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Does credit default swap spread affect the value of the Turkish LIRA against the U.S. dollar?
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Borsa _Istanbul Review _ Borsa Istanbul Review xx (2016) 1e9
http://www.elsevier.com/journals/borsa-istanbul-review/2214-8450
Full Length Article
Does credit default swap spread affect the value of the Turkish LIRA against the U.S. dollar? M. Kabir Hassan a,*, Selim Kayhan a,1, Tayfur Bayat b a
Department of Economics and Finance, University of New Orleans, 2000 Lakeshore Drive, 70148, New Orleans, LA, United States b Department of Economics, Inonu University, Merkez Kampus, 44280, Malatya, Turkey Received 4 June 2016; revised 22 July 2016; accepted 16 October 2016 Available online ▪ ▪ ▪
Abstract We examine possible links between CDS spreads and the value of the Turkish lira against the U.S. dollar by using the recently developed rolling window causality method as well as the Markov Switching Vector Autoregressive method. Results show that credit default swap premiums drive the value of the Turkish lira against the U.S. dollar in the post crisis period. We conclude that market risk as a part of financial risk has become an important factor in determining exchange rate fluctuations in the Turkish economy during the post-crisis period. _ Copyright © 2016, Borsa Istanbul Anonim S¸irketi. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NCND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). JEL classification: F31; G10 Keywords: CDS premium; MS-VAR; Rolling window causality; Exchange rate
1. Introduction The driving forces of exchange rate fluctuations have been highly debated among economists. In an early study by Dornbusch et al. (1980) and Branson (1981), the dynamics of exchange rates were attributed to monetary factors. In later studies, real macroeconomic variables (Pindyck & Rotemberg, 1990; Bergstrand, 1991; Faruqee, 1995; Clarida and Gali, 1994; Mark & Choi, 1997; Chinn, 2006) as well as resource endowments, changes in terms of trade, and productivity differentials relative to a country's trading partners (Zalduendo, 2006) were employed to explain exchange rate fluctuations. Commodity prices such as oil and gold were also the subject
* Corresponding author. E-mail addresses: [email protected], [email protected] (M.K. Hassan), [email protected] (S. Kayhan), [email protected] (T. Bayat). _ Peer review under responsibility of Borsa Istanbul Anonim S¸irketi. 1 Permanent address: Department of Economics, Necmettin Erbakan University, Meram Kampus, 42060, Konya, Turkey.
of research investigating exchange rate fluctuations (Bayat, Nazlioglu, & Kayhan, 2015; Golub, 1983; Krugman, 1980). Finance theory suggests that the price of a financial asset depends on its risk. The currency of a country is no exception as it is akin to a financial asset (Zhang, Yau, & Fung, 2010: 440). In this regard, economists claim that “risk factor” can be considered an important determinant of exchange rate volatility. Early studies by Eichengreen and Hausmann (1999), Eichengreen, Rose, Wyplosz, Dumas, & Weber (1995), and Obstfeld and Rogoff (2001) sought to determine the role of risk factor in exchange rate volatility. It is possible to explain the effect of risk factor on a currency via the notion of stability. The strength of a currency is positively related to its economic-political stability. Increased country risk due to economic-political instability will lead investors to sell securities denominated in the country's currency and to repatriate funds, hence putting downward pressure on the currency (Hui & Chung, 2011: 2945). In this respect, an increase in country risk would induce depreciation of the national currency. As a result, the volatility of a currency depends highly on the level of risk found in an economy and/or financial system.
http://dx.doi.org/10.1016/j.bir.2016.10.002 _ 2214-8450/Copyright © 2016, Borsa Istanbul Anonim S¸irketi. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Please cite this article in press as: Hassan, M. K., et al., Does credit default swap spread affect the value of the Turkish LIRA against the U.S. dollar?, Borsa _ Istanbul Review (2016), http://dx.doi.org/10.1016/j.bir.2016.10.002
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Thus further debate on measuring the risk of an economy and/or financial system began among economists. Financial risk is usually broken into three categories (Gu¨nay, 2016: 21): market risk, credit risk, and liquidity risk. It is possible to add political risk into these risk categories. Previous studies have employed various indicators to measure different types of financial risk categories, but recently a new one has enjoyed increasing popularity because of its rising market size: credit default swaps (CDS, hereafter). CDS is an over-the-counter credit protection contract in which a protection seller pays compensation to a protection buyer to make a payment in the case of a pre-defined credit event. For credit protection buyers who pay a fixed premium called CDS spread, the CDS market offers the opportunity to reduce credit risk (Hui & Fong, 2015, 174). The protection seller would have the opportunity to earn income without having to fund the position. In other words, a CDS is a swap contract in which the contract buyer pays a series of payments to a seller in exchange for protection from default in the reference entity (Yang, Morley, & Hudson, 2010: 2). The CDS market has grown over the last decade and has thus become more prominent in finance literature (Galil, Shapir, Amiram, & Ben-Zion, 2014: 271). The market for CDS has ballooned from 180 billion U.S. dollars in a notional amount in 1996 to over 54.6 trillion U.S. dollars as of the second quarter of 2008 (Hassan, Ngow, & Yu, 2011: 2). The growth of the CDS market makes it a useful tool to reflect the situation in financial markets. A change in the credit risk of a sovereign borrower reflected in its sovereign CDS spread can thus be considered an indicator of the country's economicpolitical stability, which is linked to country-specific macroeconomic variables such as output growth, foreign exchange reserves, budget deficit, real effective exchange rate deviation, and foreign direct investment (Hui & Fong, 2015: 174). Moreover, the changes in credit risk premiums of sovereign markets, which translate into changes in sovereign CDS spreads, do not emanate from changes in the fundamentals of the underlying economies (Hassan, Hassan, & NgowYu, 2013; Ngene et al., 2014). Rather, these variations mirror a change in the risk appetite of market participants in terms of credit exposure. A negative change in the creditworthiness of a sovereign country inevitably translates into currency depreciation along with soaring currency volatility (Bekkour, Jin, Lehnert, Rasmouki, & Wolff, 2015: 68). An increase in CDS premium, which means that the risk of the country increases, would lead investors to sell securities denominated in the country's currency and to repatriate funds, hence putting downward pressure on the currency as indicated by Hui and Chung (2011: 2945) or vice versa. The global finance crisis occurred in 2008, yet its effects on the global economy are still visible. One outcome of the crisis is that investors' risk appetite has changed. Investors increasingly ask for low-risk investments even if they are low-yield. Thus investments are more risk sensitive. Theoretically, the response of an investor to a positive change in country risk would not be the same as the response to a negative change. Investors would be diffident and skeptical as risk decreases
and react slowly to the new condition. In this case, the national currency would appreciate against any other currency only in the longer time period. In the case of an increase in risk, investors would react rapidly and the national currency would sharply depreciate in a very short time period. Business cycles are another factor that impacts the risk appetite of investors. In contraction periods, investors look for safe havens and are more sensitive to risk. On the other hand, investors increase their risk appetite and become more willing to invest during expansion periods. In short, the efficiency of the mechanism explaining the interaction between CDS spread and exchange rate depends on business cycles. If the economy is in a contraction period, an investor may follow a change in CDS premiums more curiously. On the other hand, he/she may not be so curious about it in expansion periods. In expansion periods, when we take international capital flows into account for an emerging economy such as Turkey, a change in CDS premium would not affect the value of the currency via capital inflow or outflow in a floating exchange rate regime. On the other hand, in contraction periods, a change in CDS premium would affect the value of the currency via capital movement more than the case of expansionary period. Several studies confirm the existence of a linkage between sovereign default risk and exchange rate. The initial study belonging to Carr and Wu (2007) investigates the Brazil and Mexico economies and implies the causation linkage from CDS spreads to currency option market for both of them. Recent studies mainly focus on the Eurozone after the crisis to understand the nature of the euro and to find the role of increasing financial risk in the monetary union. The studies of Hui and Chung (2011) and Bekkour et al. (2015) investigate similar periods covering crisis times. While Bekkour et al. (2015) find the effect of CDS premiums on the euro during the financial crisis, Hui and Chung (2011) relate the effect of CDS premiums to fiscal conditions of Eurozone countries. Different from initiative studies, Omachel and Rudolf (2014) investigate the case of the euro in the post-crisis period and find weak causation linkages between them. Hui and Fong (2015) examine the Eurozone, the U.S., Japan and Switzerland, and Zhang et al. (2010) examine Japan, Eurozone and the United Kingdom. Both studies conclude that CDS premiums have an effect on exchange rates even if it is contemporaneous or via expectations. In a recent study, Della Corte, Sarno, Schmeling, and Wagner (2015) present a broader analysis for the effect of CDS spreads on the value of national currencies in twenty developed and emerging economies over a long time horizon. They find a linkage between sovereign risk and currency option as well as spot currency value in both developed and emerging economies. The global financial crisis, the European debt crisis, changes in the monetary policies of developed economies, and country/region-specific economic and political instability have induced depreciation of the value of currencies of emerging market economies. Like many other emerging market economies, the volatility of the Turkish lira has increased during the recent crisis period. The Turkish lira and Brazilian real have
Please cite this article in press as: Hassan, M. K., et al., Does credit default swap spread affect the value of the Turkish LIRA against the U.S. dollar?, Borsa _ Istanbul Review (2016), http://dx.doi.org/10.1016/j.bir.2016.10.002
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depreciated the most among fragile quintet currencies. As can be seen in the figure below, the depreciation of the value of the Turkish lira against the U.S. dollar is high during the period after the global financial crisis. The nominal exchange rate increased 102% from September 2009 to September 2015. The high depreciation ratio of the Turkish lira compared to other emerging currencies, in the presence of country risk, has made the Turkish lira important to analyze (Fig. 1). In this study, we aim to analyze the Turkish economy to understand the interaction between financial risk and exchange rate volatility in order to understand how changes in financial risk affect national currency in various types of shock and business cycle periods. To measure the risk of a financial system, we employ CDS spreads as shown by Pan and Singleton (2008). We use the MS VAR method in order to see the interaction between variables in different regimes. By doing so, we will be able to better understand the behavior of the value of the Turkish lira against the U.S. dollar rate in those various shock types and business cycle periods. We also use the rolling windows causality test methods developed by Balcılar and Ozdemir (2013) in order to see the exact dates when the causality between exchange rate and CDS occurs. The empirical results obtained from our analyses might be useful in understanding whether we can use CDS premium changes to predict exchange rate fluctuations in terms of U.S. dollar/Turkish lira. More generally, our results may have implications for the predictability of an economy's exchange rate risk through the use of information in the sovereign CDS market and the effects of the monetary policies adopted by central banks on the currency and sovereign CDS markets. Unlike existing studies, we employ recently developed causality methods and investigate the relation between CDS premiums and exchange rates in different risk movements and in different regimes. By doing so, we will be able to better understand the nature of exchange rate behavior. In the following section, we explain our econometric methods. In the third section, we introduce the data employed
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in our model. We present the empirical results obtained from the empirical analyses in the fourth section. Finally, we interpret our empirical results and offer conclusions in the last section. 2. Methodology In this section, we introduce the empirical methods used. First we describe the Markov Switching Vector Autoregressive (MS VAR) method that is used to obtain impulse-response functions for each regime to see possible differences in the behavior of the exchange rate due to changes in the business cycle. Secondly, we describe the rolling windows causality analysis developed by Balcılar and Ozdemir (2013) to determine the time table of the causal relationship between variables. 2.1. Markov Switching VAR methodology As an empirical methodology, Markov Switching Vector Autoregressive Model (MS-VAR) is applied to estimations of interaction between variables in different regimes of business cycles such as expansion, contraction, boom and recession. As the financial sector is quite sensitive to fluctuations in the economy, crises affect both CDS premiums and the value of national currency against foreign currencies. Hence, MS-VAR is a good tool for monitoring asymmetric behaviors in the historical process and has the advantage of accommodating structural changes across regimes, both with respect to autoregressive dynamics and to the covariance structure of the shocks (Binder & Gross, 2013). In this respect, to measure the effect of a CDS premium change on the value of national currency in different regimes, we employ the MS-VAR method and investigate the asymmetric behavior of interactions between variables. By employing the method, we may better understand the relation between CDS premium and the value of the Turkish lira
Fig. 1. Volatility in nominal exchange rate and CDS premiums. It shows volatility in CDS premium difference between Turkey and U.S. and nominal exchange rate after the global crisis period. Time period extends from September 2009 to September 2015. Right axis shows % change in nominal exchange rate compared to previous month. Left axis shows CDS premium difference. Please cite this article in press as: Hassan, M. K., et al., Does credit default swap spread affect the value of the Turkish LIRA against the U.S. dollar?, Borsa _ Istanbul Review (2016), http://dx.doi.org/10.1016/j.bir.2016.10.002
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against the U.S. dollar and determine whether or not the changes are due to business cycles. In conventional models, it is not possible to know which regime is effective at any point in time. However, in an MSVAR method, probabilities of all regimes for any point in time are all known. The MS-VAR method was originally developed by Hamilton (1989) and used by Hamilton (1989, 1990, 1994, 1996), Kim and Nelson (1998), and Krolzig (1997, 1998, 2000, 2001) for the empirical analysis of business cycles. Hamilton’s (1989) 2-regimes Markov Switching Intercept Autoregressive Heteroscedasticity MSIAH-AR (p) model takes the following form: f1;0 þ f1;1 yt1 þ / þ f1;p ytp þ A1 εt if ðst ¼ 1Þ yt ¼ f2;0 þ f2;1 yt1 þ / þ f2;p ytp þ A2 εt if ðst ¼ 2Þ ð1Þ where f1;j and f2;j denote autoregressive lag parameters for every regime; st is the value of each regime; p shows the degree of the autoregressive process; and εit is a sequence of independent and identically distributed random variables with mean zero [εit is iidð0; s2i Þ and s2i < ∞] (Mohd and Zahid 2006; Fallahi and Rogriguez, 2007; Kayhan, Bayat, & Kocyigit, 2013). However, as each fundamental residual is pre-multiplied by a switching matrix, Ai εt , the var-cov matrix Si of the structural disturbances in Ai εt is regime-dependent as indicated by the following transformation: Si ¼ E Ai ut u0t A0i ¼ Ai E ut u0t A0i ¼ Ai I2 A0i ¼ Ai A0i ð2Þ The main characteristic of MS-VAR is that the dynamics of the variables are conditioned on the unobserved Markov process followed under the regime because the Markov chain is unobservable. After the identification of coefficients belonging to each regime, impulse response functions are obtained by employing the generalized impulse response function process. 2.2. Balcılar and Ozdemir (2013) bootstrap rolling window causality test Empirical studies that examine the causation linkage between variables may suffer from inaccurate findings from fullsample time series data when the data series experiences structural changes. Structural changes may create shifts in the parameters, and the pattern of the causal relationship may change over time (Balcılar and Ozdemir, 2013: 1400). As mentioned before, there might be asymmetries in the behavior of investors in addition to the business cycles. There might be structural changes in causation linkage between CDS premiums and the value of the national currency. In order to deal with structural changes and parameter non-constancy, we employ the bootstrap rolling window causality test developed by Balcılar and Ozdemir (2013). Balcılar and Ozdemir (2013) ran a LR (likelihood ratio) causality test using a bootstrap method that depends on the error term. The LR Granger causality test depending on bootstrap has two variables VAR (p) in the model, t ¼ 1, 2, …, T;
yt ¼ F0 þ F1 yt1 þ / þ Fp ytp þ εt
ð3Þ
In the equationP above, εt ¼ ðε1 ; ε2 Þ is iidð0; s Þ is a covariance matrix that is not odd. An optimal lag length criterion is defined by Akaike Information Criteria (AIC). While yt ¼ ½y1t ; y2t 2x1 is considered a matrix, the VAR (p) model will be shown as f f ðLÞ f12 ðLÞ y1t ε y1t ¼ 10 þ 11 þ 1t ð4Þ f21 ðLÞ f22 ðLÞ y2t y2t f20 ε2t P where fij ðLÞ ¼ pk¼1 fij ; kLk , i, j ¼ 1, 2; k is the lag operator; and Lk xt ¼ xtk . In order to avoid possible structural unit root and to overcome any problems related to the size of the sample, Balcılar and Ozdemir (2013) used the bootstrap test that was modified by Koutris, Heracleous, and Spanos (2008) and Shukur and Mantalos (2000). See the details for this process in Balcılar and Ozdemir (2013). 2
3. Data description Before describing the data and its sources, it might be useful to show the volatility of the Turkish lira and the movement of the CDS spread in the Turkish economy. As can be seen in Graph 1, the value of the Turkish lira against U.S. dollar rate is volatile during the whole period. In 2012, it is quite stable. On the other hand, CDS spread has an increasing trend. In the light of graphical presentation, it is possible to imply co-movement with exchange rate volatility. For comparison, we use the perspective of USD-based investors in our analysis. We obtain monthly data belonging to five year sovereign CDS spreads of Turkey and the U.S. from September 2009 to October 2015. As exchange rates reflect the rate of exchange between the two economies' currencies, CDS spreads are thus expressed as the differences between the CDS spreads of the Turkish and U.S. economies (the CDS spread of the Turkish economy minus the CDS spread of the U.S. economy; hereafter, CDS difference). We use monthly U.S. dollar/Turkish lira nominal exchange rate data to measure the value of the Turkish lira. According to Pan and Singleton (2008), CDS spreads are related to the investors' risk appetite associated with global event risk, financial market volatility, and macroeconomic policy (Hui & Fong, 2015: 184). Therefore, it is important to identify whether the sovereign CDS spreads and risk reversals of the U.S. and Turkish economies in this study remain cointegrated in the presence of other macro-financial factors. In order to solve this problem, we include a set of macrofinancial variables as control variables. These are macrofinancial condition variables (S&P 500 and BIST 100 indexes), interest rate difference, inflation rate difference, and global risk appetite in the S&P stock market (VIX index). The U.S. stock market index, proxied by the S&P 500, and the Turkish stock market index, proxied by the BIST 100, are used as macroeconomic control variables. To account for unusual turbulence in the stock market following the implosion of the U.S. subprime market, we include the implied volatility of the S&P 500 index option (VIX) as an additional
Please cite this article in press as: Hassan, M. K., et al., Does credit default swap spread affect the value of the Turkish LIRA against the U.S. dollar?, Borsa _ Istanbul Review (2016), http://dx.doi.org/10.1016/j.bir.2016.10.002
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explanatory variable (Zhang et al., 2010: 448). Interest rate difference data (policy interest rate of the Turkish economy minus the U.S. policy interest rate) is used as a control variable in order to see the effects of possible increases in interest rate differential on CDS spreads. Similarly, we include inflation rate difference data (inflation rate in the Turkish economy minus inflation rate in the U.S. economy) as another macroeconomic control variable in the model. The Bloomberg database is the source of the monthly data belonging to 5 year CDS spreads for the Turkish and U.S. economy, BIST 100 index, S&P 500 index, VIX index, and USD/TL nominal exchange rate. Policy interest rate and inflation rate data for both economies are obtained from the International Financial Statistics database published by the International Monetary Fund. 4. Analysis of empirical results One important issue to consider in MS VAR and Granger type causality analyses is testing the stationarity of variables. With this aim, we employ the unit root test developed by Dickey and Fuller (1979, 1981) (hereafter ADF). According to our results, BIST 100 index (hereafter, BIST100), S&P 500 index (hereafter, SP500), nominal exchange rate data (hereafter, NEER), CDS spreads difference (hereafter, CDSDIF), interest rate difference (hereafter, INTDIF), VIX index (hereafter, VIX) and inflation rate difference (hereafter, INFDIF) include unit root in level, and the first differences of the variables are stationary. Thus the first difference of each variable must be used in the analysis. Moreover, we include trend and seasonality as external dummies in our model to get more robust results (Table 1). The first step of MS VAR analysis is to determine the number of regimes. In Table 2, LR (rate of probability) and Davies2 test statistics show that all regimes have a non-linear and asymmetric structure. According to the test statistics, the model with two regimes has the smallest SC and AIC statistics. It also has the biggest rate of LR. Thus, the optimal lag length is two according to the Schwarz Information Criteria. In light of this finding, the transition probability matrices which are obtained by using the MSIA(2)-VAR(2) model are presented in Table 3. According to regime transition probability results, when the economy is in the first regime, the probability of remaining in the same regime is 74%, while the probability of transitioning to the second regime is 26%. On the other hand, if the economy is in the second regime, the probability of remaining in the same regime is 77%, while the probability of transitioning to the first regime is 23%. According to the regime analysis results, the length of regime one is 0.96 years and 1.09 years for the second regime. The speed of entrance to the second regime from the first regime is 0.32 years, while the speed of entrance to the first regime from the second one is 0.33 years (Fig. 2).
2 For detailed information about Davies asymmetry test, please see Davies (1977, 1987) and Garcia and Perron (1996).
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Table 1 ADF unit root test results. Variables
Level Constant
1.986 (0) [0.291] CDS DIF 1.471 (0) [0.542] INF DIF 2.241 (0) [0.193] INT DIF 1.757 (0) [0.398] SP500 0.442 (0) [0.893] EXC RATE 2.112 (0) [0.999] VIX 3.214 (0) [0.023**]
BIST100
First difference Constant þ trend Constant
Constant þ trend
2.590 (0) [0.285] 2.156 (0) [0.506] 2.604 (0) [0.279] 2.296 (0) [0.430] 2.837 (0) [0.189] 0.374 (0) [0.986] 4.383 (0) [0.004]***
7.733 (0) [0.00]*** 8.283 (0) [0.00]*** 7.302 (0) [0.00] *** 7.266 (0) [0.00]*** 9.623 (0) [0.00]*** 7.020 (0) [0.00]*** 9.772 (0) [0.00]***
7.778 (0) [0.00]*** 8.211 (0) [0.00]*** 7.366 (0) [0.00]*** 7.252 (0) [0.00]*** 9.701 (0) [0.00]*** 6.685 (0) [0.00]*** 9.846 (0) [0.00]***
It shows the unit root test results. The null hypothesis claims that series contain unit root is tested in level and first difference with a constant and constant and trend models separately. Results for each variable includes test statistics, lag length in parentheses and probability values in brackets. If the test statistics are lower than critical values, the null hypothesis is rejected and there is no unit root. *, ** and *** show significance levels 10%, 5% and 1%, respectively.
Table 2 Test statistics and regime determinants for VAR(2) model. No. of regime
Log prob
LR linearity
Davies
AIC
SC
MS(2) MS(3) MS(4)
14.6057 1312.2906 997.0867
274.2465 478.6674 1109.0751
0.00 0.00 0.00
41.6057 46.7969 41.0447
54.2542 57.9191 55.7043
It represents the test statistics employed to determine the number of regime in VAR (2) model. The lowest Akaike Information Criteria (AIC), Schwartz Information Criteria (SC) and highest LR linearity statistics denote the right regime number for the model.
The impulse-response analysis results for the first regime are presented in Fig. 3. According to our results, a 1% positive shock in CDS difference would induce a positive shock in the exchange rate and continue for three months. The response of the exchange rate is quite high and significant both statistically and theoretically. An increase in CDS difference means that risk in the Turkish financial system increases relative to the U.S. Thus, the price of the U.S. dollar will increase in terms of Turkish lira. On the other hand, the response of the exchange rate to a positive shock in the BIST is negative and statistically
Table 3 Regime transition probability matrices.
Regime 1 Regime 2
Regime 1
Regime 2
0.7408 0.2273
0.2592 0.7727
It represents regime transition probability matrices. The value of cell in upper left corner of the matrices is probability of continue to stay in the first regime after a period in the same regime. The second cell in the first row indicates the probability of transition to second regime from first regime. In the second row, first cell implies the probability of transition to first regime from second regime and bottom right corner of the matrices is probability of continue to stay in the second regime after a period in the same regime.
Please cite this article in press as: Hassan, M. K., et al., Does credit default swap spread affect the value of the Turkish LIRA against the U.S. dollar?, Borsa _ Istanbul Review (2016), http://dx.doi.org/10.1016/j.bir.2016.10.002
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MSIA(2)-VAR(2), 5 - 73 0
D BIST DEXC D INTD D VIX
D CDS D INFD D SP 500
-10000
1.0
10 20 Probabilities of Regime 1 filtered predicted
30
40
50
60
70
30
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70
30
40
50
60
70
smoothed
0.5
1.0
10 20 Probabilities of Regime 2 filtered predicted
smoothed
0.5
10
20
Fig. 2. Smoothed avg posterior probability of regime 1 and 2. It shows the probability of regime 1 and 2 in the whole period taken into consideration. In the second figure, blue columns show that economy is in the first regime, vice versa. Red line in figures shows smoothed probability value, while blue line shows predicted probability.
Fig. 3. Response of EXC to 1% positive shock in other variables in regime 1. It represents the response of Turkish lira/U.S. dollar nominal exchange rate to shocks each variable listed in data section in the first regime. The vertical axis indicates the size of response of each variable, while the horizontal axis shows the monthly time period. Blue line symbolizes the response of nominal foreign exchange rate. If the blue line is under the horizontal axis, this means the response of nominal exchange rate to a positive shock in any variable is negative. Please cite this article in press as: Hassan, M. K., et al., Does credit default swap spread affect the value of the Turkish LIRA against the U.S. dollar?, Borsa _ Istanbul Review (2016), http://dx.doi.org/10.1016/j.bir.2016.10.002
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significant. An increase in the BIST can be interpreted as a capital inflow into the financial system and would appreciate the national currency. Conversely, the response of the exchange rate to a positive shock in the SP500 is positive and statistically significant. It is possible to imply that an increase in the S&P 500 index is a capital outflow from the Turkish financial system into the U.S. financial system. The response of the exchange rate to a positive shock in interest rate difference, inflation rate difference, and VIX variables are negative and statistically insignificant. This means that these variables do not affect the exchange rate in the first regime. The impulse-response analysis results for the second regime are presented in Fig. 4. According to our results, the response of the exchange rate to a positive shock in CDS difference is positive and statistically significant for two months. The response of the exchange rate is low relative to the first regime. Similarly, the response of the exchange rate to a positive shock in the BIST is negative and significant. Conversely, the response of the exchange rate to a positive shock in the S&P 500 index is insignificant. The impact of interest rate difference on exchange rate is statistically significant in the second period. An increase in the difference
between interest rates in the Turkish economy and U.S. economy induces appreciation in the Turkish lira. This is also significant theoretically. On the other hand, VIX variable and inflation difference variable do not affect exchange rate either. According to the rolling windows causality analysis results, causality running from CDS difference to nominal exchange rate occurs in October 2011 and January, February, March, April, May, June, September, and October of 2012. On the other hand, the causation linkage from nominal exchange rate to CDS differential occurs in December 2010; January, February, March, and April of 2011; May, July, and October of 2013; January, February, and October of 2014; and February and March of 2015. When we take a look at Fig. 5, it is possible to easily see the causality running from CDS difference to exchange rate in 2012. As is evident, the volatility of exchange rate is low and during the same period the CDS difference between the U.S. and Turkish economies decreases and remains stably low. When we compare all our results with the existing literature, it is possible to conclude that our results are in line with the literature investigating the behavior of national currencies
CDS
BIST 100 1
0.08
0
0.06
1
-1 0.04
2
3
4
7
8
9
10
7
8
9
10
-4 1
2
3
4
5
6
7
8
9
-5
10
-0.02
-6
INFD
INTD
0.2 0.1 0 1
2
3
4
5
6
7
8
9
10
-0.2 -0.3 -0.4 -0.5
0.1 0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7
1
2
3
4
SP500
5
6
VIX 0.25
0.4
0.2
0.3
0.15
0.2
0.1
0.1
0.05
0
-0.2
6
-3
0
-0.1
5
-2
0.02
-0.1
7
1
2
3
4
5
6
7
8
9
10
0 -0.05
1
2
3
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5
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Fig. 4. Response of EXC to 1% positive shock in other variables in regime 2. It represents the response of Turkish lira/U.S. dollar nominal exchange rate to shocks each variable listed in data section in the second regime. The vertical axis indicates the size of response of each variable, while the horizontal axis shows the monthly time period. Blue line symbolizes the response of nominal foreign exchange rate. If the blue line is under the horizontal axis, this means the response of nominal exchange rate to a positive shock in any variable is negative. Please cite this article in press as: Hassan, M. K., et al., Does credit default swap spread affect the value of the Turkish LIRA against the U.S. dollar?, Borsa _ Istanbul Review (2016), http://dx.doi.org/10.1016/j.bir.2016.10.002
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10% CV
CDSDIF≠>NEER
NEER≠CDSDIF
Fig. 5. Balcılar and Ozdemir (2013) bootstrap rolling window causality results. It represents the timeline of existency of causation linkage between variables. While green line represent the uni-directional causality running from foreign nominal exchange rate to CDS diffierence, red line symbolizes uni-directional causality running from cds difference to nominal foreign exchange rate. Blue line represents 10% confidence interval. When red and/or green line is under blue line, the causation linkage exists variables even in 10% significance level.
against the U.S. dollar. The findings of our study and the results of Carr and Wu (2007), Zhang et al. (2010), and Della Corte et al. (2015) resemble one another. 5. Conclusion This study determines the nature of causal relations between CDS spreads and the value of the national currency against the U.S. dollar in the Turkish economy. A novel approach to the Markov Switching VAR method, which investigates this relation in multiple periods, is applied to the Turkish economy during the period 2009e2015. Furthermore, we carry out the rolling window causality approach. The method is useful for determining the exact date that causality occurs. The rolling window causality analysis results emphasize the causal relationship from CDS spreads to the value of Turkish lira against the U.S. dollar, especially in 2012 when exchange rate volatility was relatively low and CDS spreads remained low. The MS VAR analysis results point out that the Turkish economy experienced two regimes in the period between the years 2009 and 2015. Impulse-response analysis results show that impact of CDS spreads on the value of Turkish lira against U.S. dollar is positive and stronger in the first period relative to the second period. In the second period, interest rate difference is another variable affecting exchange rate. The behavior of investors in expanding regimes might be affected by interest rate differentials rather than any risk factor due to changing risk appetite; this is why we can label the second period an expanding regime. On the other hand, the impact of risk factor on exchange rate in the first regime might be caused by the behavior of investors, for instance low risk appetites. According to our results obtained from all econometric analysis methods, there is a causation linkage running from CDS spread to the nominal foreign exchange rate. The strength of causality differs according to regime. These results imply that observation of CDS spread changes might be useful to predict exchange rate instability. This would help policymakers in stabilizing the value of the Turkish lira against U.S. the dollar.
Balcılar, M., & Ozdemir, Z. A. (2013). The export-output growth nexus in Japan: A bootstrap rolling window approach. Empirical Economics, 44(2), 639e660. Bayat, T., Nazlioglu, S., & Kayhan, S. (2015). Exchange rate and oil price interactions in transition economies: Czech Republic, Hungary and Poland. Panoeconomicus, 62(3), 267e285. Bekkour, L., Jin, X., Lehnert, T., Rasmouki, F., & Wolff, C. (2015). Euro at risk: The impact of member countries' credit risk on the stability of the common currency. Journal of Empirical Finance, 33, 67e83. Bergstrand, J. H. (1991). Structural determinants of real exchange rates and national price levels: Some empirical evidence. The American Economic Review, 81(1), 325e334. Binder, M., & Gross, M. (2013). Regime-switching global vector autoregressive models. ECB Working Paper No. 1569. Branson, W. H. (1981). Macroeconomic determinants of real exchange rates. NBER Working Paper No. 801. Carr, P., & Wu, L. (2007). Theory and evidence on the dynamic interactions between sovereign credit default swaps and currency options. Journal of Banking & Finance, 31(8), 2383e2403. Chinn, M. D. (2006). Real exchange rates. New Palgrave Dictionary. Clarida, R., & Gali, J. (1994, December). Sources of real exchange-rate fluctuations: How important are nominal shocks?. In , Vol. 41. CarnegieRochester conference series on public policy (pp. 1e56). North-Holland. Davies, R. B. (1977). Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika, 4(2), 247e254. Davies, R. B. (1987). Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika, 74(1), 33e43. Della Corte, P., Sarno, L., Schmeling, M., & Wagner, C. (2015). Exchange rates and sovereign risk. Available at SSRN 2354935 http://ssrn.com/ abstract¼2354935. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427e431. Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 1057e1072. Dornbusch, R., Branson, W. H., Kenen, P., Houthakker, H., Hall, R. E., Lawrence, R., et al. (1980). Exchange rate economics: Where do we stand? Brookings Papers on Economic Activity, 1980(1), 143e205. Eichengreen, B., & Hausmann, R. (1999). Exchange rates and financial fragility (No. w7418). National Bureau of Economic Research. Eichengreen, B., Rose, A. K., Wyplosz, C., Dumas, B., & Weber, A. (1995). Exchange market mayhem: The antecedents and aftermath of speculative attacks. Economic Policy, 249e312. Fallahi, F., & Rogriguez, G. (2007). Using Markov-switching models to identify the link between unemployment and criminality. Department of Economics, University of Ottawa¼ Dep. de Science economique, Universite d'Ottawa. Faruqee, H. (1995). Long-run determinants of the real exchange rate: A stockflow perspective. International Money Fund Staff Papers, 42(1), 80e107. Galil, K., Shapir, O. M., Amiram, D., & Ben-Zion, U. (2014). The determinants of CDS spreads. Journal of Banking & Finance, 41, 271e282. Garcia, R., & Perron, P. (1996). An analysis of the real interest rate under regime shifts. The Review of Economics and Statistics, 78(1), 111e125. Golub, S. S. (1983). Oil prices and exchange rates. The Economic Journal, 93(371), 576e593. Gu¨nay, S. (2016). Is political risk still an issue for Turkish stock market? Borsa Istanbul Review, 16(1), 21e31. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica: Journal of the Econometric Society, 357e384. Hamilton, J. D. (1990). Analysis of time series subject to changes in regime. Journal of Econometrics, 45(1), 39e70. Hamilton, J. D. (1994). Time series analysis (Vol. 2). Princeton: Princeton University Press.
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Krugman, P. R. (1980). Oil and the dollar. NBER Working Paper Series No. 554. Mark, N. C., & Choi, D. Y. (1997). Real exchange-rate prediction over long horizons. Journal of International Economics, 43(1), 29e60. Mohd, T. I., & Zahid, I. (2006). Modelling exchange rates using regime switching models. Sains Malaysiana, 35, 55e62. Ngene, G. M., Kabir Hassan, M., & Alam, Nafis (2014). Price discovery process in the emerging sovereign CDS and equity markets. Emerging Markets Review, 21, 117e132. Obstfeld, M., & Rogoff, K. (2001). The six major puzzles in international macroeconomics: Is there a common cause?. In , Vol. 15. NBER macroeconomics annual 2000 (pp. 339e412). MIT Press. Omachel, M., & Rudolf, M. (2014). The relationship between sovereign defaults and exchange rate shocks in the Eurozone: A simple measure for systemic sovereign credit risk. Available at SSRN http://ssrn.com/ abstract¼2513178. Pan, J., & Singleton, K. J. (2008). Default and recovery implicit in the term structure of sovereign CDS spreads. The Journal of Finance, 63(5), 2345e2384. Pindyck, R. S., & Rotemberg, J. J. (1990). The excess co-movement of commodity prices. The Economic Journal, 100(403), 1173e1189. Shukur, G., & Mantalos, P. (2000). A simple investigation of the Grangercausality test in integrated-cointegrated VAR systems. Journal of Applied Statistics, 27(8), 1021e1031. Yang, L., Morley, B., & Hudson, J. (2010). A study of the causal relationships between sovereign CDS spreads, risk-free interest rates and exchange rates. In 8th INFINITI conference on international finance, 14e15 May, Dublin. Zalduendo, M. J. (2006). Determinants of Venezuela's equilibrium real exchange rate (No. 6e74). International Monetary Fund. Zhang, G., Yau, J., & Fung, H. G. (2010). Do credit default swaps predict currency values? Applied Financial Economics, 20(6), 439e458.
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Does credit default swap spread affect the value of the Turkish LIRA against the U.S. dollar? M. Kabir Hassan a,*, Selim Kayhan a,1, Tayfur Bayat b a
Department of Economics and Finance, University of New Orleans, 2000 Lakeshore Drive, 70148, New Orleans, LA, United States b Department of Economics, Inonu University, Merkez Kampus, 44280, Malatya, Turkey Received 4 June 2016; revised 22 July 2016; accepted 16 October 2016 Available online ▪ ▪ ▪
Abstract We examine possible links between CDS spreads and the value of the Turkish lira against the U.S. dollar by using the recently developed rolling window causality method as well as the Markov Switching Vector Autoregressive method. Results show that credit default swap premiums drive the value of the Turkish lira against the U.S. dollar in the post crisis period. We conclude that market risk as a part of financial risk has become an important factor in determining exchange rate fluctuations in the Turkish economy during the post-crisis period. _ Copyright © 2016, Borsa Istanbul Anonim S¸irketi. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NCND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). JEL classification: F31; G10 Keywords: CDS premium; MS-VAR; Rolling window causality; Exchange rate
1. Introduction The driving forces of exchange rate fluctuations have been highly debated among economists. In an early study by Dornbusch et al. (1980) and Branson (1981), the dynamics of exchange rates were attributed to monetary factors. In later studies, real macroeconomic variables (Pindyck & Rotemberg, 1990; Bergstrand, 1991; Faruqee, 1995; Clarida and Gali, 1994; Mark & Choi, 1997; Chinn, 2006) as well as resource endowments, changes in terms of trade, and productivity differentials relative to a country's trading partners (Zalduendo, 2006) were employed to explain exchange rate fluctuations. Commodity prices such as oil and gold were also the subject
* Corresponding author. E-mail addresses: [email protected], [email protected] (M.K. Hassan), [email protected] (S. Kayhan), [email protected] (T. Bayat). _ Peer review under responsibility of Borsa Istanbul Anonim S¸irketi. 1 Permanent address: Department of Economics, Necmettin Erbakan University, Meram Kampus, 42060, Konya, Turkey.
of research investigating exchange rate fluctuations (Bayat, Nazlioglu, & Kayhan, 2015; Golub, 1983; Krugman, 1980). Finance theory suggests that the price of a financial asset depends on its risk. The currency of a country is no exception as it is akin to a financial asset (Zhang, Yau, & Fung, 2010: 440). In this regard, economists claim that “risk factor” can be considered an important determinant of exchange rate volatility. Early studies by Eichengreen and Hausmann (1999), Eichengreen, Rose, Wyplosz, Dumas, & Weber (1995), and Obstfeld and Rogoff (2001) sought to determine the role of risk factor in exchange rate volatility. It is possible to explain the effect of risk factor on a currency via the notion of stability. The strength of a currency is positively related to its economic-political stability. Increased country risk due to economic-political instability will lead investors to sell securities denominated in the country's currency and to repatriate funds, hence putting downward pressure on the currency (Hui & Chung, 2011: 2945). In this respect, an increase in country risk would induce depreciation of the national currency. As a result, the volatility of a currency depends highly on the level of risk found in an economy and/or financial system.
http://dx.doi.org/10.1016/j.bir.2016.10.002 _ 2214-8450/Copyright © 2016, Borsa Istanbul Anonim S¸irketi. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Please cite this article in press as: Hassan, M. K., et al., Does credit default swap spread affect the value of the Turkish LIRA against the U.S. dollar?, Borsa _ Istanbul Review (2016), http://dx.doi.org/10.1016/j.bir.2016.10.002
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Thus further debate on measuring the risk of an economy and/or financial system began among economists. Financial risk is usually broken into three categories (Gu¨nay, 2016: 21): market risk, credit risk, and liquidity risk. It is possible to add political risk into these risk categories. Previous studies have employed various indicators to measure different types of financial risk categories, but recently a new one has enjoyed increasing popularity because of its rising market size: credit default swaps (CDS, hereafter). CDS is an over-the-counter credit protection contract in which a protection seller pays compensation to a protection buyer to make a payment in the case of a pre-defined credit event. For credit protection buyers who pay a fixed premium called CDS spread, the CDS market offers the opportunity to reduce credit risk (Hui & Fong, 2015, 174). The protection seller would have the opportunity to earn income without having to fund the position. In other words, a CDS is a swap contract in which the contract buyer pays a series of payments to a seller in exchange for protection from default in the reference entity (Yang, Morley, & Hudson, 2010: 2). The CDS market has grown over the last decade and has thus become more prominent in finance literature (Galil, Shapir, Amiram, & Ben-Zion, 2014: 271). The market for CDS has ballooned from 180 billion U.S. dollars in a notional amount in 1996 to over 54.6 trillion U.S. dollars as of the second quarter of 2008 (Hassan, Ngow, & Yu, 2011: 2). The growth of the CDS market makes it a useful tool to reflect the situation in financial markets. A change in the credit risk of a sovereign borrower reflected in its sovereign CDS spread can thus be considered an indicator of the country's economicpolitical stability, which is linked to country-specific macroeconomic variables such as output growth, foreign exchange reserves, budget deficit, real effective exchange rate deviation, and foreign direct investment (Hui & Fong, 2015: 174). Moreover, the changes in credit risk premiums of sovereign markets, which translate into changes in sovereign CDS spreads, do not emanate from changes in the fundamentals of the underlying economies (Hassan, Hassan, & NgowYu, 2013; Ngene et al., 2014). Rather, these variations mirror a change in the risk appetite of market participants in terms of credit exposure. A negative change in the creditworthiness of a sovereign country inevitably translates into currency depreciation along with soaring currency volatility (Bekkour, Jin, Lehnert, Rasmouki, & Wolff, 2015: 68). An increase in CDS premium, which means that the risk of the country increases, would lead investors to sell securities denominated in the country's currency and to repatriate funds, hence putting downward pressure on the currency as indicated by Hui and Chung (2011: 2945) or vice versa. The global finance crisis occurred in 2008, yet its effects on the global economy are still visible. One outcome of the crisis is that investors' risk appetite has changed. Investors increasingly ask for low-risk investments even if they are low-yield. Thus investments are more risk sensitive. Theoretically, the response of an investor to a positive change in country risk would not be the same as the response to a negative change. Investors would be diffident and skeptical as risk decreases
and react slowly to the new condition. In this case, the national currency would appreciate against any other currency only in the longer time period. In the case of an increase in risk, investors would react rapidly and the national currency would sharply depreciate in a very short time period. Business cycles are another factor that impacts the risk appetite of investors. In contraction periods, investors look for safe havens and are more sensitive to risk. On the other hand, investors increase their risk appetite and become more willing to invest during expansion periods. In short, the efficiency of the mechanism explaining the interaction between CDS spread and exchange rate depends on business cycles. If the economy is in a contraction period, an investor may follow a change in CDS premiums more curiously. On the other hand, he/she may not be so curious about it in expansion periods. In expansion periods, when we take international capital flows into account for an emerging economy such as Turkey, a change in CDS premium would not affect the value of the currency via capital inflow or outflow in a floating exchange rate regime. On the other hand, in contraction periods, a change in CDS premium would affect the value of the currency via capital movement more than the case of expansionary period. Several studies confirm the existence of a linkage between sovereign default risk and exchange rate. The initial study belonging to Carr and Wu (2007) investigates the Brazil and Mexico economies and implies the causation linkage from CDS spreads to currency option market for both of them. Recent studies mainly focus on the Eurozone after the crisis to understand the nature of the euro and to find the role of increasing financial risk in the monetary union. The studies of Hui and Chung (2011) and Bekkour et al. (2015) investigate similar periods covering crisis times. While Bekkour et al. (2015) find the effect of CDS premiums on the euro during the financial crisis, Hui and Chung (2011) relate the effect of CDS premiums to fiscal conditions of Eurozone countries. Different from initiative studies, Omachel and Rudolf (2014) investigate the case of the euro in the post-crisis period and find weak causation linkages between them. Hui and Fong (2015) examine the Eurozone, the U.S., Japan and Switzerland, and Zhang et al. (2010) examine Japan, Eurozone and the United Kingdom. Both studies conclude that CDS premiums have an effect on exchange rates even if it is contemporaneous or via expectations. In a recent study, Della Corte, Sarno, Schmeling, and Wagner (2015) present a broader analysis for the effect of CDS spreads on the value of national currencies in twenty developed and emerging economies over a long time horizon. They find a linkage between sovereign risk and currency option as well as spot currency value in both developed and emerging economies. The global financial crisis, the European debt crisis, changes in the monetary policies of developed economies, and country/region-specific economic and political instability have induced depreciation of the value of currencies of emerging market economies. Like many other emerging market economies, the volatility of the Turkish lira has increased during the recent crisis period. The Turkish lira and Brazilian real have
Please cite this article in press as: Hassan, M. K., et al., Does credit default swap spread affect the value of the Turkish LIRA against the U.S. dollar?, Borsa _ Istanbul Review (2016), http://dx.doi.org/10.1016/j.bir.2016.10.002
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depreciated the most among fragile quintet currencies. As can be seen in the figure below, the depreciation of the value of the Turkish lira against the U.S. dollar is high during the period after the global financial crisis. The nominal exchange rate increased 102% from September 2009 to September 2015. The high depreciation ratio of the Turkish lira compared to other emerging currencies, in the presence of country risk, has made the Turkish lira important to analyze (Fig. 1). In this study, we aim to analyze the Turkish economy to understand the interaction between financial risk and exchange rate volatility in order to understand how changes in financial risk affect national currency in various types of shock and business cycle periods. To measure the risk of a financial system, we employ CDS spreads as shown by Pan and Singleton (2008). We use the MS VAR method in order to see the interaction between variables in different regimes. By doing so, we will be able to better understand the behavior of the value of the Turkish lira against the U.S. dollar rate in those various shock types and business cycle periods. We also use the rolling windows causality test methods developed by Balcılar and Ozdemir (2013) in order to see the exact dates when the causality between exchange rate and CDS occurs. The empirical results obtained from our analyses might be useful in understanding whether we can use CDS premium changes to predict exchange rate fluctuations in terms of U.S. dollar/Turkish lira. More generally, our results may have implications for the predictability of an economy's exchange rate risk through the use of information in the sovereign CDS market and the effects of the monetary policies adopted by central banks on the currency and sovereign CDS markets. Unlike existing studies, we employ recently developed causality methods and investigate the relation between CDS premiums and exchange rates in different risk movements and in different regimes. By doing so, we will be able to better understand the nature of exchange rate behavior. In the following section, we explain our econometric methods. In the third section, we introduce the data employed
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in our model. We present the empirical results obtained from the empirical analyses in the fourth section. Finally, we interpret our empirical results and offer conclusions in the last section. 2. Methodology In this section, we introduce the empirical methods used. First we describe the Markov Switching Vector Autoregressive (MS VAR) method that is used to obtain impulse-response functions for each regime to see possible differences in the behavior of the exchange rate due to changes in the business cycle. Secondly, we describe the rolling windows causality analysis developed by Balcılar and Ozdemir (2013) to determine the time table of the causal relationship between variables. 2.1. Markov Switching VAR methodology As an empirical methodology, Markov Switching Vector Autoregressive Model (MS-VAR) is applied to estimations of interaction between variables in different regimes of business cycles such as expansion, contraction, boom and recession. As the financial sector is quite sensitive to fluctuations in the economy, crises affect both CDS premiums and the value of national currency against foreign currencies. Hence, MS-VAR is a good tool for monitoring asymmetric behaviors in the historical process and has the advantage of accommodating structural changes across regimes, both with respect to autoregressive dynamics and to the covariance structure of the shocks (Binder & Gross, 2013). In this respect, to measure the effect of a CDS premium change on the value of national currency in different regimes, we employ the MS-VAR method and investigate the asymmetric behavior of interactions between variables. By employing the method, we may better understand the relation between CDS premium and the value of the Turkish lira
Fig. 1. Volatility in nominal exchange rate and CDS premiums. It shows volatility in CDS premium difference between Turkey and U.S. and nominal exchange rate after the global crisis period. Time period extends from September 2009 to September 2015. Right axis shows % change in nominal exchange rate compared to previous month. Left axis shows CDS premium difference. Please cite this article in press as: Hassan, M. K., et al., Does credit default swap spread affect the value of the Turkish LIRA against the U.S. dollar?, Borsa _ Istanbul Review (2016), http://dx.doi.org/10.1016/j.bir.2016.10.002
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against the U.S. dollar and determine whether or not the changes are due to business cycles. In conventional models, it is not possible to know which regime is effective at any point in time. However, in an MSVAR method, probabilities of all regimes for any point in time are all known. The MS-VAR method was originally developed by Hamilton (1989) and used by Hamilton (1989, 1990, 1994, 1996), Kim and Nelson (1998), and Krolzig (1997, 1998, 2000, 2001) for the empirical analysis of business cycles. Hamilton’s (1989) 2-regimes Markov Switching Intercept Autoregressive Heteroscedasticity MSIAH-AR (p) model takes the following form: f1;0 þ f1;1 yt1 þ / þ f1;p ytp þ A1 εt if ðst ¼ 1Þ yt ¼ f2;0 þ f2;1 yt1 þ / þ f2;p ytp þ A2 εt if ðst ¼ 2Þ ð1Þ where f1;j and f2;j denote autoregressive lag parameters for every regime; st is the value of each regime; p shows the degree of the autoregressive process; and εit is a sequence of independent and identically distributed random variables with mean zero [εit is iidð0; s2i Þ and s2i < ∞] (Mohd and Zahid 2006; Fallahi and Rogriguez, 2007; Kayhan, Bayat, & Kocyigit, 2013). However, as each fundamental residual is pre-multiplied by a switching matrix, Ai εt , the var-cov matrix Si of the structural disturbances in Ai εt is regime-dependent as indicated by the following transformation: Si ¼ E Ai ut u0t A0i ¼ Ai E ut u0t A0i ¼ Ai I2 A0i ¼ Ai A0i ð2Þ The main characteristic of MS-VAR is that the dynamics of the variables are conditioned on the unobserved Markov process followed under the regime because the Markov chain is unobservable. After the identification of coefficients belonging to each regime, impulse response functions are obtained by employing the generalized impulse response function process. 2.2. Balcılar and Ozdemir (2013) bootstrap rolling window causality test Empirical studies that examine the causation linkage between variables may suffer from inaccurate findings from fullsample time series data when the data series experiences structural changes. Structural changes may create shifts in the parameters, and the pattern of the causal relationship may change over time (Balcılar and Ozdemir, 2013: 1400). As mentioned before, there might be asymmetries in the behavior of investors in addition to the business cycles. There might be structural changes in causation linkage between CDS premiums and the value of the national currency. In order to deal with structural changes and parameter non-constancy, we employ the bootstrap rolling window causality test developed by Balcılar and Ozdemir (2013). Balcılar and Ozdemir (2013) ran a LR (likelihood ratio) causality test using a bootstrap method that depends on the error term. The LR Granger causality test depending on bootstrap has two variables VAR (p) in the model, t ¼ 1, 2, …, T;
yt ¼ F0 þ F1 yt1 þ / þ Fp ytp þ εt
ð3Þ
In the equationP above, εt ¼ ðε1 ; ε2 Þ is iidð0; s Þ is a covariance matrix that is not odd. An optimal lag length criterion is defined by Akaike Information Criteria (AIC). While yt ¼ ½y1t ; y2t 2x1 is considered a matrix, the VAR (p) model will be shown as f f ðLÞ f12 ðLÞ y1t ε y1t ¼ 10 þ 11 þ 1t ð4Þ f21 ðLÞ f22 ðLÞ y2t y2t f20 ε2t P where fij ðLÞ ¼ pk¼1 fij ; kLk , i, j ¼ 1, 2; k is the lag operator; and Lk xt ¼ xtk . In order to avoid possible structural unit root and to overcome any problems related to the size of the sample, Balcılar and Ozdemir (2013) used the bootstrap test that was modified by Koutris, Heracleous, and Spanos (2008) and Shukur and Mantalos (2000). See the details for this process in Balcılar and Ozdemir (2013). 2
3. Data description Before describing the data and its sources, it might be useful to show the volatility of the Turkish lira and the movement of the CDS spread in the Turkish economy. As can be seen in Graph 1, the value of the Turkish lira against U.S. dollar rate is volatile during the whole period. In 2012, it is quite stable. On the other hand, CDS spread has an increasing trend. In the light of graphical presentation, it is possible to imply co-movement with exchange rate volatility. For comparison, we use the perspective of USD-based investors in our analysis. We obtain monthly data belonging to five year sovereign CDS spreads of Turkey and the U.S. from September 2009 to October 2015. As exchange rates reflect the rate of exchange between the two economies' currencies, CDS spreads are thus expressed as the differences between the CDS spreads of the Turkish and U.S. economies (the CDS spread of the Turkish economy minus the CDS spread of the U.S. economy; hereafter, CDS difference). We use monthly U.S. dollar/Turkish lira nominal exchange rate data to measure the value of the Turkish lira. According to Pan and Singleton (2008), CDS spreads are related to the investors' risk appetite associated with global event risk, financial market volatility, and macroeconomic policy (Hui & Fong, 2015: 184). Therefore, it is important to identify whether the sovereign CDS spreads and risk reversals of the U.S. and Turkish economies in this study remain cointegrated in the presence of other macro-financial factors. In order to solve this problem, we include a set of macrofinancial variables as control variables. These are macrofinancial condition variables (S&P 500 and BIST 100 indexes), interest rate difference, inflation rate difference, and global risk appetite in the S&P stock market (VIX index). The U.S. stock market index, proxied by the S&P 500, and the Turkish stock market index, proxied by the BIST 100, are used as macroeconomic control variables. To account for unusual turbulence in the stock market following the implosion of the U.S. subprime market, we include the implied volatility of the S&P 500 index option (VIX) as an additional
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explanatory variable (Zhang et al., 2010: 448). Interest rate difference data (policy interest rate of the Turkish economy minus the U.S. policy interest rate) is used as a control variable in order to see the effects of possible increases in interest rate differential on CDS spreads. Similarly, we include inflation rate difference data (inflation rate in the Turkish economy minus inflation rate in the U.S. economy) as another macroeconomic control variable in the model. The Bloomberg database is the source of the monthly data belonging to 5 year CDS spreads for the Turkish and U.S. economy, BIST 100 index, S&P 500 index, VIX index, and USD/TL nominal exchange rate. Policy interest rate and inflation rate data for both economies are obtained from the International Financial Statistics database published by the International Monetary Fund. 4. Analysis of empirical results One important issue to consider in MS VAR and Granger type causality analyses is testing the stationarity of variables. With this aim, we employ the unit root test developed by Dickey and Fuller (1979, 1981) (hereafter ADF). According to our results, BIST 100 index (hereafter, BIST100), S&P 500 index (hereafter, SP500), nominal exchange rate data (hereafter, NEER), CDS spreads difference (hereafter, CDSDIF), interest rate difference (hereafter, INTDIF), VIX index (hereafter, VIX) and inflation rate difference (hereafter, INFDIF) include unit root in level, and the first differences of the variables are stationary. Thus the first difference of each variable must be used in the analysis. Moreover, we include trend and seasonality as external dummies in our model to get more robust results (Table 1). The first step of MS VAR analysis is to determine the number of regimes. In Table 2, LR (rate of probability) and Davies2 test statistics show that all regimes have a non-linear and asymmetric structure. According to the test statistics, the model with two regimes has the smallest SC and AIC statistics. It also has the biggest rate of LR. Thus, the optimal lag length is two according to the Schwarz Information Criteria. In light of this finding, the transition probability matrices which are obtained by using the MSIA(2)-VAR(2) model are presented in Table 3. According to regime transition probability results, when the economy is in the first regime, the probability of remaining in the same regime is 74%, while the probability of transitioning to the second regime is 26%. On the other hand, if the economy is in the second regime, the probability of remaining in the same regime is 77%, while the probability of transitioning to the first regime is 23%. According to the regime analysis results, the length of regime one is 0.96 years and 1.09 years for the second regime. The speed of entrance to the second regime from the first regime is 0.32 years, while the speed of entrance to the first regime from the second one is 0.33 years (Fig. 2).
2 For detailed information about Davies asymmetry test, please see Davies (1977, 1987) and Garcia and Perron (1996).
5
Table 1 ADF unit root test results. Variables
Level Constant
1.986 (0) [0.291] CDS DIF 1.471 (0) [0.542] INF DIF 2.241 (0) [0.193] INT DIF 1.757 (0) [0.398] SP500 0.442 (0) [0.893] EXC RATE 2.112 (0) [0.999] VIX 3.214 (0) [0.023**]
BIST100
First difference Constant þ trend Constant
Constant þ trend
2.590 (0) [0.285] 2.156 (0) [0.506] 2.604 (0) [0.279] 2.296 (0) [0.430] 2.837 (0) [0.189] 0.374 (0) [0.986] 4.383 (0) [0.004]***
7.733 (0) [0.00]*** 8.283 (0) [0.00]*** 7.302 (0) [0.00] *** 7.266 (0) [0.00]*** 9.623 (0) [0.00]*** 7.020 (0) [0.00]*** 9.772 (0) [0.00]***
7.778 (0) [0.00]*** 8.211 (0) [0.00]*** 7.366 (0) [0.00]*** 7.252 (0) [0.00]*** 9.701 (0) [0.00]*** 6.685 (0) [0.00]*** 9.846 (0) [0.00]***
It shows the unit root test results. The null hypothesis claims that series contain unit root is tested in level and first difference with a constant and constant and trend models separately. Results for each variable includes test statistics, lag length in parentheses and probability values in brackets. If the test statistics are lower than critical values, the null hypothesis is rejected and there is no unit root. *, ** and *** show significance levels 10%, 5% and 1%, respectively.
Table 2 Test statistics and regime determinants for VAR(2) model. No. of regime
Log prob
LR linearity
Davies
AIC
SC
MS(2) MS(3) MS(4)
14.6057 1312.2906 997.0867
274.2465 478.6674 1109.0751
0.00 0.00 0.00
41.6057 46.7969 41.0447
54.2542 57.9191 55.7043
It represents the test statistics employed to determine the number of regime in VAR (2) model. The lowest Akaike Information Criteria (AIC), Schwartz Information Criteria (SC) and highest LR linearity statistics denote the right regime number for the model.
The impulse-response analysis results for the first regime are presented in Fig. 3. According to our results, a 1% positive shock in CDS difference would induce a positive shock in the exchange rate and continue for three months. The response of the exchange rate is quite high and significant both statistically and theoretically. An increase in CDS difference means that risk in the Turkish financial system increases relative to the U.S. Thus, the price of the U.S. dollar will increase in terms of Turkish lira. On the other hand, the response of the exchange rate to a positive shock in the BIST is negative and statistically
Table 3 Regime transition probability matrices.
Regime 1 Regime 2
Regime 1
Regime 2
0.7408 0.2273
0.2592 0.7727
It represents regime transition probability matrices. The value of cell in upper left corner of the matrices is probability of continue to stay in the first regime after a period in the same regime. The second cell in the first row indicates the probability of transition to second regime from first regime. In the second row, first cell implies the probability of transition to first regime from second regime and bottom right corner of the matrices is probability of continue to stay in the second regime after a period in the same regime.
Please cite this article in press as: Hassan, M. K., et al., Does credit default swap spread affect the value of the Turkish LIRA against the U.S. dollar?, Borsa _ Istanbul Review (2016), http://dx.doi.org/10.1016/j.bir.2016.10.002
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MSIA(2)-VAR(2), 5 - 73 0
D BIST DEXC D INTD D VIX
D CDS D INFD D SP 500
-10000
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10 20 Probabilities of Regime 1 filtered predicted
30
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smoothed
0.5
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10 20 Probabilities of Regime 2 filtered predicted
smoothed
0.5
10
20
Fig. 2. Smoothed avg posterior probability of regime 1 and 2. It shows the probability of regime 1 and 2 in the whole period taken into consideration. In the second figure, blue columns show that economy is in the first regime, vice versa. Red line in figures shows smoothed probability value, while blue line shows predicted probability.
Fig. 3. Response of EXC to 1% positive shock in other variables in regime 1. It represents the response of Turkish lira/U.S. dollar nominal exchange rate to shocks each variable listed in data section in the first regime. The vertical axis indicates the size of response of each variable, while the horizontal axis shows the monthly time period. Blue line symbolizes the response of nominal foreign exchange rate. If the blue line is under the horizontal axis, this means the response of nominal exchange rate to a positive shock in any variable is negative. Please cite this article in press as: Hassan, M. K., et al., Does credit default swap spread affect the value of the Turkish LIRA against the U.S. dollar?, Borsa _ Istanbul Review (2016), http://dx.doi.org/10.1016/j.bir.2016.10.002
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significant. An increase in the BIST can be interpreted as a capital inflow into the financial system and would appreciate the national currency. Conversely, the response of the exchange rate to a positive shock in the SP500 is positive and statistically significant. It is possible to imply that an increase in the S&P 500 index is a capital outflow from the Turkish financial system into the U.S. financial system. The response of the exchange rate to a positive shock in interest rate difference, inflation rate difference, and VIX variables are negative and statistically insignificant. This means that these variables do not affect the exchange rate in the first regime. The impulse-response analysis results for the second regime are presented in Fig. 4. According to our results, the response of the exchange rate to a positive shock in CDS difference is positive and statistically significant for two months. The response of the exchange rate is low relative to the first regime. Similarly, the response of the exchange rate to a positive shock in the BIST is negative and significant. Conversely, the response of the exchange rate to a positive shock in the S&P 500 index is insignificant. The impact of interest rate difference on exchange rate is statistically significant in the second period. An increase in the difference
between interest rates in the Turkish economy and U.S. economy induces appreciation in the Turkish lira. This is also significant theoretically. On the other hand, VIX variable and inflation difference variable do not affect exchange rate either. According to the rolling windows causality analysis results, causality running from CDS difference to nominal exchange rate occurs in October 2011 and January, February, March, April, May, June, September, and October of 2012. On the other hand, the causation linkage from nominal exchange rate to CDS differential occurs in December 2010; January, February, March, and April of 2011; May, July, and October of 2013; January, February, and October of 2014; and February and March of 2015. When we take a look at Fig. 5, it is possible to easily see the causality running from CDS difference to exchange rate in 2012. As is evident, the volatility of exchange rate is low and during the same period the CDS difference between the U.S. and Turkish economies decreases and remains stably low. When we compare all our results with the existing literature, it is possible to conclude that our results are in line with the literature investigating the behavior of national currencies
CDS
BIST 100 1
0.08
0
0.06
1
-1 0.04
2
3
4
7
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9
10
7
8
9
10
-4 1
2
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-0.02
-6
INFD
INTD
0.2 0.1 0 1
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10
-0.2 -0.3 -0.4 -0.5
0.1 0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7
1
2
3
4
SP500
5
6
VIX 0.25
0.4
0.2
0.3
0.15
0.2
0.1
0.1
0.05
0
-0.2
6
-3
0
-0.1
5
-2
0.02
-0.1
7
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0 -0.05
1
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-0.1
Fig. 4. Response of EXC to 1% positive shock in other variables in regime 2. It represents the response of Turkish lira/U.S. dollar nominal exchange rate to shocks each variable listed in data section in the second regime. The vertical axis indicates the size of response of each variable, while the horizontal axis shows the monthly time period. Blue line symbolizes the response of nominal foreign exchange rate. If the blue line is under the horizontal axis, this means the response of nominal exchange rate to a positive shock in any variable is negative. Please cite this article in press as: Hassan, M. K., et al., Does credit default swap spread affect the value of the Turkish LIRA against the U.S. dollar?, Borsa _ Istanbul Review (2016), http://dx.doi.org/10.1016/j.bir.2016.10.002
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References
10% CV
CDSDIF≠>NEER
NEER≠CDSDIF
Fig. 5. Balcılar and Ozdemir (2013) bootstrap rolling window causality results. It represents the timeline of existency of causation linkage between variables. While green line represent the uni-directional causality running from foreign nominal exchange rate to CDS diffierence, red line symbolizes uni-directional causality running from cds difference to nominal foreign exchange rate. Blue line represents 10% confidence interval. When red and/or green line is under blue line, the causation linkage exists variables even in 10% significance level.
against the U.S. dollar. The findings of our study and the results of Carr and Wu (2007), Zhang et al. (2010), and Della Corte et al. (2015) resemble one another. 5. Conclusion This study determines the nature of causal relations between CDS spreads and the value of the national currency against the U.S. dollar in the Turkish economy. A novel approach to the Markov Switching VAR method, which investigates this relation in multiple periods, is applied to the Turkish economy during the period 2009e2015. Furthermore, we carry out the rolling window causality approach. The method is useful for determining the exact date that causality occurs. The rolling window causality analysis results emphasize the causal relationship from CDS spreads to the value of Turkish lira against the U.S. dollar, especially in 2012 when exchange rate volatility was relatively low and CDS spreads remained low. The MS VAR analysis results point out that the Turkish economy experienced two regimes in the period between the years 2009 and 2015. Impulse-response analysis results show that impact of CDS spreads on the value of Turkish lira against U.S. dollar is positive and stronger in the first period relative to the second period. In the second period, interest rate difference is another variable affecting exchange rate. The behavior of investors in expanding regimes might be affected by interest rate differentials rather than any risk factor due to changing risk appetite; this is why we can label the second period an expanding regime. On the other hand, the impact of risk factor on exchange rate in the first regime might be caused by the behavior of investors, for instance low risk appetites. According to our results obtained from all econometric analysis methods, there is a causation linkage running from CDS spread to the nominal foreign exchange rate. The strength of causality differs according to regime. These results imply that observation of CDS spread changes might be useful to predict exchange rate instability. This would help policymakers in stabilizing the value of the Turkish lira against U.S. the dollar.
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Please cite this article in press as: Hassan, M. K., et al., Does credit default swap spread affect the value of the Turkish LIRA against the U.S. dollar?, Borsa _ Istanbul Review (2016), http://dx.doi.org/10.1016/j.bir.2016.10.002