Any company that owns commodities, either through production or merchant activities, needs to know not only the current value of those commodities based on market prices, but also needs to develop a view of the future value of those commodities during the time that they are projected to be held in inventory. Additionally, agreements to purchase commodities in the future must be accounted for, not only at their agreed or projected purchase price, but also during their anticipated holding period. Commodity prices are constantly changing and are driven by market forces that are virtually impossible to predict with any degree of certainty. As such, accurately forecasting costs and price exposures is difficult at best, and particularly so now, given the rapidly changing supply and demand patterns that define the global commodity complex. Huge growth in demand for all commodities in Asia, the rapid rise of agricultural exports from developing countries in the Asia-Pac region, and the shale revolution that is driving unprecedented growth in US oil production, are all examples of the new dynamics that have fundamentally altered price formation in markets around the world. In this globalized and increasingly interconnected market-place, which is being constantly buffeted by economic uncertainty, predicting future prices is more difficult, but perhaps more important, than ever.

1. Managing Forward Curves
in a Complex Market
WHITE PAPER
Sponsored by

2. © Commodity Technology Advisory LLC, 2014
Introduction
Commodity prices are constantly changing and are
driven by market forces that are virtually impossible
to predict with any degree of certainty. As such,
accurately forecasting costs and price exposures is
difficult at best, and particularly so now, given the
rapidly changing supply and demand patterns that
define the global commodity complex. Huge growth
in demand for all commodities in Asia, the rapid rise
of agricultural exports from developing countries in
the Asia-Pac region, and the shale revolution that
is driving unprecedented growth in US oil produc-
tion, are all examples of the new dynamics that have
fundamentally altered price formation in markets
around the world. In this globalized and increasingly
interconnected market-place, which is being con-
stantly buffeted by economic uncertainty, predicting
future prices is more difficult, but perhaps more im-
portant, than ever.
Along with ever shifting supply and demand
patterns, new markets, trading hubs, and storage
facilities have opened, creating new trading loca-
tions where none existed just a few short years ago.
Though many have already become recognized pric-
ing centers, others are, and continue to be, rather
illiquid, with few transactions and little knowledge in
the broader market as how to price those locations
on a future basis.
Even in areas and markets that have had a long
and sustained history of prices, new productive re-
gions (such the massive growth in natural gas pro-
duction from the Marcellus Shale in the Northeast
US, for example) can create a lasting and dramatic
change in futures prices. Future price prediction then becomes difficult
as the sudden change in fundamentals produces prices that are uncor-
related from historical activity.
With these market changes, the ability to interpret market activity
and measure the future impact of anticipated developments becomes
more imperative. Defaulting to a common exchange price curve or at-
tempting to simply project historical prices forward is insufficient in this
dynamic environment as it ignores the both the global impact of chang-
ing supply and demand patterns and the growing inter-relationships
amongst commodities and markets.
While some wholesale spot markets that trade on exchanges,
such as Henry Hub’s natural gas contract, are well established, highly
liquid and somewhat seasonally predictable, the majority of commodity
trading locations and markets around the globe are not, and exchange
data is either not directly reflective or is unreliable. It’s these imperfect,
inefficient and sometimes insufficiently liquid wholesale spot markets
where the need for careful and thoughtful modeling of future prices, or
the “forward curve”, becomes an essential exercise in risk management
and financial reporting for commodity trading companies.
In this paper, we’ll examine the complexities associated with the
development, and the specific uses, of forward price curves. In addi-
tion, we’ll review a sophisticated technology available from DataGenic –
the Genic CurveBuilder - that can automate and reduce the complexity
associated with the development of forward price curves.
Any company that owns commodities, either through production or merchant activities, needs to know not only the
current value of those commodities based on market prices, but also needs to develop a view of the future value
of those commodities during the time that they are projected to be held in inventory. Additionally, agreements to
purchase commodities in the future must be accounted for, not only at their agreed or projected purchase price,
but also during their anticipated holding period.

3. © Commodity Technology Advisory LLC, 2015, All Rights Reserved.
A ComTech Advisory WhitepaperManaging Forward Curves in a Complex Market
FORWARD CURVES DEFINED
USES OF FORWARD CURVES
The term “forward curve” refers to a series of sequential prices either for future delivery of an asset or expected
future settlements of an index. Established futures markets, such as the NYMEX Henry Hub natural gas contract,
provide a series of future month contracts which are traded for fixed prices. These published future month prices
take on a curve shape when graphed, and are thus referred to as the “forward curve”.
A forward curve can be derived for any commodity
with a forward delivery market; however, the accu-
racy and completeness of that curve is going to de-
pend on a number of factors, and primarily on the
liquidity of each forward period. Unfortunately, given
The predominant use of forward curves is in the
preparation of corporate financial statements. Com-
panies will use forward curves as key inputs to deriv-
ative valuation models in order to calculate the fair
value of commodity inventories or financial instru-
ments that are carried on the balance sheet.
For US-based, public companies that operate
under the oversight of the Securities and Exchange
Commission (SEC), this valuation activity is gov-
erned by GAAP, and specifically ASC Topic 820
(formerly, SFAS-57). Amongst its requirements,
Topic 820 states that companies should use mar-
ket-based price inputs and should disclose the re-
liability of those inputs. Input reliability is classified
as either “level 1” (unadjusted quotes from active
markets), “level 2” (quotes from inactive markets or
markets for similar instruments), or “level 3” (price
inputs based on management assumptions). These
reliability level requirements often mean that compa-
nies must use the most active market quotes, even
The second common use of forward curves is in asset valuation for ei-
ther planning purposes or dynamic hedging. As these valuations are
not part of, or included, in the preparation of financial statement, com-
panies may use something other than exchange-based curves. This is
especially helpful in cases where the operating characteristics of a par-
ticular asset are more granular than available market quotes; that is,
they operate in a market or region not directly traded or otherwise well
reflected by an exchange instrument. In this case, using derived curves
would provide the asset holder with a better estimate of the asset’s cur-
rent and future value.
With an improved estimate of the asset’s value, the asset holder
would be in a better position to manage the asset’s net risk via pro-
duction or fuels hedging, or operational adjustments to maximize val-
ue. However, again, it’s important to remember that such price curves
would not meet GAAP definitions for input price reliability.
that most markets and/exchanges do not exhibit high liquidity in all fu-
ture periods, it is generally best practice to derive the curve from many
sources of market data – including exchanges, broker marks, trader
indications and independent data publishers.
FINANCIAL STATEMENTS AND
FORWARD CURVES
ASSET VALUATION
in instances where those markets are quoted as strips as opposed to
individual months.
Accidentally using lower-level price inputs or misrepresenting the
reliability of price inputs may put the company at risk of re-statement
in future periods; and in the process, bring increased scrutiny of their
accounting and management practices by regulators and shareholders.

4. © Commodity Technology Advisory LLC, 2015, All Rights Reserved.
A ComTech Advisory WhitepaperManaging Forward Curves in a Complex Market
RISK MANAGEMENT AND
REPORTING
In addition, settlement data from an exchange will be
limited to transactions that have been executed across
that platform and the “accuracy” of that data may be
constrained by the liquidity in those markets – the
fewer the deals consummated at any particular mar-
ket point, the greater the impact an anomalous trade
will have in establishing the published price. Further,
should a trade not be consummated for a particular
market point in any given period, the exchange will
still “settle” its open interest using a formula-based ap-
proach in order to keep margin accounts in balance.
Clearly, simply relying on exchange data to es-
tablish forward prices may be insufficient, particularly
for illiquid points or markets. So, in order to meet a
company’s requirement for forward curves reflective
of their markets and curve usage, more robust curves
can be internally developed against independent mar-
ket data aggregated from multiple sources.
A third common use for forward curves is in risk man-
agement and reporting; and for these purposes, prac-
tices can vary widely amongst market participants.
Some companies may wish to have Value-at-
Risk measurements and limits-monitoring processes
match observable market data regardless of granu-
larity. In this case, an exchange-based curve source
will likely be the best option for forward curve devel-
opment.
Other companies may wish to apply liquidity and
seasonality adjustments if they believe those practic-
SOURCES OF FORWARD CURVE DATA
There are many choices available to market participants seeking
forward curve data sources. The most common sources are exchanges,
brokers, data publishers, data distributors, ETRM system vendors, and in-
ternally-developed models. It is important to understand your company’s
intended use of any particular forward curve in order to select the appro-
priate sources and methodologies for deriving those curves. It is also key
to understand the limitations and methodologies inherent in each of the
selected data sources.
Internally modeled curves may be the only option where reliable
market data does not exist (e.g. illiquid points and tenors). In these cir-
cumstances, the forward curve’s quality is highly dependent on the qual-
ity of the market data inputs, modeling assumptions and methodology.
Whenever internally modeled curves are used, calibration and back-test-
ing should be done regularly to validate the quality of the curve and its
assumptions. Additionally, when possible, internally developed curves
should be compared to independently modeled curves for further valida-
tion.
es provide a more nuanced view of firm risk. For these companies, the
use of non-exchange sources, in addition to exchange data, may provide
them with the better fit curves that reflect their operations and risk port-
folio.
Regardless of which situation a company finds themselves, best
risk practices dictate that a curve validation process is used in which
independent forward curve data is compared to the forward curves that
they use for financial reporting, risk measurement, and risk reporting.
Companies that utilize forward curves derived from multiple sources, or
with internally-developed adjustments, should enshrine a regular testing
of those curves and adjustments as part of their risk management poli-
cies. And most critically, all forward curve information should be archived
indefinitely for audit and compliance purposes.
As previously noted, the data used for the construction of forward curves will likely differ, sometimes dramatically,
between different sources. Market activity, knowledge, and insights available to those different sources will impact
their views of value of the commodity in the future. Different sources may, and usually will, provide a different set
of periods over different time horizons. For example, an exchange may have monthly contracts that will extend for
ten years, while an over-the-counter broker may quote future prices as multi-month strips that extend over a period
of 5 years.

5. © Commodity Technology Advisory LLC, 2015, All Rights Reserved.
A ComTech Advisory WhitepaperManaging Forward Curves in a Complex Market
UNLIMITED CURVE BUILDING
FLEXIBILITY
DATAGENIC FORWARD CURVE
SOLUTION - GENIC CURVEBUILDER
Given that OTC and spot markets can be volatile, illiquid and inefficient, the need for careful and detailed modelling
of forward prices is an essential aspect of risk management in the industry. Even when curves are available from
exchanges, brokers, or published, often they will not match your company’s needs for achieving accurate mark-to-
market, value at risk and portfolio optimisation calculations. Genic CurveBuilder provides automated generation of
fully customized forward curves based on your choice of source data coupled with rules that you define.
Genic CurveBuilder is an intelligent, fully automat-
ed, powerful and flexible forward curve builder and
price data management application. Utilizing built-in
artificial intelligence, this SMART application offers
complete flexibility that goes well beyond standard
curve configuration. Modelling definitions include
basis and arbitrage-free calculations, interpolation
and extrapolation, shaping and smoothing, flexible tenor specification,
prioritization and weighting.
For the simple to the most complex curves, a rules-based frame-
work offers unlimited flexibility in the creation of the curves. Using a de-
finable English language-based logic, all rules are then interpreted auto-
matically using an expert system. Rules can be expanded and re-applied
to other curves. The process for curve building can be data event driven
or scheduled, allowing for end-of-day and real-time creation. Contract
rollover calendars along with holiday calendars are utilized to ensure
accurate market condition modelling.

6. © Commodity Technology Advisory LLC, 2015, All Rights Reserved.
A ComTech Advisory WhitepaperManaging Forward Curves in a Complex Market
IN-MEMORY CURVE BUILDING
FORWARD CURVE REAL-TIME MONITOR
FORWARD CURVE SECURITY
Performance can mean the difference between suc-
cess and failure in building forward curves. Genic
CurveBuilder uses ‘in-memory’ processing to speed
up calculations and processing time, thereby reduc-
ing data access delays. Curves are built in rapid time
ensuring the end-users and systems have immediate
and correct access to information required for rapid
decision making using real-time curve building.
The Genic CurveBuilder provides interactive real-time monitoring and
visualisation with pro-active alerts for monitoring the curve build pro-
cesses. Users can quickly assess the business impact and take imme-
diate corrective action.
Security should be robust not complex. With Genic CurveBuilder you
get a role based security access control segregated into resource lev-
el and workgroup level coupled with a data encryption security layer,
using single ‘sign-on’ authentication (Active Directory). You can be as-
sured on the protection of your data assets.
SUMMARY
Developing and managing forward curves is a chal-
lenge in any environment – selecting the appropri-
ate data sources that meet your particular curve use
case or application; developing appropriate curve
adjustments to meet your particular market, location
or asset; and ensuring appropriate controls and con-
stant testing can be a complex exercise. However, in
a market that is constantly hammered by economic
uncertainty, rapidly changing supply and demand
patterns, and intense regulatory scrutiny, managing
the complexities associated with curve development
and maintenance becomes even more difficult, and
even more critical.
In this environment, spreadsheets or simplistic
models are insufficient as they are prone to errors
and lack the necessary sophistication to perform the
complex multi-commodity, multi-dimensional analysis that is required in
a globally integrated marketplace. Without a dedicated curve develop-
ment and management solution, like the Genic CurveBuilder from Dat-
aGenic, gaining accurate insights and ensuring proper financial report-
ing and risk management of trading and production assets becomes a
tenuous proposition at best; and at worst, may actually increase the risk
of financial loss, shareholder dissatisfaction and regulatory scrutiny of
your operations.
CURVE VISUALISATION
& ANALYSIS
Working with forward curves can require being in-
teractive. Being able to visualise market dynamics,
can instantly help the business or manager to rapidly
process multiple complex data configurations.
Genic CurveBuilder provides visualisation and
analysis of curves within different curve structure
views and sources and with pre-formed reports and
options.

7. DataGenic is the leading global provider of on-premise and in-cloud Smart
Commodity Data Management software, delivering intelligent analytics,
real-time data content and proven business value.
The innovative solutions include a data-agnostic multi-commodity data management platform, visual
mapping and management of business processes, extensive and extensible data quality management,
unlimited forward curves construction and an intelligent decision framework. DataGenic customers
include participants in the energy, metals, minerals, chemicals, agriculture, shipping and food and
beverage industries.
DataGenic operates in Europe, Asia and the Americas.
For more information, please contact DataGenic at:
Tel: +44 203 651 5560 or +1 281 810 8290
info@datagenicgroup.com
ABOUT DATAGENIC LTD

8. ABOUT
Commodity
Technology
Advisory
LLC
Commodity Technology Advisory is the leading analyst organization covering the
ETRM and CTRM markets. We provide the invaluable insights into the issues and
trends affecting the users and providers of the technologies that are crucial for
success in the constantly evolving global commodities markets.
Patrick Reames and Gary Vasey head our team, whose combined 60-plus years in the
energy and commodities markets, provides depth of understanding of the market and
its issues that is unmatched and unrivaled by any analyst group.
For more information, please visit:
ComTech Advisory also hosts the CTRMCenter, your online portal with news and
views about commodity markets and technology as well as a comprehensive online
directory of software and services providers.
Please visit the CTRMCenter at:
19901 Southwest Freeway
Sugar Land TX 77479
+1 281 207 5412
Prague, Czech Republic
+420 775 718 112
ComTechAdvisory.com
Email: info@comtechadvisory.com
www.comtechadvisory.com
www.ctrmcenter.com