DARALMA VE GENİŞLEME DÖNEMLERİNDE ULUSLARARASI PORTFÖY YATIRIMLARI NASIL ETKİLENİYOR? TÜRKİYE ÖRNEĞİ

Türkiye Pay piyasası, uluslararası sermaye piyasalarındaki likiditeden en çok etkilenen piyasalardan birisidir. Amerikan Merkez Bankası (FED) ve Avrupa Merkez Bankası (ECB)’nın vermiş olduğu para politikası kararları gibi küresel ekonomiyi etki altına alan çok sayıda değişken ile ülkeye özgü değişkenler, pay piyasasına yönelik sermaye hareketlerini eşzamanlı olarak etkilemektedir. Özellikle Türkiye pay piyasasına yapılan uluslararası net portföy yatırımlarını (NPY), USD/TRY döviz kuru, pay endeks getirisi ve ülke riski göstergesi olarak Türkiye 5 yıllık kredi temerrüt swap (CDS) primleri ile ilişkileri açısından ele alan çalışmada, Çok Değişkenli Markov Rejim Değişim Vektör Otoregresif Modelleri (MMS-VAR) kullanılmıştır. Değişkenler arasındaki ilişkiyi en iyi tanımlayan model, üç rejimli (daralma, ılımlı büyüme ve genişleme) MSIH(3)-VAR(2) modelidir. Modeldeki daralma ve genişleme rejimleri, finans piyasalarındaki ayı (daralma) ve boğa (genişleme) piyasaları olarak da ifade edilebilir. 2013-2016 dönemindeki haftalık verilerin kullanıldığı çalışmada, NPY ile döviz kuru arasındaki ilişkinin piyasanın içinde bulunduğu daralma veya büyüme rejimlerinde farklılık göstermesi dikkat çeken ampirik bulgulardan biridir.

HOW THE INTERNATIONAL PORTFOLIO INVESTMENTS AFFECTED IN THE RECESSION AND GROWTH PERIODS? TURKEY EXAMPLE

Since 1980s, the ongoing increase in global capital movements has led to the rise of emerging markets (EMs). Large external financing needs and risk premiums of EMs took the attention of international investors. EMs with high growth rates compared to the world average, are also attracting these markets in terms of international investors. Global capital movements are in the form of foreign direct investment and portfolio investments. Turkey stock market is one of the mostly affected EMs from the liquidity in international capital markets. Numerous variables affecting the global economy, such as monetary policy decisions made by the Federal Reserve (FED) and the European Central Bank (ECB), and country-specific variables such as exchange rate, interest rate, economic growth simultaneously affect capital flows as portfolio investments to the stock markets. Tax legislation, trust in law and justice, political stability of the country are some other important factors as the variety of financial instruments, transaction volume and market depth. There is a wide literature investigating the factors effecting international portfolio investments tend towards EMs. In particular, exchange rate, interest rate and asset turnover are the primary factors for portfolio investments which aim to gain high returns. Credit default swap’s (CDS) premiums, are major country risk indicators for those investing in different capital markets and are followed by the international portfolio investor. In the study which analyzes the international net portfolio investments to Turkey stock market in the context of the relationship with USD/TRY exchange rate, equity market index and Turkey’s 5 years credit default swap’s (CDS) premiums’, the weekly observations used beginning from 2013 to 2016. Because the data of the weekly net portfolio investments to the stock market have negative values in the sample period, all other time series used in the study are weekly changes too. By Multivariate Markov Switching Vector Autoregressive Models (MMS-VAR), the relationships analyzed in a nonlinear perspective. Use of MMS-VAR models presents the relationship according to the different regimes of the markets in the study. In the Markov Regime Switching (MRS) model, the regime of the economy is described by a state variable with probabilities and durations. Switching between different regimes of the economy is in a Markov chain process. The model that best describes the relationship between variables in this study is the three regimes (recession, moderate growth and expansion) Markov switching intercept and heteroscedasticity model (MSIH(3)-VAR(2)) model. The MSIH model has proven to be strong in explaining financial time series. The recession and expansion regimes in the model may also be expressed as bear (recession) market and bullish (expansion) market in financial markets. Firstly, MSIH(3)-VAR(2) model indicate that the variables used in the study governed by a long run relationship, and volatility is important in modelling this relationship. Secondly, the coefficients in the model presents negative relationships between net portfolio flows and two variables (exchange rate and CDS premiums) in long term. While exchange rate or CDS premiums increase (decrease), net portfolio flows decrease (increase). Conversely, net portfolio flows and stock index move in the same direction. While the transition probabilities examined, the maximum probabilities are seen from switching any regime to regime 2 (moderate growth). Another attracting result is that the transition from regime 1 (recession regime) to regime 3 (expansion regime) has the lowest probability of 0,1%. Moreover, the highest number of observations and the longest duration are in regime 2 in the sample period. In the MMS-VAR model, the response of the variables to a standard deviation shock made to other variables can be monitored separately for each regime. One of the important empirical findings of the study seen by impulse-response analyzes. In the short term, the relationship between net portfolio investments and exchange rate vary whether it is in the recession regime or in the growth regime. The strongest effect is shown in the third regime in the negative direction. Lastly, the direction of responds of net portfolio flows do not vary according to the regimes for the changes in stock index or CDS premium. If one standard deviation’s shock is applied to the index, the respond of net portfolio flows is positive in all three regimes as increase in three weeks and becoming permanent at the end of the third week. The responses of net portfolio flows to CDS premiums are seen as decrease in three weeks and become permanent at the end of the third week. Owing to the results are obtained in accordance with anticipated expectations of the study and the market dynamics, t

Kaynakça

Ahmad, F., Draz, M. U. ve Yang, S. (2015). Determinants of Foreign Portfolio Inflows: Analysis and Implications for China. Asian Journal of Finance & Accounting. Vol:7(2), 66-77.

Akkaya, M. (2017). Türk Tahvillerinin CDS Primlerini Etkileyen İçsel Faktörlerin Analizi. Maliye Finans Yazıları. 107, 129-146.

Ali, F. M., Spagnolo, F. ve Spagnolo, N. (2017). Portfolio flows and the US dollar–yen exchange rate.  Empirical Economics, 52(1), 179-189.

Arslan, S. ve Çiçek, M. (2017). Yabancı Portföy Yatırımları ile Döviz Kuru İlişkisi: Türkiye’de Yabancı Sermayenin Vergilendirilmesi. Uluslararası Yönetim İktisat ve İşletme Dergisi, ICMEB17 Özel Sayısı. 292-299.

Bakın, B ve Gürgün, G. (2014). Portfolio Investments and Asset Prices Relationship in Turkey. 10th International Academic Conference. Vienna, IISES.

Barışık, S. ve Açıkgöz, E. (2007). Türkiye´de Uluslararası Sermaye Hareketleri Faiz İlişkisi: (19922005 Dönemi VAR Analizi). TİSK Akademi. (1), 198.

Başarır, Ç. ve Keten, M., (2016). Gelişmekte Olan Ülkelerin CDS Primleri ile Hisse Senetleri ve Döviz Kurları Arasındaki Kointegrasyon İlişkisi. Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi. 8(15), 369-380.

Bildirici, M. E., Alp, E. A., Ersin, Ö. Ö., ve Bozoklu, Ü. (2010).  İktisatta kullanılan doğrusal olmayan zaman serisi yöntemleri. Türkmen Kitabevi.

Caporale, G. M., Ali, F. M., Spagnolo, F., ve Spagnolo, N. (2017). International portfolio flows and exchange rate volatility in emerging Asian markets.  Journal of International Money and Finance,  76, 1-15.

Çulha, A.A. (2006). A Structural VAR Analysis of the Determinants of Capital Flows into Turkey. Central Bank Review 2. 11-35.

Egyl, P. V., Johnk, D. W. ve Liston, D. P. (2010). Foreign Portfolio Investment Inflows To The United States: The Impact Of Investor Risk Aversion And US Stock Market Performance. North American Journal of Finance and Banking Research. 4(4), 25-41

Ehlers, T. ve Takast, E. (2013). Capital Flow Dynamics And FX Intervention. BIS Papers No 73, 25-3.

Franses, P. H. ve D. van Dijk. (2000). Nonlinear Time Series Models in Empirical Finance. Cambridge University Press.

Ghosh, S. ve Herwadkar, S. (2009). Foreign Portfolio Flows and their Impact on Financial Markets in India. Reserve Bank of India Occasional Papers. 30(3), 51-72.

Guidolin, M. Modelling, Estimating and Forecasting Financial Data under Regime (Markov) Switching. Lecture 7. Department of Finance. Bocconi University. http://didattica. unibocconi.it/mypage/dwload.php?nomefile=Lecture_7_-_Markov_Switching_ Models20130520235704.pdf (Erişim tarihi: 30.03.2016).

Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica. 57(2), 357-384.

Kadaoğlu Aydın, G., Hazar, A. ve Çütçü, İ., (2016). Kredi Temerrüt Takası ile Menkul Kıymetler Borsası Arasındaki İlişki-Gelişmiş ve Gelişmekte Olan Ülke Uygulamaları. Türk Sosyal Bilimler Araştırmaları Dergisi. 1(2), 1-22.

Koy, A.. (2015). The Relationship between Credit Default Swap Spreads, Equity Indexes and Sector Equity Indexes: An Empirical Study on Borsa Istanbul. 17. International Academic Conference. Vienna, IISES.

Krolzig, H. M. (1997). Markov Switching Vector Autoregressions: Modeling, Statistical Inference, and Application to Business Cycle Analysis. Springer Verlag.

Krolzig, H. M. (1998). Econometric Modeling of Markov-Switching Vector Autoregressions using MSVAR for OX. Institute of Economics and Statistics and Nuffield College. Oxford.

Krolzig, H. M. (2001). Markov-Switching Procedures for Dating the Euro-Zone Business Cycle. Vierteljahrshefte zur Wirtschaftsforschung. 70(3), 339-351.

Kurt Gümüş, G., Duru, A. ve Güngör, B. (2013). The Relationship Between Foreign Portfolio Investment And Macroeconomic Variables. European Scientific Journal. 9(34), 209-226.

Onyeisi, O. S., Odo, İ. S. ve Anoke, C. F. (2016). Foreign Portfolio Investment and Stock Market Growth in Nigeria. Developing Country Studies. 6(11), 64-76.

Rujiravanich, N. (2013). Impacts of Foreign Portfolio Investment Flows on Stock Prices and Exchange Rate in Thailand after Asian Financial Crisis. Economics and Public Policy. 4(8), 1-20.

Sethi, N. (2012). Inflows and Their Macroeconomics Impact in India a VAR Analysis. The Romanian Economic Journal. 46, 93-142.

Waqas, Y., Hashmi, S. H. ve Nazır, M. I. (2015). Macroeconomic factors and foreign portfolio investment volatility: A case of South Asian countries. Future Business Journal. 1, 65-74.

Yıldız, A. (2012). Yabancı Portföy Yatırımlarını Etkileyen Faktörlerin Belirlenmesi. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi. 26(1), 23-37.

Kaynak Göster