Sovereign credit risk and economic risk in Turkey: Empirical evidence from a wavelet coherence approach

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Sovereign credit risk and economic risk in Turkey: Empirical evidence from a wavelet coherence approach Dervis Kirikkaleli a,*, Korhan K. Gokmenoglu b a

European University of Lefke, Faculty of Economic and Administrative Science, Department of Banking and Finance, Lefke, Northern Cyprus, TR-10, Mersin, Turkey b Eastern Mediterranean University, Faculty of Business and Economics, Department of Banking and Finance, Famagusta, North Cyprus via Mersin 10, Turkey Received 7 May 2019; revised 19 June 2019; accepted 19 June 2019 Available online ▪ ▪ ▪

Abstract This study aims to shed light on the co-movement of sovereign credit risk and economic risk in Turkey using the TodaeYamamoto causality, Gradual Shift causality, and Wavelet Coherence tests. The study answers the following questions, which, to the best of our knowledge, have not been investigated in the literature: (i) Is there any causal linkage between sovereign credit risk and economic risk?; and (ii) If yes, why? Our findings reveal that (i) economic risk caused sovereign credit risk in 1997 and 2002; and (ii) between 2001 and 2012, sovereign credit risk caused economic risk at different scales. The TodaeYamamoto causality and Gradual Shift causality tests confirm that, in Turkey, changes in sovereign credit risk significantly lead to changes in economic risk, indicating the importance of sovereign credit risk for predicting economic risk. _ Copyright © 2019, 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 Codes: C32; G24; E44 Keywords: Sovereign credit risk; Economic risk; Turkey

1. Introduction For the last several decades, facilitated by ever-increasing cross-border investments, financial deregulation, and liberalization, credit ratings have gained vital importance and have become an essential component of the global financial system. Credit ratings have been increasingly hardwired into financial contracts, have become an indispensable part of the investment mandates of institutional investors, and are a key indicator for the classification of securities. Credit rating changes or outlook signals have an undeniable effect on the decisionmaking procedures of global financial players; hence, they cause reweighting of global portfolios, reallocation of capital,

* Corresponding author. E-mail addresses: [email protected] (D. Kirikkaleli), korhan. [email protected] (K.K. Gokmenoglu). _ Peer review under responsibility of Borsa Istanbul Anonim S¸irketi.

changes in the liquidity of the market, and fluctuations in the cost and flow of funds (Longstaff, Pan, Pedersen, & Singleton, 2011). Due to these factors, financial markets have become increasingly dependent on these credit ratings. This remarkable influence of credit ratings on the financial markets has also increased the risk of undesirable systemic consequences (Alsakka & Ap Gwilym, 2013). It has been argued widely that an unfavorable rating announcement can trigger a chain reaction, thus worsening the fundamentals of the country and causing subsequent rating downgrades. This issue was emphasized particularly in the wake of the 2008 financial crisis and subsequent European debt crisis. Although there is a vast amount of literature on many aspects of the credit ratings and credit ratings' agencies, the causal relationship between sovereign credit risk and economic risk for developing countries has not been investigated thoroughly. Our study examines this relationship for the case of Turkey, using several causality

https://doi.org/10.1016/j.bir.2019.06.003 _ 2214-8450/Copyright © 2019, 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 as: Kirikkaleli, D., & Gokmenoglu, K. K., Sovereign credit risk and economic risk in Turkey: Empirical evidence from a wavelet _ coherence approach, Borsa Istanbul Review, https://doi.org/10.1016/j.bir.2019.06.003

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tests, with the aim of obtaining robust findings that can be used for policy-making processes. A sovereign credit rating can be defined as a measure of the creditworthiness of a country and the capacity and perceived willingness of the government to service its financial obligations both in full and on time (Bissoondoyal-Bheenick, 2005; Chen, Chen, Chang, & Yang, 2016). Credit ratings are assigned by the notation institutions called credit rating agencies (CRA) and provide a single measure of the probability of default, hence reflecting a summary measure of all the risk factors of a country. A credit rating is expected to incorporate all publicly available economic, social, and political information. Credit ratings provide valuable signals for both financial markets and cross-border investors. One of the most significant benefits of credit ratings is that they lessen information asymmetry between debtors and borrowers by providing a reliable measure and up-to-date information for the creditworthiness of a country (Van Nieuwerburgh & Veldkamp, 2009). Credit ratings are also essential for the smooth and efficient functioning of the global financial markets, hence easing and increasing cross-border investments. CRAs have been criticized for several reasons, including the subjective nature of the rating assessment process, the opaque evaluation procedure (G€artner & Griesbach, 2012), the inherent conflict of interest problem, known as the blackmail hypothesis (Bernal, Girard, & Gnabo, 2016), poor communication, and cliff effects. Furthermore, following the 2008 global financial crisis, they were heavily criticized for failing to predict the upcoming crisis. However, a signifficant amount of additional criticism has also been directed at the relationship between CRAs and financial crises. It has been voiced widely that the deficiencies of the working methods of CRAs deepened the financial crises. CRAs have been accused of exacerbating the 2008 financial crises, the subsequent Great Recession, and the European debt crisis for two opposing reasons: their inflated ratings for structured products and their excessive and over hasty downgrades of sovereign and bank ratings in the Eurozone. The cyclical characteristics of the decisions (Bar-Isaac & Shapiro, 2013) and the untimely announcements of CRAs are the main factors underlying the criticism that CRAs exacerbated the financial crisis. It is claimed that ratings depend more on the general mood of the markets than on the economic fundamentals of the sovereigns (Barroso, 2010; Boumparis, Milas, & Panagiotidis, 2017). The relatively lax assessment and underestimation of the risk during the boom years led to overheating of the economy, misallocation of financial resources, and creation of bubbles and rendered the economy more susceptible to financial crisis. On the contrary, during periods of market pessimism, CRAs tend to have a stricter evaluation, overreact to any unfavorable developments, and act in an untimely manner. A credit rating downgrade can lead to unrest in the markets, capital flight, a higher risk premium-borrowing cost, deterioration of debt structure, higher fiscal deficits, and weaker macroeconomic fundamentals of the sovereign. These developments can contribute to contagious pessimism. Self-fulfilling panic creates a vicious

cycle that triggers further credit downgrades and ultimately causes either deeper financial distress or even default of the country (El-Shagi & von Schweinitz, 2018). This self-fulfilling and destructive characteristic of credit ratings was first theorized by Ferri, Liu, and Stiglitz (1999). Although there are different forms of this model (see Bruneau, Delatte, & Fouquau, 2014; Manso, 2013), all of them are based on the following core principle: The triggering effect of a credit downgrade can cause a worse equilibrium as a result of feedback loops created by the downgrade, and this development might lead to the default of the sovereign. This notion makes the question of whether there is a feedback relationship between economic risk and sovereign rating very interesting. Empirical studies have shown that the effects of the CRAs on the financial markets are more potent for lower-rated countries, for higher-notch downgrades, and in crisis periods (Hooper, Hume, & Kim, 2008). If the critique that credit ratings are detrimental because they do not necessarily reflect the countries' fundamentals (Vernazza & Nielsen, 2015) is correct, the above findings imply that the primary victims of this faulty evaluation will be developing countries (De Moor, Luitel, Sercu, & Vanpee, 2018). The existence of a feedback loop, theorized by Ferri et al. (1999), might have more destructive effects on developing countries. Turkey, as a developing country that has historically experienced persistently low credit ratings, has increasingly voiced its concern about the reliability of credit ratings following a series of credit downgrades that occurred in 2018. Prior to the June 2018 election, President Erdogan's hard language directed against Moody's1 brought these criticisms to a peak. Hence, recent developments have made Turkey an exciting case for researchers and have led to renewed interest in sovereign credits and CRAs. For this reason, our study aims to investigate the causal relationship between economic risk and sovereign credit risk for the case of Turkey. If a bilateral causal relationship exists between these variables, this finding implies that any unfavorable developments in either of these variables can create feedback loops (Manso, 2013), cause further deteriorations in macroeconomic fundamentals, and trigger a vicious cycle, and, ultimately, might result in certain undesirable and unfair outcomes. Therefore, it is evident that the answer to our research question has important economic and political implications. In this study, we use the wavelet coherence technique to investigate and quantify the time-frequency dependence of sovereign credit risk and economic risk for the case of Turkey. The technique, taken from statistical physics, combines information about both time and frequency to capture previously-hidden information. To support the findings from the wavelet coherence approach, we employ the TodaeYamamoto causality and Gradual Shift causality tests, which are examples of robust causality tests. To capture 1 Erdogan threatens Moody's: “With the help of God, after the elections an operation was launched against them”. https://defence.pk/pdf/threads/erdoganthreatens-moodys-with-the-help-of-god-after-the-elections-an-operation-waslaunched-agai.563606/.

Please cite this article as: Kirikkaleli, D., & Gokmenoglu, K. K., Sovereign credit risk and economic risk in Turkey: Empirical evidence from a wavelet _ coherence approach, Borsa Istanbul Review, https://doi.org/10.1016/j.bir.2019.06.003

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macroeconomic risk, we use the economic risk variable, which is defined as the “likelihood of experiencing adverse effects due to the host country's troubles in reaching macroeconomic goals” (ICRG, 2018). Economic risk data constitute five major factors: GDP per capita, real GDP growth, annual inflation rate, budget balance as a percentage of GDP, and current account as a percentage of GDP. Thus, our first aim is to capture the effect of economic risk as a determinant of the sovereign rating change. Also, following the recent crises, the effect of credit rating announcements on macroeconomic stability has risen as an important research question. Therefore, our second aim is to capture this effect. Our study design is well-suited to examining both directions of the potential causality between sovereign credit risk and economic risk. Moreover, our study can provide an answer to the question of whether criticisms directed towards CRAs, such as overreaction to unfavorable news and credit rating decisions, contribute to economic vulnerability in Turkey. 2. Literature review The undeniable supremacy of the credit ratings over the financial markets and financial decision-making procedures has led to the emergence of a large volume of literature on the determinants of credit ratings and the structure of the models used by CRAs in the rating determination process. The pioneering study of Cantor and Packer (1996) investigated the determinants of credit ratings provided by Moody's and Standard and Poors by assessing the importance of eight macroeconomic variables. That study was followed by many others. Given the importance of macroeconomic fundamentals, they were the earliest and most widely-used set of variables employed in investigating the determinants of credit ratings (Liu, Kalotay, & Tru¨ck, 2018). Researchers have found evidence for the significant effect of many macroeconomic variables, including GDP growth (Perrelli & Mulder, 2001), GDP per capita (Canuto, Dos Santos, & Porto, 2012), unemployment (Afonso, Gomes, & Rother, 2009), economic strength (Sehgal, Mathur, Arora, & Gupta, 2018), and inflation rate (Afonso, 2003), for the assessment of CRAs. Moreover, the role of fiscal variables, such as government debt and fiscal balance (Agliardi, Pinar, & Stengos, 2014; Erdem & Varli, 2014), government deficit (Afonso, Furceri, & Gomes, 2012), fiscal strength (Sehgal et al., 2018), fiscal transparency (Hameed, 2005), and public debt (Vernazza & Nielsen, 2015), and financial variables, including foreign reserves (Bissoondoyal-Bheenick, 2005), financial depth and efficiency (Alexe, Hammer, Kogan, & Lejeune, 2003, pp. 1e40), financial openness (Andreasen & Valenzuela, 2016), and the size of the banking system (Sehgal et al., 2018), were proposed as some of the most important determinants of sovereign credit ratings. Although less frequently than the variables mentioned above, several articles have shown that various other variables, such as political risk of the sovereign country (Ozturk, 2014; Remolona, Scatigna, & Wu, 2008), the political business cycle (Block & Vaaler, 2004), qualitative political and social indicators (Bissoondoyal-Bheenick, 2005),

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legal and political institutions (Butler & Fauver, 2006), government effectiveness (Afonso, Gomes, & Rother, 2011), inflation targeting (IT) adoption (Balima, Combes, & Minea, 2017), and global market factors (Aizenman, Hutchison, & Jinjarak, 2013), have an impact on the credit rating determination. Additionally, default history has been indicated as being one of the most important determinants of sovereign credit ratings (Borio & Packer, 2004). The second major strand of the literature investigates the effect of credit ratings on the sovereign country. A credit rating downgrade announcement affects the country's access to international capital markets (Reinhart, 2002) and the terms and conditions of the credits, leading to a higher cost of credit for the downgraded country, which might have detrimental effects on the economy. There are findings that a change in sovereign rating can affect the rating of the banking sector (Williams, Alsakka, & Ap Gwilym, 2013), banks' profitability and capital ratios (Cavallo, Powell, & Rigobon, 2013), and bank funding costs (Alsakka, ap Gwilym, & Vu, 2014). Higher borrowing costs lead to several other problems, including a higher budget deficit, lower government expenditureeinvestment (Cantor & Packer, 1996), lower real private investment (Chen, Chen, Chang, & Yang, 2013), slowdown of international trade and bilateral FDI flows (Cai, Gan, & Kim, 2018), and deterioration of macroeconomic fundamentals. Chen et al. (2016) stated that a one-notch downgrade causes a decline in the GDP of approximately 0.3% in the downgraded country. The effect of credit ratings on the financial markets has also been well-documented in the literature. Early research focused mainly on the short-term impact of credit rating announcements on the financial markets. Kaminsky and Schmukler (2002) found evidence for a significant effect of changes in credit ratings on the stock and bond markets of 16 developing countries for the period 1990e2000. Some findings have indicated the effect of credit ratings on CDS spreads (Reisen & Von Maltzan, 1999) and government bond yields (Baum, Sch€afer, & Stephan, 2016). According to many studies, credit rating change is a factor contributing to the volatility in financial markets, which could be harmful to financial stability and could ultimately lead to output volatility (Afonso, Gomes, & Taamouti, 2014). The most striking point of the literature is that many studies have confirmed that credit ratings have an asymmetric effect on the financial markets. There is a general consensus that, although rating downgrades have remarkable effects on the markets, the effect of rating upgrades is relatively limited or even nonexistent (Hill & Faff, 2010). The immediate asymmetric effect of a rating downgrade announcement or signal is observed as a volatility increase in the stock market (Brooks, Faff, Hillier, & Hillier, 2004; Ferreira & Gama, 2007; Hooper et al., 2008), and a similar response is also observed for the bond markets (Afonso et al., 2014) of the country. In addition to its effect on sovereign economies, rating revision also has a considerable international spillover effect, which magnifies its impact on the global economy. The rating change of one country can have spillover effects on the

Please cite this article as: Kirikkaleli, D., & Gokmenoglu, K. K., Sovereign credit risk and economic risk in Turkey: Empirical evidence from a wavelet _ coherence approach, Borsa Istanbul Review, https://doi.org/10.1016/j.bir.2019.06.003

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financial markets of other countries. This effect is particularly evident for stock (Kaminsky & Schmukler, 2002) and foreign exchange markets (Alsakka & ap Gwilym, 2012). A considerable amount of research has shown that credit rating downgrades increase the volatility in foreign currency markets (Baum et al., 2016) and have spillover effects on the exchange rates of other countries (Alsakka & ap Gwilym, 2012). Ismailescu and Kazemi (2010) proposed that international trade and financial linkages are the main reasons for the spillover effect. There is also evidence that the credit rating change of a country can have a spillover effect on the economic growth rate of other countries (Chen et al., 2016). This phenomenon is called the output spillover effect. According to Arezki, Candelon, and Sy (2011), the spillover effect is significantly more powerful in the case of a rating downgrade of a country to near-speculative grade. It has been found that the rating downgrades of Greece had systematic spillover effects on other European periphery countries (Arezki et al., 2011). These findings imply that credit rating changes have strong, multifaceted impacts at both national and international levels (Amstad & Packer, 2015). As summarized above, there is a vast amount of literature on several aspects of the sovereign credit ratings, such as the determinants of the ratings and their domestic and global effects. Although there are studies on the causal relationship between sovereign credit ratings and several selected variables, this literature is relatively scarce. For example, Afonso et al. (2011) found evidence for a bi-directional causality between rating changes and CDS spread. They also listed four channels to explain the causal effect from sovereign ratings to the financial sector and two other channels to describe the reverse direction of causality. Although there are studies that have criticized the CRAs either for their harmful effects in the short-run or for the misalignment of their ratings, these studies have not provided sufficient evidence to prove that credit ratings might trigger a vicious cycle (El-Shagi & von Schweinitz, 2015). The literature on the causal relationship between the sovereign credit rating and economic risk of the sovereign is quite limited. This question is more pertinent for developing countries that have historically low ratings and are prone to financial distress. Our study is the first to investigate in detail the causal relationship between sovereign credit rating and economic risk for the case of Turkey. 3. Data To explore the causal relationship or correlation between sovereign credit risk and economic risk in the emerging market of Turkey, we use quarterly time series data covering the period 1991e2016. The dataset for the economic risk index is collected from the Political Risk Service (PRS) Group, and the sovereign credit risk is constructed based on the numerical transportation method of Chiang, Jeon, and Li (2007) and Christopher, Kim, and Wu (2012) for the ratings and outlooks of SandP, Fitch, and Moody's for Turkey. The numerical transformation of the ratings and outlooks is demonstrated in Table 1. The components of the economic risk

Table 1 Rating summery statistics and rating linear transformation. Explanation

SandP and Fitch

Moody's

Linear Scale

Prime

AAA AAþ AA AA Aþ A A BBBþ BBB BBB BBþ BB BB Bþ B B CCCþ CCC CCC CC SC/RD

Aaa Aa1 Aa2 Aa3 A1 A2 A3 Baa1 Baa2 Baa3 Ba1 Ba2 Ba3 B1 B2 B3 Caa1 Caa2 Caa3 Ca C

20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0

High Upper Medium

Lower Medium

Non-Investment

Highly Speculative

Substantial Credit Risk

Selective Default

Chiang et al. (2007) and Christopher et al. (2012) suggested that, whereas a positive and negative outlook adds 1/3 and 1/3 to the rating values, correspondingly, a stable outlook does not affect the rating value.

index are GDP per capita, real GDP growth, annual inflation, budget balance as a percentage of GDP, and current account as a percentage of GDP. The economic risk index ranges from 0, indicating the maximum risk level, to 50, indicating the minimum risk level. The index simply provides information about the strengths and weaknesses of a country in terms of the economic environment, as mentioned in the ICRG Methodology report (ICRG, 2018). 4. Methodology As an initial test, we checked the order of integration of the variables sovereign credit risk and economic risk for Turkey using the Ng and Perron (2001), Zivot and Andrews (1992), and Elliot, Rothenberg, and Stock (1996) unit root tests. The reason why we used these tests is because, as is well known, unit root is not a pre-requisite of wavelet analysis, but it is important for Granger-causality analysis. Based on the main purpose of this study, we explored the time-frequency dependence of the sovereign credit risk and economic risk in Turkey using the wavelet power spectrum and wavelet coherence techniques from the “bivariate” package of the R statistical software. The techniques were developed initially by Goupillaud, Grossmann, and Morlet (1984). It is wellknown and widely accepted that time series-based economic or finance indices are likely to be non-stationary at their levels. Moreover, “the key problem with a standalone frequency domain approach, more specifically referred to as the Fourier transform, is that by focusing solely on the frequency domain, the information from the time domain is completely omitted” (Pal & Mitra, 2017, p. 231). Furthermore, a significant

Please cite this article as: Kirikkaleli, D., & Gokmenoglu, K. K., Sovereign credit risk and economic risk in Turkey: Empirical evidence from a wavelet _ coherence approach, Borsa Istanbul Review, https://doi.org/10.1016/j.bir.2019.06.003

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structural break(s) in the time series dataset causes the estimation results of traditional causality tests with fixed parameters to suffer. To avoid these problems, we used the waveletbased Granger causality test. In this study, we used a wavelet - w - which is one of the components of the Morlet wavelet family. The wavelet equation is shown simply as: wðtÞ ¼ p4 eiu0 t e2t ; pðtÞ; t ¼ 1; 2; 3 1

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ð1Þ

where w is applied on limited times series observations. Frequency, represented by ( f ), and time or location, represented by (k), are the main parameters of the wavelet. While a wavelet's particular location in time is the fundamental character of the k parameter, the frequency parameter controls the distended wavelet for localizing various frequencies. By transforming the wavelet equation, wk; f can first be constructed. The equation of this transformation is shown below:   1 tk wk; f ðtÞ ¼ pffiffiffi w ; k; f 2R; f s0 ð2Þ f h In the next step, the continuous wavelet can be generated from w as a function of k and f, given time series data p(t), as follows: Z∞ Wp ðk; f Þ ¼ ∞

  1 tk pðtÞ pffiffiffi w dt; f f

ð3Þ

The next equation re-creates initial times series p(t) with the w coefficient, as follows: 1 pðtÞ ¼ Cj

Z∞ Z∞     Wp ða; bÞ2 da db: b2 0

ð4Þ

∞

To capture the fluctuation in the economic risk and sovereign credit risk in Turkey, the wavelet power spectrum (WPS) is employed; the simple equation of the WPS is shown below;  2 WPSp ðk; f Þ ¼ Wp ðk; f Þ : ð5Þ As the main tool of this study, the wavelet coherence method is employed to investigate any correlation or causality between economic risk and sovereign credit risk in Turkey, while taking frequency and time-based causality approaches into account at the same time. However, before performing wavelet coherence, the cross wavelet transform (CWT) of the time series has to be done; the CWT equation is as follows: Wpq ðk; f Þ ¼ Wp ðk; f ÞWq ðk; f Þ;

ð6Þ

where Wp(k,f ) and Wq(k,f ) represent the CWT of the two variables p(t) and q(t), respectively. According to Torrence and Compo (1998), the equation of the squared wavelet coherence, R2 ðk; f Þ , is presented in Equation (6).   1  C f Wpq ðk; f Þ 2 2 R ðk; f Þ ¼   ð7Þ 2   2 C f 1 Wp ðk; f Þ C f 1 Wq ðk; f Þ

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In Equation (6), time and the smoothing process over time are represented by c, with 0  R2(k,f )  1. Whenever R2(k,f ) approaches 1, it indicates either that the time series indices are correlated or a causal linkage exists among the time series indices at a particular frequency, bounded by a black line and painted in a red color. However, when the value of R2(k,f ) gets close to 0, it indicates that there is no correlation or causality among the time series indices. The value of R2(k,f ) does not provide any information about the sign of the relationship. Therefore, “Torrence and Compo (1998) postulated a means by which to detect the wavelet coherence differences through indications of deferrals in the wavering of two time series” (Pal & Mitra, 2017, pp. 232e233). The equation of the wavelet coherence difference phase is constructed as follows:

 1  ! Wpq ðk; f Þ 1 L C f

  ; fpq ðk; f Þ ¼ tan ð8Þ O C f 1 Wpq ðk; f Þ where L and O denote an imaginary operator and a real part operator, respectively. In this study, we have also employed the TodaeYamamoto causality test, which was developed by Toda and Yamamoto (1995) to investigate a time domain causality linkage between sovereign credit risk and economic risk in Turkey. Contrary to the traditional Granger causality test proposed by Granger (1969), Toda and Yamamoto (1995) developed a modified Wald test statistic (MWALD) to overcome bias and spurious models based on an augmented VAR approach. Moreover, the technique can be employed if the sovereign credit risk and economic risk variables are integrated of order zero, one, or two and if the sovereign credit risk and economic risk variables are not cointegrated. In addition to the TodaeYamamoto causality test, we have also conducted a Gradual Shift causality test, which was developed by Nazlioglu, Gormus, and Soytas (2016). The test is also called the “Fourier TodaeYamamoto causality test”, and it permits the use of structural breaks, including gradual and smooth shifts, in the causality analysis. Thus, the test takes into account breaks using a Fourier approximation in a Granger causality analysis. As the time series variables used in the present paper contain structural breaks, a Gradual shift causality test is likely to provide results superior to those of the traditional causality test. By taking structural shifts into account, Nazlioglu et al. (2016) define the VAR (p þ d) model as yt ¼ b1 ðtÞ þ b2 yt1 þ … þ bpþd ytðpþdÞ þ et

ð9Þ

where the intercept term b1(t) is the functions of time and indicates any structural shifts in yt. The Fourier approximation for the traditional TodaeYamamoto causality test is applied by Nazlioglu, Gormus, and Soytas (2019) to capture structural shifts as a gradual process, as follows.    X  n n X 2pkt 2pkt b1 ðtÞ¼b ~ 1þ f1k sin f2k cos þ ð10Þ T T k¼1 k¼1

Please cite this article as: Kirikkaleli, D., & Gokmenoglu, K. K., Sovereign credit risk and economic risk in Turkey: Empirical evidence from a wavelet _ coherence approach, Borsa Istanbul Review, https://doi.org/10.1016/j.bir.2019.06.003

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where n is the number of frequencies, and f1k and f2k measure the size and shift of the frequency, correspondingly. By substituting equation (10) in equation (9), the following equation is constructed;    X  n n X 2pkt 2pkt f1k sin f2k cos yt ¼ b 1 þ þ þ b2 yt1 T T k¼1 k¼1 þ … þ bpþd ytðpþdÞ þ et ð11Þ “A single Fourier frequency mimics a variety of breaks in deterministic components regardless of date, number, and form of breaks. One, therefore, can use a single frequency component and hence define” (Nazlioglu et al., 2019, p. 113) b1(t) as;     2pkt 2pkt b1 ðtÞ¼b ~ 1 þ f1k sin þ f2k cos ð12Þ T T where k symbolizes the frequency for the approximation. By substituting equation (12) in equation (9), the following equation is constructed;     2pkt 2pkt yt ¼ b1 þ f1k sin þ f2k cos þ b2 yt1 þ … T T þ bpþd ytðpþdÞ þ et

ð13Þ

As mentioned clearly by Nazlioglu et al., 2019 (page 113), “If both the TodaeYamamoto and the Fourier TodaeYamamoto test reject the null hypothesis of nontransmission, this result would provide a robust conclusion of mean-transmission.” 5. Empirical findings In this study, to explore the order of integration of the time series variables, namely economic risk and sovereign credit risk, the ADF, DF-GLS, and Ng-Perron tests are employed. It is assumed that the time series do not contain any structural breaks when applying these tests. Table 2 presents the results of these tests, which show that the null hypothesis that the ER and SR variables do not contain a unit root at their level was rejected with the model with both intercept and trend. However, as an initial difference, the time series variables seem stationary, meaning that the order of integration of the ER and SR variables is one, I(1). To take into account possible structural break in the time series variables, the ZivoteAndrews unit root test is also employed in the present study. Although the ER and SR variables have a structural shift in the third quarter of 1997 and the first quarter of 2001, respectively, structural breaks do not significantly affect the unit root behavior, as the order of the integration of the variables is one. In the next step, to capture the vulnerability in the economic risk and sovereign credit risk in Turkey over the period 1992Q1e2016Q2, a wavelet power spectrum technique is performed. The outcomes of the wavelet power spectrum for

Table 2 Unit root test. ER Panel A: Unit-root tests in levels ADF 1.685 MZa 3.091 MZt 1.049 MSB 0.339 MPT 7.637 DF-GLS 1.092 Zivot-Andrews 4.622B SB 1997Q3 Panel B: Unit-root test in first differences ADF 9.166** MZa 82.175** MZt 6.409** MSB 0.078** MPT 0.298** DF-GLS 9.206** Zivot-Andrews 8.110**

SR 0.933 3.219 1.021 0.317 7.367 0.969 4.066 2001Q1 6.695** 40.460** 4.497** 0.111** 0.605** 6.712** 8.069**

Notes: ** and * indicate rejection of null hypothesis at 1% and 5% significance level, respectively. MZa, MZt, MSB, MPT are the tests of Ng and Perron (2001).

the time series variables are presented in Figs. 1 and 2. In this study, a scale of 32 periods is selected, as the dataset covers the period 1992Q1e2016Q2 (98 quarterly observations). The cone of influence is represented by the white cone-shaped line, indicating an edge below which the wavelet power is affected because of discontinuity, and the thick black shape shows a 5% significance level determined by Monte Carlo simulations. As can be observed clearly in Fig. 2, between 2000 and 2003, at 4 and 8 scales (4 and 8 quarters), there was significant vulnerability in ER in Turkey, which was struggling during one of the most destructive economic crises experienced in its history. Moreover, significant volatility is detected in economic risk at 4 scales during the period of global economic crisis at the end of 2008. However, significant volatility dissipates within a few quarters. Fig. 3 shows the wavelet power spectrum for the variables of sovereign credit risk in Turkey.

Fig. 1. Power spectral for ER.

Please cite this article as: Kirikkaleli, D., & Gokmenoglu, K. K., Sovereign credit risk and economic risk in Turkey: Empirical evidence from a wavelet _ coherence approach, Borsa Istanbul Review, https://doi.org/10.1016/j.bir.2019.06.003

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Table 3 TodaeYamamoto and gradual shift causality tests. TodaeYamamoto Causality Test Direction of Causality

Lag

MWALT

Prob.

SR / ER ER / SR

2 2 Lag 3 3

11.357 0.782 MWALT 9.755 2.512

0.003** 0.676 Prob. 0.021* 0.473

Direction of Causality

Lag

F-stat

Prob.

SR / ER ER / SR

2 2 Lag 3 3

10.065 0.738 F-stat 7.913 2.510

0.006** 0.691 Prob. 0.048* 0.691

SR / ER ER / SR Gradual Shift Causality Test

SR / ER ER / SR

Fig. 2. Power spectral for SR.

Fig. 3. Wavelet coherence between ER and SR.

As can be seen in the figure, the size of the significant vulnerability in sovereign credit risk in Turkey is considerably greater than is that of economic risk, demonstrating that the decisions made by the credit agencies regarding Turkey's sovereign credit score were aggressive. It can be seen in Fig. 3 that, at the low frequency, a high variation is detected over the sample period of 1992Q1e2016Q2. To explore the causal linkage or correlation between sovereign credit risk and economic risk in Turkey, the wavelet coherence technique is applied. Fig. 3 shows the findings from the technique, which provides information about the timefrequency dependence of sovereign credit risk and economic risk in Turkey. The existence of right-up, straight-up, and leftdown arrows within the thick black shape indicates that sovereign credit risk leads to economic risk in Turkey at different frequencies over the period 1992Q1e2016Q2. This result shows how sovereign credit risk is an essential factor for

Note: / indicates the direction of causality. While the optimal lag of the model is selected as three by Hannan-Quinn information criterion, Akaike information criterion, and Final prediction error, Schwarz information criterion selected two as an optimal lag for the model. Therefore, both model results based on lags 2 and 3 are presented in Table 3. ** and * denote statistical significance at 1% and 5% levels, respectively. The time series variables are used at the first differences in the TodaeYamamoto Causality and Gradual Shift Causality tests, while the variables are used in levels in the wavelet coherence approach, as wavelet analysis works well if data are noisy, nonstationary, and have several volatility shifts and structural breaks.

predicting economic risk in Turkey, particularly in the shortand medium-terms. In Fig. 3, there is a significant causality running from sovereign credit risk to economic risk: (i) between 1997 and 2002 at 16 scales, (ii) between 1999 and 2003 at 8 scales, and (iii) between 2007 and 2012 at 4 and 8 scales. These periods involve the 1998 Russian crisis, the 2000 domestic banking crisis, the 2001 economic crisis, and the 2007e2008 global crisis. In other words, the results support the assertion that the decisions of prominent global credit agencies significantly affected the main macroeconomic dynamics in Turkey, in addition to the effects of the domestic and global crisis. As a robust causality check and to investigate the time domain causal linkage between sovereign credit risk and economic risk in Turkey for the period 1992Q1e2016Q2, the TodaeYamamoto and Gradual Shift causality tests are used in this study. The outcomes from these tests are reported in Table 2. It is worth mentioning that, whereas the Gradual Shift causality test takes structural breaks into account, the TodaeYamamoto causality test assumes that the intercept parameters are constant over time. According to the TodaeYamamoto causality test results, the null hypothesis that sovereign credit risk does not cause economic risk in Turkey can be rejected, implying that vulnerability in sovereign credit risk leads to vulnerability in economic risk. This finding reveals the importance of sovereign credit risk in predicting the economic risk for Turkey. The results of the Gradual Shift causality test confirm the causal links running from sovereign credit risk to economic risk in Turkey. Furthermore, the outcomes of the Gradual Shift causality test verify the robustness

Please cite this article as: Kirikkaleli, D., & Gokmenoglu, K. K., Sovereign credit risk and economic risk in Turkey: Empirical evidence from a wavelet _ coherence approach, Borsa Istanbul Review, https://doi.org/10.1016/j.bir.2019.06.003

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_ D. Kirikkaleli, K.K. Gokmenoglu / Borsa Istanbul Review xxx (xxxx) xxx

of the uni-directional causal linkage while taking structural breaks into account. To obtain uni-directional causal linkage is rational and in line with the arguments of El-Shagi and von Schweinitz (2018), who underline that a credit rating downgrade of CRAs can lead to unrest in the markets and weaker macroeconomic fundamentals. Apart from the direct effect of credit rating changes to the economy, downgrading credit rating in Turkey is likely to cause deterioration of the financial market, which contributes negatively to the macroeconomic fundamentals through the financial market. It is also wellknown that emerging markets, like Turkey, are likely to be affected more deeply by downgrading the credit than are developed markets. 6. Conclusion Despite the existing extensive literature about the aspects of credit ratings and credit rating agencies (CRAs), the timefrequency dependence of sovereign credit risk and economic risk for emerging markets has not been investigated thoroughly. Our study aims to fill this gap by examining the relationship between credit ratings and economic risk for Turkey as an emerging market using wavelet analysis, which allows the investigation of shorter time periods, independently from longer time periods. We have also used the TodaeYamamoto causality and Gradual Shift causality tests for robustness. The wavelet analysis for the period 1992Q1e2016Q2 reveals that (i) the period of vulnerability of sovereign credit risk in Turkey is significantly longer than is that of economic risk, indicating that the credit score changes made by the CRAs for Turkey were aggressive over the selected sample period; (ii) there is an uni-directional causality running from sovereign credit risk to economic risk for the periods (a) 1997e2002 at 16 scales, (b) 1999e2003 at 8 scales, and (c) 2007e2012 at 4 and 8 scales; and (iii) the importance of sovereign credit risk in predicting the economic risk for Turkey is confirmed by the outcomes of the TodaeYamamoto and Gradual Shift causality test, meaning that sovereign credit risk significantly causes economic risk in Turkey. It is important to mention that this study is beneficial for policymakers as well as investors, as it observes the comovement of sovereign credit risk and economic risk in Turkey. In both the short- and the medium-term, policymakers need to be aware of the potential effects of rating and outlook announcements on the Turkish economy. This is because the score and outlook changes of the CRAs for Turkey are likely to change the macroeconomic fundamentals in Turkey. Additionally, in the present study, the observation of greater vulnerability to sovereign credit risk in Turkey relative to economic risk over the period 1992e2016 reveals that the score and outlook changes of the CRAs for Turkey deserve to be criticized strongly. Although this research has presented robust empirical findings, further studies should be conducted for other emerging markets around the world.

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Please cite this article as: Kirikkaleli, D., & Gokmenoglu, K. K., Sovereign credit risk and economic risk in Turkey: Empirical evidence from a wavelet _ coherence approach, Borsa Istanbul Review, https://doi.org/10.1016/j.bir.2019.06.003