The Effects of Currency Futures Trading on the Turkish Spot Market - Samet Marasli

Università degli Studi di Roma “Tor Vergata”
Facoltà di Economia
Master of Science in Economics
The Effects of Currency Futures Trading
on the Turkish Spot Market
Supervisor: Candidate:
Prof. Gianni Nicolini Samet Maraşlı
March 2016

iii
ABSTRACT
The aim of this study is to investigate the impact of the currency futures introduction on
the volatility of the underlying Turkish currency market. For this purpose, a GARCH (1,1)
model has been employed by using the returns to a currency basket consist of Euro and
USD. The analysis yielded the following results: First, the introduction of currency futures
reduces the currency market volatility. Second, by taking into account the 2008 global
crisis, the futures trading introduction yields even more beneficial results. Finally, the onset
of currency futures increases the spot market efficiency.

iv
TABLE OF CONTENTS
LIST OF TABLES............................................................................................................ v
LIST OF FIGURES......................................................................................................... vi
INTRODUCTION............................................................................................................. 1
1. THE TURKISH MARKET IN BRIEF....................................................................... 2
1.1. EFFECTS OF 2008-2009 GLOBAL CRISIS....................................................... 2
1.2. THE CURRENCY MARKET............................................................................... 3
1.3. THE DERIVATIVES MARKET.......................................................................... 4
2. LITERATURE REVIEW ............................................................................................ 5
2.1.THE THEORETICAL DEBATE .......................................................................... 5
2.2. THE EMPIRICAL LITERATURE...................................................................... 6
2.2.1. EARLY STUDIES........................................................................................... 6
2.2.2. CONTEMPORARY STUDIES...................................................................... 7
4. METHODOLOGY AND DATA ................................................................................. 9
4.1. CONDITIONAL HETEROSKEDASTICITY MODELS .................................. 9
4.1.1. ARCH ............................................................................................................... 9
4.1.2. GENERALIZED ARCH (GARCH) ............................................................ 10
4.2. DATA..................................................................................................................... 12
4.3. METHODOLOGICAL ISSUES......................................................................... 14
5. EMPIRICAL RESULTS............................................................................................ 14
CONCLUSIONS ............................................................................................................. 22
Bibliography.................................................................................................................... 23

v
LIST OF TABLES
Table 1- Test fot unit roots.................................................................................................13
Table 2- Descriptive statistics............................................................................................15
Table 3- Selection criteria..................................................................................................16
Table 4 - GARCH (1,1) estimates without the crisis dummy............................................18
Table 5 - GARCH (1,1) estimates with the crisis dummy.................................................19
Table 6 - Diagnostic Checking ..........................................................................................21

vi
LIST OF FIGURES
Figure 1: GDP of Turkey from 1980 to 2010...................................................................................3
Figure 2 : Euro and USD exchange rates series ...............................................................................4
Figure 3- Return of the currency basket between 1999-2011.........................................................11

1
INTRODUCTION
Currency futures of emerging markets has appeared as an interesting subject to investigate
due to increased volume and volatility of investment movements to the emerging countries.
Both national and foreign bodies have exposed to lots of currency risk due to rapid
economic growth and financial liberalization of emerging countries. Therefore, demand for
instruments to manage the currency risk such as derivatives, is growing rapidly in these
markets. Emerging countries such as Turkey has introduced currency hedging instruments
as they moved from fixed to floating exchange regimes. Even these instruments are
frequently used in these markets, their effects on the underlying cash markets are still in
question.
The focus of the argument about derivatives trading in the economic literatures is
whether the futures trading has a positive or negative influence on the underlying market
volatility. Some studies argue that futures trading increases the spot market volatility
because uninformed traders are attracted to these markets due to these market’s high degree
of leverage. Since the future traders have less information about the market relative to spot
market traders, they tend to make transactions which increase the market volatility.
Speculators are also another part of the argument since the futures market enables
speculation. On the other hand, some studies show that the futures markets increase
efficiency and enhance market depth. Thus, they are favorable for the underlying spot
markets. Advocates of the futures markets also claim that since these markets offer
protection to traders by means of hedging, it helps to stabilize the market and reduce the
risk of traders. Both the arguments against and in favor of futures trading have been
analyzed extensively by studying developed markets’ stock index and currency futures.
However, the same issue has not been addressed enough in the context of currency markets
of emerging countries.
The purpose of this paper is to study the effect of currency futures introduction on
the underlying Turkish stock market volatility. This analysis brings a new perspective to

2
the studies about the Turkish currency derivatives market for two reasons. First, it takes
into account the 2008 global crisis while studying the effects of currency futures trading.
Second, the study uses a larger data set than that employed by (Oduncu, 2011).
The remainder of the paper is organized as follows. Section 1 gives brief
information about the Turkish market. Section 2 presents a short review of the theoretical
literature and main results of some relevant empirical studies. Section 3 introduces the
methodology and data used in the analysis. Empirical results are presented in Section 4 and
some conclusions are given at the end of the study.
1. THE TURKISH MARKET IN BRIEF
1.1. EFFECTS OF 2008-2009 GLOBAL CRISIS
Starting with the financial liberalization policies in the late 1980s, the Turkish economy
experienced several boom and busts until early 2000s. Later, the economy exercised a
strong and continues expansion until 2007. Some important macroeconomic improvements
such as reduced budget deficits and public debts were the reasons for this expansion. In
addition, the great political stability let decrease capital costs and risk premium, and
increased commercial activities. On the other hand, stronger relations with the European
Union had a positive influence on the investor confidence.1
The 2008 global crisis severely affected the Turkish economy, and unlike the other
crisis experienced by Turkey, it was caused by an extraordinary foreign demand shock.
During the crisis between 2008 and 2009, the exports decreased dramatically and it was
followed by an immense downturn in GDP as it is illustrated in the Figure 1. Fortunately,
the Turkish economy found its way to recover after the middle of 2009.2
1(Rawdanowicz, 2010)
2 The comments on the Turkish economy are based on the OECD website.

3
Figure 1: GDP of Turkey from 1980 to 20103
1.2. THE CURRENCY MARKET
Like many other emerging countries, Turkey used to have a fixed exchange rate regime.
During 2001, Turkey underwent some severe political crisis which also effected the
economy significantly. Therefore, the government took some fundamental precautions
such exchange regime change in order to prevent further crisis.
At the end of the February 2001 the floating exchange rate regime introduced, and
the central bank limited its intervention in the foreign exchange market with intentions to
prevent excessive volatility. The first month after the regime change, the exchange rates
contained many outliers, and therefore exhibited different statistical properties. After the
onset of floating regime, USD exchange rate increased by almost 40% in two days. There
is a debate on the hypothesis that whether the central bank intervention decreases
expected exchange rate volatility. Anyway, the central bank of Turkish Republic directly
mediated in the foreign exchange market on several cases to avoid excessive volatility in
exchange rates since the onset of the floating exchange rate regime.
3 Data collected from data.worldbank.org/country/turkey/turkish
0
200400600800
gdp
1980 1990 2000 2010
Time

4
Figure 2 illustrates Euro and USD exchange rate series from 1999 to 2011.
(Coudert, Couharde, & Mignon, 2011) argue that the volatility of exchange rates tends to
increase more than proportionally with the indicator of global financial stresses. The
Turkish experience of currency market was no different during the global crisis between
the 2008 and 2009.
Figure 2 : Euro and USD exchange rates series
1.3. THE DERIVATIVES MARKET
The demand for derivatives in Turkey had emerged with the financial liberalization policies
in 1980’s. The first stock exchange market called Istanbul Stock Exchange (ISE) was found
in 1986, and the first attempt to build a derivatives market occurred in 1991. After some
unsuccessful ventures, finally the market of Turkish Derivatives Exchange (TurkDEX) was
established in 2003, and started its formal transactions in February 2005 in Izmir.
.5
1
1.5
2
2.5
Euro
0
251
502
753
1004
1255
1506
1757
2008
2259
2510
2761
3012
3263
Time
0
.5
1
1.5
2
USD
0
251
502
753
1004
1255
1506
1757
2008
2259
2510
2761
3012
3263
Time

5
The Turkish derivatives market has grown rapidly through years and was ranked
the world's fastest-growing derivative instruments market in 2006 with a 273% increase.
With the intention of becoming more closely integrated with the global derivatives market,
TurkDEX became a member of the Futures Industry Association in 2009.4
The volume of foreign currency contracts traded in the Turkish derivatives market
was growing rapidly in the first years of its introduction. In 2006, the fastest growing
contract was USD foreign currency future with a 1.1 million contracts, risen 304.7% in one
year.5
During 2008, the trade volume of currency futures lost its pace, whereas stock index
futures became popular.
2. LITERATURE REVIEW
2.1.THE THEORETICAL DEBATE
While futures trading became a substantial part of all main stock and currency markets,
researchers came up with theories regarding whether the futures market increases the
underlying spot market volatility and therefore destabilizes it. One group of researchers
argue that futures trading increases the volatility of spot markets thanks to the high rate of
leverage provided by futures markets and the informational externalities created by
speculative traders. (Cox, 1976) comes up with theoretical explanation which studies the
relationship between futures trading and price variability. He argues that uninformed
speculators reduce the quality of the information in the market and destabilize it. (Stein,
1987) argues that introducing more speculators into the market not only increase the risk
sharing opportunities, but also brings new information to carry on the prices in the markets
which may bring negative externalities on those traders already in the market. According
to Stein's model these externalities may yield destabilized prices and reduced welfare.
4 http://www.borsaistanbul.com/
5 http://www.borsaistanbul.com/

6
Other group of researchers built theories supporting the argument that the futures
markets are useful for the underlying spot markets since they increase the overall market
depth by enhancing information available and increasing price discovery. (Danthine, 1978)
theorizes that futures trading increases market depth thanks to the group of informed
speculators who bring the relevant information to the market, and therefore reduce cash
market volatility. (Powers, 1970) discovers that the random price fluctuations reduce with
the futures trading since it enhances the information flow in the market. (Stroll & Whaley,
1988) argue that futures trading increases market efficiency since index futures expanded
the number of informational routes in the economy which increases the possibility that
correct message is transmitted to the participants of the market. (Bray M. , 1981) developed
a model in which the futures price is a sufficient statistic for information about the spot
price, in other words futures trading increases the overall market informativeness.
To sum up, many scholars provided theoretical frameworks both in favor of and
against the idea that futures markets have a beneficial effect on the underlying cash
markets. The main debate is on whether the speculators are fruitful for the trade since they
increase the information available and provide depth, and so reducing the volatility of the
cash market. While the theoretical debate is inconclusive, empirical literature is also not
providing a certain answer. Nevertheless, greater volume of empirical studies presents
some common results and similar conclusions. The debate whether and how futures trading
affect underlying spot markets still continues.
2.2. THE EMPIRICAL LITERATURE
2.2.1. EARLY STUDIES
Empirical studies on futures markets at its early stage were on Government National
Mortgage Association (GNMA) futures and its impact on the regarding spot market.
(Froewiss, 1978) examines the variability of GNMA prices, and finds no significant change
after the introduction of futures. (Corgel & Gay, 1984) and (Simpson & Ireland) empirical

7
findings show that the introduction of futures trading in GNMA did not have a destabilizing
effect on the spot market. On the other hand, (Figlewski S., 1981) argues that futures
trading in GNMA had a destabilizing effect on the cash market due to speculators' adverse
effect on the market.
2.2.2. CONTEMPORARY STUDIES
Although the relationship between futures trading and underlying spot market arouse
plenty of researcher’s interest for a long time, and there is a great number of studies
regarding the topic, the debate still remains its heat. Most of the contemporary analysis
regarding the relationship between futures and spot markets investigate index futures
markets of developed countries. There are limited number of studies on emerging markets
and the currency futures markets.
First group of studies find the introduction of derivative trading beneficial. (Baklaci
& Tutek, 2006) study the impact of introduction of stock index futures on the volatility of
the Istanbul Stock Exchange 30 (ISE30). They find that despite its short history, the futures
market enhances the market efficiency by improving the rate at which new information is
impounded into spot prices, and significantly decreases the volatility of spot market prices.
(Caglayan, 2011) also investigates the impact of the introduction of index futures on the
volatility of ISE30 by enhancing the scope of Baklaci & Tuteks’s analysis, and she
proposes more or less the same argument. In addition, the author also discovers that after
the introduction of currency futures bad news increased the volatility more than good news
of the same weight. (Bologna & Cavallo, 2002) investigates the effect of the introduction
of stock index futures on the volatility of the Italian Stock Exchange, and they show that
the introduction of futures decreases stock market volatility. Also, they argue that the
impact of futures trading on the underlying market volatility is rather immediate. (Edwards,
1988) studies the daily price volatility of the S&P500 index over a period of sixteen years,
and finds out that futures trading decreases the stock market volatility.

8
The second part of the analysis yield that the derivatives market does not have a
significant beneficial or harmful effect. (Jochum & Kodres, 1998) investigate whether the
introduction of futures on emerging market currencies destabilize the underlying
currencies, and take a close look on the Mexican peso, Brazilian real and Hungarian forint
futures as case studies. This study is worth attention since it is one of the most
comprehensive work with a focus on emerging markets' currency futures. By taking
account the central banks' currency stabilizing policies of the emerging countries, Jochum
& Kodres argue that introduction of futures markets does not have a significant effect on
this spot market’s volatility. Furthermore, (Sahu, 2012) studies the impact of currency
futures on exchange rate volatility of Euro by the introduction of currency futures market
in India, and he argues that currency futures trading neither increase nor decrease the
underlying spot market volatility. Nevertheless, he finds that introduction of currency
futures increases market efficiency by increasing the importance of recent news on spot
market volatility, while decreasing the persistence effect of the old news. (Santoni, 1991)
examines both the percentage changes and intra-day variations in the S&P500 before and
after the introduction of futures trading, and he argues that the spot market volatility is not
affected from the futures trading. (Smith, 1989) claims that the S&P500 futures market
brings both harm and good to spot market volatility which mean it does not have a
significant effect on the market volatility. (Becketti & Roberts, 1990) studied both the
introduction and level of the activity in the S&P500 stock index futures market and
discover little or no relationship between the futures and stock market volatility. (Harris,
1989) studies S&P500 stock index futures market and found a small increase in spot market
volatility after the introduction of futures trading which can be attributed to the introduction
of derivative trade and it is economically insignificant.
The last group of studies find that the existence of derivatives trading is not fruitful
for the spot market. (Röthig, 2004) examines the linkage between currency futures trading
and currency crises, and he argues that for the five countries’ exchange rates including
Korea, Canada, Japan, Australia and Switzerland the evidence illustrates a positive
relationship between currency futures trading activity and spot exchange rate volatility.
(Bhargava & Malhotra, 2007) examines the relationship between trading activity in
currency futures and exchange rate. The authors argue that speculators and day traders

9
destabilize the market for futures, while the effect of hedgers is undetermined. While the
demand of speculators for the futures and the volatility of spot market has an inversely
proportional relationship, the demand of the hedgers depends on the method being used to
figure volatility. (Antoniou & Holmes, 1995)examines the impact of the FTSE-100 Stock
Index Futures on the volatility of the underlying spot market, and suggests that there has
been an increase in spot price volatility. The increased volatility is due to increased speed
and quality of information in the market, and not because of adverse destabilizing effects
caused by speculators. The authors do not perceive the increased volatility as a "bad thing"
since the source of the volatility is increased rate of flow of information (Chatrath & Song,
1998) investigates the relative intraday reactions in the Japanese yen futures and spot
markets to scheduled U.S. macroeconomic announcements. Parallel to the findings of
Antoniou & Holmes, Chatrath &Song argue that the futures trading cause spot market
volatility. (Chatrath, Ramchander, & Song, 1996) examines the impact of speculative
futures trading on the volatility of the S&P 500 index. They argue that there is not any
evidence that speculators contribute to market volatility. Nonetheless, the authors detect
short-lived but significant increase in the currency volatility after a rise in the trading
activity on the futures market.
4. METHODOLOGY AND DATA
4.1. CONDITIONAL HETEROSKEDASTICITY MODELS
4.1.1. ARCH
Autoregregressive conditional heteroscedasticity model abbreviated as ARCH is a famous
model frequently used in the financial analysis since it can overcome the heteroskedasticity
problem. One of the important assumption of the classical linear regression model is that
it has homoscedastic errors which states that the error terms has a constant variance. If this
assumption does not hold it is told that the data exhibits heteroscedasticity. In terms of

10
financial time series, it is common that the error variance change over time due to external
shocks and changing circumstances. Therefore, a model which accounts the
heteroscedasticity phenomenon is needed in order to study financial data accurately such
as the ARCH models.
Volatility clustering is one of the particular feature of financial time series, which
is another reason for the usage of ARCH models. Volatility clustering introduced by
(Mandelbrot, 1963) is a widely observed phenomenon in finance which argues that “large
changes tend to be followed by large changes, of either sign, and small changes tend to be
followed by small changes.” Particularly, it states that the ongoing rate of volatility is likely
to be positively associated with the following prior date’s volatility. It is fair to say that
there is autocorrelation in volatility. ARCH model is competent to investigate these trends
in volatility and detect the effects of volatility clustering. The model captures the
mentioned autocorrelation by adding the former values of squared errors in the calculation
of the conditional variance of the error term.
ARCH models are not anymore popular in the field of finance due to its limitations
and researchers prefer contemporary technics to study financial time series. It is
challenging to decide how many lags of squared residuals should be in the model. In
addition, ARCH models yields lavish conditional variance models with large number of
lags in order to grasp all the dependence in the conditional variance. The coefficients in the
conditional variance must be non-negative since the value of the conditional variance must
be always strictly positive. Third, more parameters in the conditional variance equation
have the risk of violating the non-negativity principle. Finally, the ARCH models fail to
capture the leverage effects. Nevertheless, ARCH model is an important stepping stone for
the models used today in the field of finance namely GARCH family of models.
4.1.2. GENERALIZED ARCH (GARCH)
The generalized autoregressive conditional heteroscedasticity model abbreviated as
GARCH is greatly superior to the ARCH model. (Bollerslev R. , 1986) developed this

11
comparably capable model by making the conditional variance sensitive to its previous
lags. GARCH is widely used for financial analysis instead of ARCH since it’s a more
parsimonious model in terms of the number of parameters, which lowers the probability to
break the non-negativity principle. GARCH (p,q) model is represented as follows:
𝑦𝑡 = 𝛽𝑋𝑡 + 𝜀𝑡 (1)
𝜀𝑡 ~ 𝑁(0, ℎ 𝑡) (2)
ℎ 𝑡 = 𝛼0 + ∑ 𝛼𝑖 𝜀𝑡−𝑖
2𝑝
𝑖 + ∑ 𝛽𝑗
𝑞
𝑖 ℎ 𝑡−𝑗 (3)
The GARCH (1,1) model can be written as a restricted infinite order ARCH model
by using some algebraic manipulations, which means the GARCH (1,1) model with just
three parameters in the conditional variance equation lets an endless number of past
squared errors to effect the current conditional variance. Although it is possible the extend
the model to GARCH(p,q) , the model with three parameters is usually strong enough to
study the volatility clustering in the data and the GARCH(1,1) model is very popular
between the contemporary financial researchers.
Figure 3- Return of the currency basket between 1999-2011

12
4.2. DATA
The data set consists of a twelve-year period from 1999 to 2011, containing 3022
observations in all. It is divided into two sub-periods which contains 1511 observations for
each. One from 1 February 1999 to 1 February 2005, representing the pre-futures period
and the other from 1 February 2005 to 1February 2011, representing the post-futures period
are categorized in order to study whether the presence of futures trading affects daily
volatility in the underlying currency market. The data used in this study are daily exchange
rate of Euro and USD in terms of Turkish Lira. The data is obtained from Central Bank of
the Republic of Turkey. The analysis uses the daily return on the currency basket that is
calculated as:
𝑃𝑡 = 0.5×(
𝐸𝑢𝑟𝑜
𝑇𝐿
) + 0.5×(
𝑈𝑆𝐷
𝑇𝐿
) (4)
where Pt is the value of the currency basket at end of the period t. In general, the price
series do not fluctuate around a constant level, but the returns series usually looks
stationary. Therefore, the first difference of log of exchange rates mentioned as log returns
is used throughout the study. The results are obtained on the basis of Rt, which is the rate
of return in period t, calculated as:
𝑅𝑡 = 𝑙𝑛 (
𝑃𝑡
𝑃𝑡−1
) ×100 (5)
Figure 3 shows the time series graph of Rt , and the introduction of currency futures
illustrated with a straight line in the middle of the time series. The returns seem to alter
around a constant level, but exhibit volatility clustering means large changes in the returns

13
tend to cluster together, and small changes also tend to group together. Both the Augmented
Dickey Fuller (ADF) and the Phillips–Perron (PP)6
tests are carried out for the return series
in order to check the presence of unit root. Table 1 shows the test statistics for total period,
pre-futures and post-futures period of return of exchange rate series. The null hypothesis
of both tests are rejected at the 1% level, indicates the absence of unit root and the series
are stationary.
Table 1- Test fot unit roots
Variables Period
ADF
(with trend)
ADF
(without
trend)
PP
(with
trend)
PP
(without
trend)
Rt Total -49.746 -49.659 -49.516 -49.411
Pre-
futures
-33.795 -33.712 -33.484 -33.381
Post-
futures
-38.699 -38.710 -38.711 -38.721
6
PP tests has two advantages over the ADF tests: first PP tests are more powerful to general
forms of heteroskedasticity in the error term; second, unlike ADF, for the test regression it is not
necessary to specify a lag length

14
4.3. METHODOLOGICAL ISSUES
As mentioned before, the relationship between currency futures trading and market
volatility for the Euro and USD Dollar currencies is examined addressing three precise
questions:
-Does the introduction of futures trading affect the volatility of Turkish currency market
and how?
-What is the impact of the global financial crisis in 2008 by considering the present analysis
of currency futures?
-How does the existence of currency futures affect the spot market’s efficiency?
In order to find answers to these questions the study proceeds as follows:
- The effect of the futures trading on volatility is studied with the help of a dummy variable
added in the conditional variance equation of the GARCH model.
- Another dummy variable added to the conditional variance equation in order to determine
the impact of the global crisis and commented on the relevant coefficients.
- Relevant coefficients and measures are compared between pre-futures and post-futures
period and evaluated in terms of market efficiency.
5. EMPIRICAL RESULTS
Before starting to the econometric analysis, some descriptive statistics including mean,
standard deviation, skewness, kurtosis, Ljung-Box (Q) test and Engle’s ARCH test (LM)
results for the returns of the sub-periods and the whole period are considered and tabulated
in Table 2.

15
Table 2- Descriptive statistics
Variables Period
Std.
Dev.
Skewness Kurtosis Q (20) Q (5) LM (10) LM (5)
𝑅𝑡 Total 1.149 8.242 240.166 159.19 116.87 107.733 104.996
Pre-
futures 1.361 9.819 240.246 149.13 112.4 53.706 52.580
Post-
futures 0.886 0.216 14.4399 40.151 7.7755 203.782 165.954
As illustrated in the Table 2 the standard deviation of the return series of the
currency basket showed a significant decrease after the introduction of futures, in
comparison to the pre-futures period. On the basis of this measure, the volatility of the cash
market with currency futures is significantly lower, and introduction of futures has not
destabilized the market. Yet, drawing inference by just comparing the standard deviations
is quite superficial, and further analysis is needed in order to make a concrete decision
about the cash market volatility.
The kurtosis of the all periods time series are found to be greater than 3, which
states that the distribution of all sample of returns are fat tailed. In addition, by considering
that all the period of series has skewness different than 0, it would be fair to comment that
the data for all periods do not have a normal distribution.7
Furthermore, since the sample
data exhibits excess kurtosis (leptokurtosis in other words), predicting a fatter-tailed
distribution such as student’s t may yield more fruitful results.
In general, financial time series data exhibit the above mentioned problem,
heteroskedasticity, and in order to detect if the data relevant to this study bear this problem
two tests with various lags are undertaken. First, Ljung-Box test with 20 and 10 lags are
7 Jarque-Bera test for normality is also exercised which tests if a sample data has the skewness and
kurtosis consistent with the normal distribution. The null hypothesis that is the joint assumption of
skewness and the excess kurtosis being zero is rejected for all the periods of sample data.

16
implemented, to analyze if there is serial correlation in returns, and for all data sets, the
null hypothesis that is the data are independently distributed is rejected. Second, Engle’s
ARCH test with 5 and 10 lags are applied, and the analysis yields that the null hypothesis
that is the absence of ARCH components is rejected. In conclusion, all the evidences
presented above submit that the return series exhibit ARCH type habits such as clustering
volatility, leptokurtosis and heteroskedastic error terms, thus ARCH/GARCH type models
are suitable for the volatility estimations.
Table 3- Selection criteria
Number of lags Adjusted 𝑹 𝟐 F-test AIC BIC
1 0.0300 63.58 9389.98 9408.021
2 0.0398 45.23 9298.127 9322.182
3 0.0397 42.63 9299.369 9329.437
In order to detect the most suitable GARCH model, both for the mean and the
variance equation a series of alternative provisions are compared. Three different models
are examined in order to find the suitable mean equation, which includes AR models with
one, two and three lags. Table 3 illustrates that the equation with two lags seems marginally
superior by considering the results in terms of adjusted R2
and both of the information
criterions. On the other hand, the equation with one lag seems favorable in terms of the F-
test. Since in terms of statistics parsimony of parameters is a desirable criterion, and since
equations with one and two lags are almost identical, it is favorable to choose the equation
with one lag.
As mentioned in the previous part, it is widely accepted that the GARCH (1,1)
model is the most parsimonious demonstration of conditional variance, which optimally
fits many financial time series, and the model has been commonly used in the literature.
Therefore, the GARCH (1,1) model is used in this study. Since the return series display a
significant amount of excess kurtosis, student’s t-distribution is used in the maximum
likelihood function to estimate the GARCH model. The model that offers the best fit is as
follows:

17
𝑅𝑡 = 𝛽0 + 𝛽1 𝑅𝑡−1 + 𝜀𝑡 (6)
𝜀𝑡 ~ 𝑁(0, ℎ 𝑡) (7)
ℎ 𝑡 = 𝑎0 + 𝑎1 𝜀𝑡−1
2
+ 𝑎2ℎ 𝑡−1 + 𝛾1 𝐷𝑓 + 𝛾2 𝐷𝑟 (8)
In equation 6 , Rt is the daily return calculated as in the Equation5, and the lag
return, Rt−1is a proxy for th e mean of Rt conditional on previous information. Concerning
the conditional variance Equation (8, it has been enriched with two dummy variables in
addition to the ARCH and GARCH terms. First, 𝐷𝑓 is added to determine whether the
introduction of the currency futures has an impact on the volatility of the cash market. It
takes value zero for the pre-futures period and one for the post-futures period. Second, the
dummy 𝐷𝑟 is augmented in order to capture the effect of fixed exchange rate regime on the
volatility. As mentioned in the first section, before 26 February 2001, Turkish foreign
currency market was governed with a fixed exchange rate regime. Since the exchange rate
system is controlled by the central bank under this policy, the currency market had
significantly lower volatility during this period. The dummy variable takes value one for
the fixed exchange rate regime period and zero for the floating exchange rate regime
period. Furthermore, change in exchange rate system embodies a shock to currency
markets. During the time before and shortly after the change, central banks and currency
traders respond differently than they do at a normal period. Thus, the exchange rate series
comprises many outliers and has different statistical characteristics during this period.
Essentially, the observations do not come from the usual data generating procedure.
Therefore, the data for one month before the change of regime and one month after are
omitted from the analysis.8
Consequently, it is expected that the analysis should not
comprise any bias due to unsteady times.9
8 Before and after the currency regime change, the return of the currency basket exhibits results below
and above three standard deviations for 13 times within two months.
9 (Kocenda & Valachy, 2006)

18
Table 4 - GARCH (1,1) estimates without the crisis dummy
𝛽0 𝜷 𝟏 𝑎0 𝒂 𝟏 𝑎2 𝜸 𝟏 𝛾2
0.064 0.092 -3.560 0.173 0.816 -0.642 -4.250
(0.044) (0.019) (0.241) (0.020) (0.015) (0.262) (0.242)
All the coefficients in the conditional variance equation which are tabulated in the
Table 4 with their standard deviations, are significant at the 1% level. According to the
analysis’ findings, thanks to the significant dummy variable 𝛾1, the volatility of the
currency market might have been affected by the introduction of currency futures trading.
Moreover, by considering the negative coefficient of the dummy variable which evaluates
the effect due to the onset of futures trading, it is suggested that the introduction of currency
futures yields a significant decrease in the underlying cash market volatility. The findings
of the analysis are in line that of (Oduncu, 2011)who studied the period between 2002-
2008. Finally, the coefficient of the exchange rate regime dummy is negative and quite
high, which suggests that with a strong probability, the fixed exchange rate system may be
responsible of the low volatility until the period of regime change, as expected before.
In the second part of the analysis, the effect of the 2008 global financial crisis is
considered while studying the effect of the introduction of currency futures trading on the
underlying cash market of Turkey. According to (Coudert, Couharde, & Mignon, 2011)
the volatility of exchange rates in most of the emerging countries tends to increase more
than proportionally with the global financial stress. Therefore, to address the issue
mentioned before, regarding the 2008 crisis effects, the analysis of currency returns is
attuned for exposition to the global crisis factor which possibly affect the cash market
volatility in Turkey. The modification is obtained by including a dummy variable in the
conditional variance equation. The crisis dummy 𝐷𝑐 takes value one for years 2008 and
2009, and zero for the rest. Thus, the estimated model evolves as follows:

19
𝑅𝑡 = 𝛽0 + 𝛽1 𝑅𝑡−1 + 𝜀𝑡 (9)
𝜀𝑡 ~ 𝑁(0, ℎ 𝑡) (10)
ℎ 𝑡 = 𝑎0 + 𝑎1 𝜀𝑡−1
2
+ 𝑎2ℎ 𝑡−1 + 𝛾1 𝐷𝑓 + 𝛾2 𝐷𝑟 + 𝛾3 𝐷𝑐 (11)
According to the Table 5, coefficients in the conditional variance equation are
significant at the 1% level. The negative coefficient on the crisis dummy suggests that the
crisis between 2008 and 2009 may be responsible of the increased market volatility. This
result is in line with the finding of (Coudert, Couharde, & Mignon, 2011) since the global
financial crisis resulted in excessive noise in the currency market. Moreover, considering
the crisis effect regarding the present study, adding the crisis dummy in the analysis yielded
a significantly lower futures dummy coefficient, meaning that the positive effect of the
introduction of currency futures on the cash market volatility may be higher.
Table 5 - GARCH (1,1) estimates with the crisis dummy
β0 𝛃 𝟏 a0 𝐚 𝟏 a2 𝛄 𝟏 γ2 𝛄 𝟑
Whole period
0.064 0.092 -3.490 0.170 0.822 -0.866 -4.240 0.704
(0.004) (0.019) (0.234) (0.020) (0.015) (0.293) (0.339) (0.312)
Pre-futures period
0.073 0.122 -3.093 0.151 0.832 -4.299
(0.004) (0.026) (0.259) (0.029) (0.022) (-0.296)
Post-futures period
-0.340 0.025 -4.516 0.221 0.765 0.801
(0.134) (0.272) (0.331) (0.034) (0.026) (0.326)
The final part of the analysis is to discover whether the existence of currency futures
affect the spot market’s efficiency. Two sub-periods of the GARCH (1,1) models are
estimated as pre and post-futures in order to check how the estimates of the coefficients
change regarding the ones related with currency market’s efficiency. The model is the same
before with the exclusion of futures dummy variable, γ1. The results are tabulated at the

20
Table 5. The coefficients in the conditional variance equations of both sub periods are
significant at the 1% level. In order to analyze the effect of currency futures on the cash
market’s efficiency, the coefficients of both of the sub-periods should be analyzed further.
In their seminal paper (Antoniou & Holmes, 1995) suggested that a1 could be interpreted
as “recent news” since the lagged error term a1relates to changes in the spot price on the
previous day. On the other hand, a2 term could be taken as “old news” since a2 is the
coefficient on the lagged variance term which relates the impact of price changes relating
to days prior to the previous day. Regarding the Turkish currency futures experience, Table
5 shows that a1 increased in the post-futures which suggests that impact of recent incoming
news increased with the onset of currency futures. Whereas, a2 is decreased slightly which
should be an evidence for that after the presence of futures trading `old news’ have less
impact in determining the volatility of the cash market. Since the speed of cash price
adjustment to new information is central to market efficiency it would be fair to comment
that the currency futures activity increased the spot market efficiency. In addition, the
reduction of the coefficient β1 after the onset of futures trading is another signal of
increased market efficiency. Since the autoregressive term decreased considerably,
forecasting the return of the currency prices by relying on the lagged return, and so past
information, has become significantly difficult.
Before finishing this chapter, it would be appropriate to diagnostic check the
estimated GARCH (1,1) models. Table 6 presents Ljung-Box (Q) test and Engle’s ARCH
test (LM) results for all models estimated in this study. The Q-tests up to five lags show
that all the models capture the conditional dependence in returns10
. The LM-test statistics
for all the periods are insignificant means that any further ARCH effect does not exist. In
conclusion, it would be fair to comment that the residuals of the models are reasonably
well behaved.
10 Except the Q test scores with two lags.

21
Table 6 - Diagnostic Checking
Models Q(1) Q(2) Q(3) Q(4) Q(5) LM(5)
Whole period
with crisis
dummy
2.040
(0.15)
5.523
(0.06)
6.163
(0.10)
6.175
(0.18)
9.365
(0.12)
2.036
(0.13)
Whole period
without crisis
dummy
2.381
(0.12)
6.253
(0.05)
6.882
(0.07)
6.903
(014)
9.903
(0.07)
2.376
(0.12)
Pre-futures
with/without
crisis dummy
6.104
(0.05)
14.926
(0.00)
15.234
(0.09)
16.233
(0.10)
16.666
(0.14)
4.093
(0.05)
Post-futures
with crisis
dummy
5.545
(0.06)
9.564
(0.00)
11.014
(0.04)
11.161
(0.14)
11.466
(0.10)
7.081
(0.01)

22
CONCLUSIONS
In this study the GARCH (1,1) technique was used in order to analyze the relationship
between currency futures and underlying cash market volatility in the Turkish market. The
first analysis’ result show that the introduction of currency futures trading decrease the
underlying spot market volatility. This result is in line with the previous study made by
(Oduncu, 2011) whose analysis investigates a six-year period. The recent study extends the
data set to a twelve-year period which also involves the period of fixed exchange rate
regime.
The second analysis incorporated the effects of 2008 global crisis. The evidences showed
that Turkey had been affected severely from the global crisis. Therefore, necessary
modification was made on the GARCH (1,1) to determine the impact of the global crisis
while studying the effect of currency trading introduction on the spot market. It was found
that during the period between 2008-2009, the crisis caused to an increase in the cash
market volatility. Furthermore, controlling the global crisis effects on the Turkish currency
market yielded the introduction of currency futures to have a higher stabilizing effect on
the volatility of the spot market.
Finally, it was argued that the existence of currency futures increased the Turkish currency
market efficiency. In contrast to old news, the impact of recent incoming news increased
thanks to the introduction of currency futures. Therefore, the speed of cash price adjustment
to the recent information was increased.

23
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