Similar to Temperature-based weather derivatives as a technique for maize production hedging

Similar to Temperature-based weather derivatives as a technique for maize production hedging (20)

Temperature-based weather derivatives as a technique for maize production hedging