Spillovers Between Institutional Interactions Index Market Risk and Return Case of Turkey 2007-2020

Abstract

Keywords: Arch-garch model; bist100 ındex; cds; exchange rate; relations with institutions

1. Introduction

In recent economics literature, institutionalists have argued that economies with inclusive institutionalist structures are better able to sustain economic performance and achieve stability, which leads to economic growth. Acemoğlu D. & Robinson J.A. defined sufficiently centralized and pluralistic political institutions as “inclusive political institutions” and defined institutions that fail to meet either of these conditions as “exploitative institutions”. The authors highlighted the powerful synergy between economic and political institutions and argued that exploitative political institutions will concentrate power in the hands of a narrow elite who will take few steps to limit that power. In such cases, the elites generally structure economic institutions to exploit the resources of the rest of society. The present authors assert that exploitative economic institutions naturally accompany exploitative political institutions. Inclusive institutions, which widely distribute power, operate to remove the economic institutions that seize the sources of the majority, impose barriers to entry, and manipulate market operations so only a small segment of society can benefit. Political and economic institutions, which ultimately have choices, can be inclusive and stimulate economic growth. Naturally, economic growth and technological change will create new firms through “creative destruction”. This change is a change from old to new. As a matter of course, the old firms will resist this change. Since inclusive institutions allow this transformation, they can become targets in the political arena. Conversely, institutions can be exploitative, which means they can become a formidable obstacle to economic growth and technological change (Acemoğlu D. & Robinson J.A., 2015, p. 80-84). As can be seen in the Appendix, in Figure 5, the Political Stability Index (PSI) published daily by the PSI consists of various sub-indices, one being Institutional Interactions. The index consists of the relationships between the government and higher judicial organ, the presidency and the government, the government and the military bureaucracy, the government civil bureaucracy and autonomous economic institutions, and the relations with international institutions. Lower index scores indicate deterioration in the relationships among related institutions, whereas higher scores indicate that the relations among institutions are compatible with

the legal and institutional legislation within the framework of constitutional principles and serve to generate stability in terms of institutional experience, accumulation, and independence. This study focuses on the PSI sub-index of Institutional Interaction to examine the interaction between credit default swap (CDS), foreign exchange (FX), and Borsa Istanbul (BIST), or Istanbul Stock Exchange. By examining this interaction, the function imposed on institutions by institutional economists can be tested with three important indicators. As known, in unstable economies, CDSs rise, and significant volatility is experienced in the Stock Exchange Index and FX rates. In these cases, economic stability deteriorates to a great extent. On the other hand, high CDS rates limit the inflow of external sources and increase its costs as well. Excess volatility in the FX rate negatively affects growth in many respects, such as causing inflation and uncertainty.

2. Literature

In the literature review, there was no study found on the Institutional Interaction that looked specifically at the relationship between CDS, Stock Market Index, and FX rate. However, some studies that explore the concept of Institutional Interaction and its impact on economic performance, such as growth. The study will therefore be the first attempt of its kind. Institutional Interaction, one of the determinants of political stability, is a sub-index of the Political Stability Index. Therefore, different studies addressing political stability and the relationship between CDS, the equity market, and/or FX basket were guiding for the present study. Regarding the related works, Artan and Hayaloğlu (2014) conducted a co-integration analysis of the relationship between institutional structure and economic growth in Turkey for the period of 1972-2009 based on real GDP, political liberties, gross fixed capital formation, public consumption expenditure, foreign trade (the sum of exports and imports), and inflation rate. The results from the study showed that while political freedom, taken as an indicator of the level of institutional structure, had a positive effect on long-run economic growth, there was no relationship found between institutional structure and economic growth in the

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short run. Davis, L. S. (2010) developed a formal model to investigate the relationship between institutional quality, existing property rights, and institutional flexibility, and the ability to develop new institutions, relating these aspects of institutional structure to dynamic economic performance. This model attempts to advance this convergence by developing a theory of growth that takes into account the economic historical distinction between institutional quality and institutional flexibility. The analysis suggests that institutional flexibility plays a central role in economic growth. The model is also used to highlight the relatively temporary gains from reforms on institutional quality, an aspect of the analysis that may prove useful to understand prolonged periods of stagnation in developed countries and the fragility of growth experienced in some countries. According to the study findings, an increase in institutional quality lowers market transaction costs, producing an immediate but short-lived increase in the rate of economic growth. In contrast, an increase in institutional flexibility results in a delayed but permanent increase in economic growth. Knack, S. and Keefer, P. (1995) aimed to advance this convergence by developing a growth theory that takes into account the historical economic difference between institutional quality and institutional flexibility. The study examined the impact of property rights on economic growth using indicators provided to potential foreign investors by country risk assessors. The indicators included evaluations of contract enforceability and risk of expropriation. Results from the research suggested strong supports for three hypotheses. First, political violence and Gastil’s index of political and civil liberties indicators (Freedom House Index) do not adequately represent the quality of institutions that protect property rights. More accurate indicators are required to properly explain the impact of institutions. Second, institutions that protect property rights are crucial to economic growth and investment, and the impact of institutions on growth continues even after the investment is controlled. This indicates that the security of property rights affects not only the size of the investment but also the efficiency of input allocation. Third, stronger evidence for controlled convergence emerges when institutions are regulated. Jensen, N. (2008) examined the relationship between democratic institutions and foreign direct investment (FDI) flows. In the study, qualitative data collected from 28 interviews with investors, political risk insurers, plant location consultants, and international lawyers who represent multinational companies and price data collected from political risk insurance companies were used to directly test how local political institutions affect the premiums paid by multinational companies for state expropriation and contract disputes. According to the study results, democratic institutions have lower risk levels due to the restrictions imposed on executives in democratic regimes. In other words, democratic regimes appear to reduce risks for multinational investors,

particularly by increasing restrictions on executives. Scully, G. W. 1988 analyzed the compound growth rates of production per capita and Farrell-type efficiency measures for 115 market economies for the 1960-80 period by comparing them against measures of political, civil, and economic liberty. It was found that the institutional framework had significant and profound effects on the efficiency and growth rate of economies. Politically open societies subject to the rule of law, private property, and market allocation of resources, grow at two and one-half times the rate and are two and one-half times as productive as societies in which these freedoms are restricted. Easterly et al. (2006) examined the effect of social cohesion level on institutional structure and the relationship between institutional structure and economic growth. The variables applied to the regression analysis included the concept of social cohesion, the number of ethnic groups (determined based on the language spoken), the Gini coefficient, the middle-class share in income, the measure of trust, income per capita, and income growth. To determine the institutional structure, in addition to the Freedom House’s indices of political rights and civil liberties, variables such as government accountability and effectiveness, quality of bureaucracy, and the rule of law were used. According to the study results, more social cohesion leads to better institutions, and better institutions in turn lead to higher growth. Yıldırım, S. 2010 examined the effect of institutions on economic growth by considering the dynamic structure of institutions. In the study, a panel data analysis was conducted using the economic freedoms index, the political rights and civil liberties index, education, distance from technology frontier, and GDP. According to the results of the study, countries with better institutional structures have higher growth rates, and an increase in the economic freedom or the political and civil rights of citizens in countries that are far from the technology frontier slows these countries’ growth rates; in other words, the position of a country in relation to the technology frontier indicates how the institutional structure will affect the country’s growth rate. In a country far from the technology frontier, an improvement in institutional structure increases the growth rate less than that of a country closer to the technology frontier. These findings indicate that the farther a country in question is from the technology frontier, the more the effect of political and civil rights and economic liberties slows down the growth rate. It was stated that the effect of the institutional structure (its ability to protect rights and increase freedom) on a country’s growth rate is high only in countries close to the technology frontier. Şahin, E. E. and Özkan (2018) applied the panel data analysis method to examine the relationship between the CDS, BIST100, and FX rate for Turkey using the monthly data from the period of 2012-2017. According to the study findings, a bi-directional causality relationship was found between the BIST100 index and CDS, while no causality rela-

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tionship was found between the BIST100 index and FX rates. Yamak T. (2017) examined the effect of Institutional Quality Factors on Economic Growth in the Gulf countries. To measure economic growth, the study applied the flexible generalized least squares method (FGLS) and panel data analysis, using the inputs of real income per capita, gross fixed capital formations, central government final consumption expenditures, foreign trade volume, life expectancy at birth, and institutional quality indicators for the period 1995-2014. According to the results of the study, in the six Gulf Arab Countries, a positive and significant relationship was identified between economic growth and the institutional quality variables of “Expression and Accountability” and “Control of Corruption”, while a negative and significant relationship was identified between economic growth and the institutional quality variables of “Rule of Law” and “Regulatory Quality”. There was no significant relationship found between economic growth and the institutional quality variables of “Political Stability and Absence of Violence” and “Government Effectiveness”. In our study, we will utilize VAR-VECH-TARCH models to analyze the interactions and exhibit spillovers in a holistic approach.

This method is extended from VAR–GARCH, which is proposed by Ling and McAleer (2003). An examination of the conditional returns and conditional volatility can be conducted with meaningful estimated parameters via this structure. This method is composed of two parts, namely the VAR model and asymmetric VECH–TARCH model, which are used to explore the joint evolution of conditional returns and volatility spillovers between different financial markets. First, the VAR model extends the univariate autoregressive (AR) to vector autoregressive (VAR) by internalizing the related variables into endogenous variables to examine the contagion and spillover effect between major financial markets. The basic mathematical expression of the VAR model is as follows:

3. Methods

Usually, financial data suggest that some time periods are riskier than others. The goal of such models is to provide a volatility measure that can be used in financial decision making with risk analysis, portfolio selection, and derivative pricing (Engle 1982, Engle and Ng 1993). An important characteristic of such asset prices is that “bad” news has a more persistent impact on volatility than “good” news has. Most of the stocks have a strong negative correlation between the current return and future volatility. This is called “the leverage effect”, which refers to the well-established relationship between stock returns and both implied and realized volatility. A standard explanation ties the phenomenon to the effect a change in market valuation of a firm’s equity has on the degree of leverage in its capital structure, with an increase in leverage producing an increase in stock volatility (Figlewski and Wang 2000). Glosten et al. (1993) showed how to allow the effects of good and bad news to have different effects on volatility. In a sense, εt−1 = 0 is a threshold such that shocks greater than the threshold have different effects than shocks below the threshold. Consider the threshold–GARCH (TARCH) process: (1) where dt−1 is a dummy variable that is equal to one if εt−1 < 0 and is equal to zero if εt−1 ≥ 0. The intuition behind the TARCH model is that positive values of εt−1 are associated with a zero value of dt−1. Hence if εt−1 ≥ 0, the effect of εt−1 when εt−1 < 0, dt−1 = 1, and the effect of shocks on ht is negative shocks an εt−1 shock on ht is will have larger effects on volatility than positive shocks.

(2) where Rt refers to the value of endogenous variables vector at time t, C is the constant vector, matrix A is the estimated coefficients and k is the lag operator. Residual vector εt is assumed to be normally distributed with a zero mean and constant variance where the market information available at time t−1 denoted as dt−1. The lag order of (k) VAR structure is decided via AIC criterion, FPE criterion and LR. In this approach we incorporate a three-dimensional model to examine the news spillover between different markets. Suppose that our model structure is as follows:

Equation (3) specifies the relation between the residual term εi.t and the conditional variance hi.t. υi.t, which is normally distributed with a zero mean and constant variance. α, β are the coefficients. Hi,t represents the conditional variance–covariance matrix, C represents the lower triangular matrix, A and B are square arrays. If CTC is positive, then it is almost positive.

where h11,t, h22,t, h33,t in the matrix Ht represent the conditional variances. Matrix A is the ARCH coefficients of the model, a11, a22, a33 represent the ARCH effect while Matrix B is the GARCH coefficients of the model, b11, b22, b33 are the GARCH effect. In consideration of the asymmetric effect, diagonal VECH is:

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(6) where the conditional variance–covariance equation of a bivariate (VECH) TARCH model has the following form: (7) where the last term on the RHS of Equation (7) depicts the asymmetries. In this context, the diagonal bivariate VECH model is as follows:

The coefficient a11 refers to the ARCH process in the residuals from asset i which depicts the fluctuations of the assets reflecting the impact of external shocks on fluctuations. The ARCH effects measure the short-term persistence while the GARCH effect measures the long-term persistence. The a33 coefficient represents the ARCH process in the second asset residuals and the parameters between asset i and asset j. The calculation of the time-varying beta coefficient is done as

(11) where the symbol ^ indicates the estimated values of conditional variance.

4. Data And Empirical Results

The effect of relationships with institutions on the equity market and FX basket1 in Turkey was examined using the daily data for the period between January 1, 2007 and March 30, 2020. Models were also expanded with CDS returns, as the CDS2 data had a significant impact on both markets. Descriptions of the series used in the study are presented below in Table 1. We again narrowed the period between March 15, 2011, and March 30, 2020, to analyze the impact of Arab Spring3 which is a major milestone for II. Considering its impact on Turkey, we started the Arab Spring period with the beginning of pro-democracy protests in Syria. The Augmented Dickey Fuller (ADF) was used to test the stationarity of the variables before creating the model. The inputs of the GARCH model were then determined. The descriptive statistical information of the data is presented in Table 2. In examining the kurtosis and skewness values, the high Jarque-Bera values indicated that the data are not normally4,5 distributed. The ADF test results indicat-

Exchange rate is taken as a basket. ((Euro+Dollar)/2) and its return was calculated

5-year CDs yield returns of Turkey credit default swap series

The foreign exchange basket is generally used when the local currency is closely related to foreign currencies. For example, in Turkey, the US dollar gains weight in trade and external borrowing, whereas the Euro is dominant in some service areas and trade. In such a case, the value of the Turkish Lira against the US dollar and Euro can be easily measured by creating a FX basket. CDS (Credit Default Swap) is a financial instrument that eliminates the bond risk that a person who is holding a financial instrument will not be paid for a certain amount of their receivable at the end of maturity. There is a cost of 1% for every 100 CDS base points calculated for countries. Countries with high CDS premiums and the institutions in these countries bear higher costs to meet their borrowing requirements. Therefore, the CDS premium is an important, up-to-date, and reliable indicator for countries. The Arab Spring was a series of anti-government protests, uprisings, and armed rebellions that spread across much of the Arab world in the early 2010s. It began in response to oppressive regimes and a low standard of living, starting with protests in Tunisia. From Tunisia, the protests then spread to five other countries: Libya, Egypt, Yemen, Syria, and Bahrain, where either the ruler was deposed (Zine El Abidine Ben Ali, Muammar Gaddafi, Hosni Mubarak, and Ali Abdullah Saleh) or major uprisings and social violence occurred including riots, civil wars, or insurgencies. Another way to determine whether the data distribution is normally distributed is to look at the mean, mode, and median values. These values coincide in normal distribution. The closer these statistics are to each other, the closer the distribution is to a normal distribution. The distribution skews the further these values diverge from one another. Another way to test for normal distribution is to look at the skewness and kurtosis coefficients. The skewness coefficient is 0 at normal distribution. A negative skewness coefficient indicates a right-skewed distribution, while a positive skewness coefficient indicates a left-skewed distribution. The kurtosis coefficient is 3 in normal distribution. A positive kurtosis coefficient indicates that the distribution has thicker tails, while a negative kurtosis coefficient indicates a light-tailed distribution. The Ergenekon and Sledgehammer cases became instrumental for the political and ideological transformation that the AKP had been planning by changing the institutional power balances (Saraçoğlu and Yeşilbağ, 2015: 991), especially between the elected political power and the military. Turkish military played a key role in the nation building process; hence the modernization era was also stimulated by the Army. It can be argued that, having a role like this, the military elites from the very beginning of the Republican era up to present, have not been experiencing any appreciable difficulties in placing themselves in the political life. Unlike its counterparts, the Turkish Army has a considerable amount of political and institutional autonomy which ultimately leads to emphasize its role in guarding the state from “internal enemies” (Burak, 2011). Sledgehammer (Balyoz) case in 2010, which claimed that “alleged coup plans were prepared by military officers” and accordingly indicted retired and serving generals including the then Chiefs of the land forces, of the navy, and of the air forces for “establishing a structure outside the military hierarchy and attempting to overthrow the government and constitutional order” (European Commission, 2010: 7). Consequently, the 2011 Arab Spring marked another turn in the Turkish foreign policy. The Arab Spring revealed several contradictions, constraints as well as opportunities for Turkish foreign policy, all of which are of key relevance both to Turkey and to its transatlantic partners. This process enabled Turkey to end the conflict of institutional power in favor of politicians whether it became good or bad is still arguable and this started the rally of Institutional Interaction Index after 2011.

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Table 2. Descriptive Statistics Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera Probability ADF Test Level

Rbist100

Notes: Between parenthesis: p-values. The number of observations is 3523. ADF Tests refer to Augemented Dickey Fuller test for the presence of unit root for long differences (returns).

ed that all data are stable. The descriptive statistical values of the variables are presented below in Table 2. Figure 1 presents the graphs of the series that constitutes the data set. According to the graphs, after the rapid recovery it experienced after 20116, the Institutional Interaction Index7 has been on a downward trend since 2017. The FX basket, on the other hand, started on a highly accelerated increasing trend after 2018, with the CDS premiums following this movement in the same period. After 2018, CDS premiums and the BIST100 index moved almost the same, and the

correlation between them increased significantly. This points to the pressure in the markets created by the deterioration in the relations with institutions, in parallel with the volatility in CDSs because of the fluctuations experienced in the stock market, the FX rate, and the government-higher judicial organ, presidency-government, government-military bureaucracy, government civil bureaucracy-autonomous economic institutions, and relations with international institutions after the Pastor Brunson crisis. The daily logarithmic returns, rt, for use in the models

Figure 1. The Graphs on Returns of the BIST100, CDS, FX Rate, and the Institutional Interaction Index. 7 8

Detailed methodology of the Index is represented in the Appendix part Although the Institutional Interaction Index is not an investable asset like BIST100, FX, or CDS, the rate of increase of the index was calculated as yield due to the way it is used in the GARCH models, and therefore it was included in the analysis.

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Figure 2. The Graphs on Returns of BIST100, CDS, FX, and the Institutional Interaction Index were derived from the following calculation8:

Figure 2 presents the return graphs of the BIST100, CDS, FX, and Institutional Interaction Index. FX yields climbed to record levels in 2018 due to the foreign exchange crisis, while CDSs were impacted by concerns over difficulties in paying external debt with the loss in the value of the Turkish Lira. The effects of the break in the Institutional Interaction Index in 2012 due to Arab Spring contagion are clearly seen in the charts. We constructed two different model systems with three different VAR9 system specifications which are equity markets model (Model 1) and market risk (Model 2). In this context, VAR System Specification for Model 1:

This model system represents the relationship between equity markets (BIST 100), institutional interactions (II), and market risk (CDS). According to the results represented in Table 3 Panel A, the own conditional ARCH effects (aii) are significant at the %1 level for all variables which indicates that all variables are influenced by the volatility of their own dynamics. There is no significant volatility spillover over effect among II and CDS in the short term since a23 is not statistically significant even at 10% level. How9

ever, volatility spillover over effect among BIST100-II and BIST100-CDS is detected in the short term since a12 and a13 are statistically significant at 1% and %5 level respectfully. Moreover, the own conditional GARCH effects (bii) in matrix B are all significant at %1 level for all related markets in Model 1. Consequently, for the long-term volatility spillovers, the volatility spillover exists only between BIST100 and CDS as only b13 is significant at %1 level. As a result, we can conclude that a volatility spillover between equity markets and market risk strongly exists in the long term, which is consistent with the modern finance theory. Finally, the D matrix refers to the asymmetric coefficients. According to the results only d11 and d33 are significant at %1 and %5 level respectfully. Positive coefficients mean that good news increases volatility. The coefficients of d13 is positive, which indicates that good news to equity market increases the volatility while the coefficients of d33 is negative, which indicates that bad news to market risk increases the volatility. Last but not least, asymmetric volatility does not exist among all variables in Model 1. VAR System Specification for Model 2:

In Model 2, the relationship between foreign exchange basket (FX), institutional interactions (II), and market risk (CDS) are analyzed. The own conditional ARCH effects (aii) are significant at the %1 level for all variables. These results indicate that all variables are influenced by the volatility of

Since we detected cointegration even with higher level of lag numbers for all variables we used VECM models with two lags. The lag structure was decided due to lag length criteria test of Eviews.

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their markets. Besides, there is no significant volatility spillover over effect between FX-II and CDS-II in the short term since a12 and a23 are not statistically significant even at the 10% level. However, a13 is significant at 5% level leading a volatility spillover over effect between FX and CDS. Moreover, the own conditional GARCH effects (bii) in matrix B are all significant at %1 level for all related markets in Model

2. Consequently, for the long-term volatility spillovers, the

volatility spillover between FX-II and FX-CDS are all significant at 1% level that are b12 and b13. As a result, we conclude that volatility spillover between mentioned markets exists in

the long term. Finally, the D matrix refers to the asymmetric coefficients. According to the results only d11 and d33 are significant at %1 level and negative coefficients mean that bad news increases the volatility. The coefficients d11 and d33 are negative which indicates that bad news to FX basket and the market risk increases the volatility. Finally, asymmetric volatility exists only between FX and CDS in Model 2. As mentioned earlier since Arab Spring is a milestone for Institutional Interaction Index, we also applied all models for a second period in Table 3 Panel B. In this period, we can see that volatility spillover over effect occurs among CDS and II

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in short term in Model 1. Also, in the long-term volatility spillover over effect occurs among BIST100-II and CDS-II in Model 1. In Model 2 in the long-term only volatility spillover over effect occurs among CDS and II. If we summarize the results, we can conclude that volatility spillover between institutional interaction index and equity market exists in the short and long term. For the second period results, we see volatility spillover between insti-

tutional interaction index and CDS also exists in both the short and long term. Also, volatility spillover between institutional interaction index and equity market is still valid in both short and long term. In Model 2 we observe volatility spillover between II and FX basket only in short term for all period results. In the second period results volatility spillover between the II-FX basket and II-CDS occurs in the long period as well. Finally, we can show the movement of

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the conditional covariance and the conditional correlation of II, FX, BIST 100 and CDS in Model 1 and 2 for overall period according to Figure 3 and 4. Impact of 2011 on II is quite visible in these graphs.

FX basket only in short term for all period results. In the second period results volatility spillover between the II-FX basket and II-CDS occurs in the long period as well. As the bottom line we can sum up the results as II has more impact on markets and markets risk create volatility spillover impact on both equity market and FX market obviously. The results from this study support institutionalist approaches. As the relations with institutions deteriorate, BIST100 and FX rate volatilities, that is, instability, increases due to the increase in market risks. Unquestionably, albeit ironically, volatility investors derive profits from these volatilities.

5. Conclusion

One of the key components of political stability is relations with institutions, which refers to the relationships between the government and higher judicial organ, government and presidency, government and military bureaucracy, government civil bureaucracy and independent economic institutions, and relations with international institutions. The importance of institutions for economic development and stability has been thoroughly discussed by both the old and new schools of institutional economics, who developed theses against the Classical and Neo-Classical paradigms. The novelty of this paper is the Institutional Interaction Index which allows us to measure the relationships between government and higher judicial organ, presidency and government, government and military bureaucracy, government civil bureaucracy and autonomous economic institutions, and relations with international institutions in a daily base which allows us to quantify and incorporate II into time series model. The higher the Institutional Interaction Index, the greater the tendency towards stability, whereas the lower this index, the greater the deterioration in stability. As stated in the institutionalist approaches, the involvement of institutions is important for economic growth and economic stability. In the study, the relationship between the Institutional Interaction Index, a sub-index of the Political Stability Index, and BIST100, FX basket rate, and CDS was analyzed, and we can conclude that volatility spillover between institutional interaction index and equity market exists in the short and long term. For the second period results, we see volatility spillover between institutional interaction index and CDS also exists in both short and long term. Also, volatility spillover between institutional interaction index and equity market is still valid in both the short and long term. In Model 2 we observe volatility spillover between II and

PSI And The Computation Methodology

The political participation in democratic societies, procures decision making and implementation about comprehensive, thoroughly, and vital issues. In the regimes where political participation is not adequate, it drifts apart from being inclusive in decision making and applying and maintaining social order becomes harder. Stability means, when looked at its Arabic origin, “the determination of the decisions”. Therefore, “Political Stability” states determination about the implementation of the decisions. In the regimes where there is no political order, making, implementing, updating, and extending the decisions is quite hard. Financial markets rapidly evaluate the value of political development and stability, and they choose the shortest way for adapting to their expectations. Political order and stability play a part in, not only the financial markets’ but also in all the economic actors’ future decisions. Within this framework, the gaugeability of Political Stability and effective usage of it becomes important. The foundations of The Political Stability Index that is calculated based upon the case series are primarily the rule of law and legal security principles.

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