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Analysis of stock market by Markov switching approach

Yıl 2020, Sayı: 49, 246 - 256, 16.12.2020

Öz

Volatility in the prices of financial assets significantly affects portfolio risks and investment returns. For this reason, it is important to model the volatilities, determine the causes and predict them. In this study, it is aimed to analyze Borsa İstanbul 100 index return with the Markov switching approach. In the study, it was used to data that BIST 100 index, USD ($) / Turkish Lira (TRY) exchange rate and 2-year government bond interest rates for the period of January 4, 2010 - December 30, 2019. It was observed that two different regimes, high volatility and low return as well as low volatility and high return, in the study. The 1st regime represents the bear market and the 2 nd regime represents the bull market. Average stay time (64) in regime 2 is higher than average stay time (11) in regime 1. The probability of switching to regime 2 is more than the probability of switching to regime 1. While the exchange rate has an effect on the 1st regime, this effect disappears in the 2nd regime. The effect of the interest rate was seen in both regimes. The results are usable to investors, portfolio managers and risk managers.

Kaynakça

  • Ahmad, W. & Sehgal, S. (2013). Regime shifts and volatility in BRIICKS stock markets: An asset allocation perspective. International Journal of Emerging Markets, 10(3), 383-408.
  • Almonares, R. A. L. (2019). Markov switching model of Philippine stock market volatility. DLSU Business & Economics Review, 29(1), 24-30.
  • Basher, S. A., Haug, A. A., & Sadorsky, P. (2016). The impact of oil shocks on exchange rates: A Markov-switching approach, Energy Economics, 54, 11-23.
  • Castellano, R. & Scaccia, L. (2014). Can CDS indexes signal future turmoils in the stock market? A Markov switching perspective. Central European Journal of Operations Research, 22(2), 285-305.
  • Chitkasame, T. & Tansuchat, R. (2019). An analysis of contagion effect on ASEAN stock market using multivariate Markov switching DCC GARCH, Thai Journal of Mathematics, 135-152.
  • Dornbusch, R. & Fischer, S. (1980). Exchange rates and current account, American Economic Association, 70(5), 960-971.
  • Liu, X., Margaritis, D., & Wang, P. (2012). Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors, Journal of Empirical Finance, 19(4), 483-496.
  • Ma, F., Wahab, M. I. M., Huang, D. & Xu, W. (2017). Forecasting the realized volatility of the oil futures market: A regime switching approach. Energy Economics, 67, 136-145.
  • Palason K., & Tansuchat R. (2019) An analysis of stock market cycle with Markov switching and Kink model. In: Kreinovich V., Sriboonchitta S. (eds) Structural changes and their econometric modeling. TES 2019. Studies in Computational Intelligence, 808. Springer, Cham
  • Qiao, Z., Li, Y., & Wong, W. K. (2011). Regime-dependent relationships among the stock markets of the US, Australia and New Zealand: a Markov-switching VAR approach, Applied Financial Economics, 21(24), 1831-1841.
  • Schaller, H. & Norden, S. V. (1997). Regime switching in stock market returns, Applied Financial Economics. 7(2), 177-191.
  • Song, W., Ryu, D., & Webb, R. I. (2016). Overseas market shocks and VKOSPI dynamics: A Markov-switching approach, Finance Research Letters, 16, 275-282.
  • Sosa, M., Ortiz, E. & Cabello, A. (2018). Dynamic linkages between stock market and exchange rate in MILA countries: A Markov regime switching approach (2003-2016). Análisis económico, 33(83), 57-74.
  • Taştan, H. & Güngör, A. (2019). Türkiye Hisse Senedi Piyasa Volatilitesinin Makroekonomik Temelleri, Business and Economics Research Journal. 10(4), 823-832.
  • Walid, C., Chaker, A., Masood, O., & Fry, J. (2011). Stock market volatility and exchange rates in emerging countries: A Markov-state switching approach, Emerging Markets Review, 12(3), 272-292.
  • Xaba, D., Moroke, N. D. & Rapoo, I. (2019). Modeling stock market returns of BRICS with a Markov-switching dynamic regression Model, Journal of Economics and Behavioral Studies, 11(3 (J)), 10-22.
  • Zare, R., Azali, M., & Habibullah, M. S. (2013). Monetary policy and stock market volatility in the ASEAN5: Asymmetries over bull and bear markets. Procedia Economics and Finance, 7(1), 18-27.
  • Zolfaghari, M. & Sahabi, B. (2017). Impact of foreign exchange rate on oil companies risk in stock market: A Markov-switching approach, Journal of Computational and Applied Mathematics, 317, 274-289.

Borsa oynaklığının Markov rejim dönüşüm yöntemiyle analizi

Yıl 2020, Sayı: 49, 246 - 256, 16.12.2020

Öz

Finansal varlıkların fiyatlarında görülen oynaklıklar portföy risklerini ve yatırım getirilerini önemli derecede etkilemektedir. Bu nedenle oynaklıkların modellenmesi, sebeplerinin belirlenmesi, tahmin edilmesi önemlidir. Bu çalışmada Borsa İstanbul 100 endeks getirisinin Markov rejim dönüşüm modeliyle incelenmesi amaçlanmıştır. Çalışmada 4 Ocak 2010 – 30 Aralık 2019 dönemine ait BİST 100 endeksi, ABD Doları ($)/Türk Lirası (₺) döviz kuru ve 2 yıllık devlet tahvilleri faiz oranları kullanılmıştır. Çalışmada yüksek oynaklık ve düşük getiri ile düşük oynaklık ve yüksek getiri olmak üzere iki farklı rejim görülmüştür. 1. rejim ayı piyasasını, 2. rejim ise boğa piyasasını temsil etmektedir. 2. rejimde ortalama kalma süresi (64) 1. rejimde ortalama kalma süresinden (11) daha yüksektir. Rejime 2’ye geçme olasılığı, rejim 1’e geçme olasılığından daha fazladır. Döviz kurunun 1. Rejimde etkisi görülürken 2. rejimde bu etki kaybolmaktadır. Faiz oranının etkisi her iki rejimde de görülmüştür. Sonuçlar, yatırımcılar, portföy yöneticileri ve risk yöneticileri açısından kullanılabilirlik taşımaktadır.

Kaynakça

  • Ahmad, W. & Sehgal, S. (2013). Regime shifts and volatility in BRIICKS stock markets: An asset allocation perspective. International Journal of Emerging Markets, 10(3), 383-408.
  • Almonares, R. A. L. (2019). Markov switching model of Philippine stock market volatility. DLSU Business & Economics Review, 29(1), 24-30.
  • Basher, S. A., Haug, A. A., & Sadorsky, P. (2016). The impact of oil shocks on exchange rates: A Markov-switching approach, Energy Economics, 54, 11-23.
  • Castellano, R. & Scaccia, L. (2014). Can CDS indexes signal future turmoils in the stock market? A Markov switching perspective. Central European Journal of Operations Research, 22(2), 285-305.
  • Chitkasame, T. & Tansuchat, R. (2019). An analysis of contagion effect on ASEAN stock market using multivariate Markov switching DCC GARCH, Thai Journal of Mathematics, 135-152.
  • Dornbusch, R. & Fischer, S. (1980). Exchange rates and current account, American Economic Association, 70(5), 960-971.
  • Liu, X., Margaritis, D., & Wang, P. (2012). Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors, Journal of Empirical Finance, 19(4), 483-496.
  • Ma, F., Wahab, M. I. M., Huang, D. & Xu, W. (2017). Forecasting the realized volatility of the oil futures market: A regime switching approach. Energy Economics, 67, 136-145.
  • Palason K., & Tansuchat R. (2019) An analysis of stock market cycle with Markov switching and Kink model. In: Kreinovich V., Sriboonchitta S. (eds) Structural changes and their econometric modeling. TES 2019. Studies in Computational Intelligence, 808. Springer, Cham
  • Qiao, Z., Li, Y., & Wong, W. K. (2011). Regime-dependent relationships among the stock markets of the US, Australia and New Zealand: a Markov-switching VAR approach, Applied Financial Economics, 21(24), 1831-1841.
  • Schaller, H. & Norden, S. V. (1997). Regime switching in stock market returns, Applied Financial Economics. 7(2), 177-191.
  • Song, W., Ryu, D., & Webb, R. I. (2016). Overseas market shocks and VKOSPI dynamics: A Markov-switching approach, Finance Research Letters, 16, 275-282.
  • Sosa, M., Ortiz, E. & Cabello, A. (2018). Dynamic linkages between stock market and exchange rate in MILA countries: A Markov regime switching approach (2003-2016). Análisis económico, 33(83), 57-74.
  • Taştan, H. & Güngör, A. (2019). Türkiye Hisse Senedi Piyasa Volatilitesinin Makroekonomik Temelleri, Business and Economics Research Journal. 10(4), 823-832.
  • Walid, C., Chaker, A., Masood, O., & Fry, J. (2011). Stock market volatility and exchange rates in emerging countries: A Markov-state switching approach, Emerging Markets Review, 12(3), 272-292.
  • Xaba, D., Moroke, N. D. & Rapoo, I. (2019). Modeling stock market returns of BRICS with a Markov-switching dynamic regression Model, Journal of Economics and Behavioral Studies, 11(3 (J)), 10-22.
  • Zare, R., Azali, M., & Habibullah, M. S. (2013). Monetary policy and stock market volatility in the ASEAN5: Asymmetries over bull and bear markets. Procedia Economics and Finance, 7(1), 18-27.
  • Zolfaghari, M. & Sahabi, B. (2017). Impact of foreign exchange rate on oil companies risk in stock market: A Markov-switching approach, Journal of Computational and Applied Mathematics, 317, 274-289.
Toplam 18 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İşletme
Bölüm Makaleler / Articles
Yazarlar

Zekai Şenol 0000-0001-8818-0752

Yayımlanma Tarihi 16 Aralık 2020
Gönderilme Tarihi 26 Ekim 2020
Kabul Tarihi 2 Aralık 2020
Yayımlandığı Sayı Yıl 2020 Sayı: 49

Kaynak Göster

APA Şenol, Z. (2020). Borsa oynaklığının Markov rejim dönüşüm yöntemiyle analizi. Erciyes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi(49), 246-256.

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