Yıl 2021, Cilt 6 , Sayı 1, Sayfalar 16 - 35 2021-04-30

MS-GARCH Yaklaşımıyla Menkul Kıymet Piyasalarında Volatilite Tahmini: Borsa İstanbul Uygulaması
Forecasting of Volatility in Stock Exchange Markets by MS-GARCH Approach: An Application of Borsa Istanbul

Abdulkadir KAYA [1] , İkram Yusuf YARBAŞI [2]


Menkul kıymet piyasalarında gözlemlenen volatiliteler, borsa paydaşlarının karar alma süreçlerini etkileyen önemli bir etkendir. Bu çalışmada Borsa İstanbul’u temsil eden BIST100 endeksinde oluşan volatiliteler analiz edilmiştir. Bu amaçla, çalışmada, 01.04.1993-20.04.2018 dönemi BIST100 endeksi kapanış verileri kullanılmıştır. BIST100 endeksi standart, yüksek ve düşük volatilite rejimleri olmak üzere üç rejime dönüştürülerek, Markov Rejim Değişim GARCH (MS-GARCH) ile analiz edilmiştir. Üçlü rejimli MS-GARCH modeli ile yapılan analiz sonucunda endeks için ele alınan rejim katsayılarının istatistiksel olarak anlamlı olduğu, endekste rejimlerin varlığı tespit edilmiştir. Rejim geçişleri olasılıkları incelendiğinde ise birer günlük süreçte standart oynaklık rejiminin sürme olasılığı 0,62, düşük volatilite rejimine geçiş olasılığı 0,23 ve yüksek volatilite rejimine geçiş olasılığının ise 0,145 olduğu belirlenmiştir. Ayrıca 5 ve 20 günlük süreçte rejim geçişlerinin olasılıklarının birbirine çok yakın olduğu tespit edilmiştir.
The volatility observed in securities markets has an important influence on the decision making processes of stock market stakeholders. In this study, the volatilities in BIST100 index which represents Borsa Istanbul was analyzed. For this purpose, BIST100 index closing data for the period of 03.01.1988-20.04.2018 was used in the study. The BIST100 index was analyzed by Markov regime switching GARCH (MS-GARCH) with three regimes, standard, high and low volatility regimes. As a result of the triple regime MS-GARCH intensive analysis, the existence of endogenous regimens was determined, in which the regime coefficients considered for the index were statistically significant. When the possibilities of regime transitions are analyzed, it is determined that the probability of continuing the standard volatility regime is 0.62, the probability of transition to low volatility regime is 0.23 and the probability of transition to high volatility regime is 0.145. Moreover, it was determined that the possibilities of regime passage in 5 and 20 days are very close to each other.
  • Abounoori, E., Elmi, Z. M. and Nademi, Y. (2016). Forecasting Tehran stock exchange volatility; Markov switching GARCH approach. Physica A: Statistical Mechanics and its Applications, 445, 264-282. https://doi.org/10.1016/j.physa.2015.10.024
  • Ardia, D. (2008). Financial risk management with Bayesian estimation of GARCH models (Vol. 18). Heidelberg: Springer. doi:10.1007/978-3-540-78657-3
  • Ardia, D., Bluteau, K. and Rüede, M. (2019). Regime changes in Bitcoin GARCH volatility dynamics. Finance Research Letters, 29, 266-271. https://doi.org/10.1016/j.frl.2018.08.009
  • Ardia, D., Bluteau, K., Boudt, K. and Catania, L. (2018). Forecasting risk with Markov-switching GARCH models: A large-scale performance study. International Journal of Forecasting, 34(4), 733-747. https://doi.org/10.1016/j.ijforecast.2018.05.004
  • Ardia, D., Bluteau, K., Boudt, K., Catania, L. and Trottier, D. A. (2019). Markov-switching GARCH models in R: The MSGARCH package. Journal of Statistical Software, 91(4). doi:10.18637/jss.v091.i04
  • Atakan, T. (2009). İstanbul Menkul Kıymetler Borsasında değişkenliğin (volatilitenin) ARCH-GARCH yöntemleri ile modellenmesi. Yönetim Dergisi, 62, 48-61. Retrieved from https://app.trdizin.gov.tr/
  • Augustyniak, M. (2014). Maximum likelihood estimation of the Markov-switching GARCH model. Computational Statistics & Data Analysis, 76, 61-75. https://doi.org/10.1016/j.csda.2013.01.026
  • Bauwens, L., Dufays, A. and Rombouts, J. V. (2014). Marginal likelihood for Markov-switching and change-point GARCH models. Journal of Econometrics, 178, 508-522. https://doi.org/10.1016/j.jeconom.2013.08.017
  • Bauwens, L., Preminger, A. and Rombouts, J. V. (2010). Theory and inference for a Markov switching GARCH model. The Econometrics Journal, 13(2), 218-244. doi:10.1111/j.1368-423X.2009.00307.x
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
  • Bollerslev, T. (1987) A conditionally heteroskedastic time series model for speculative prices and rates of return. The Review of Economics and Statistics, 69(3), 542-547. https://doi.org/10.2307/1925546
  • Broock, W. A., Scheinkman, J. A., Dechert, W. D. And LeBaron, B. (1996). A test for independence based on the correlation dimension. Econometric reviews, 15(3), 197-235. https://doi.org/10.1080/07474939608800353
  • Cai, J. (1994). A Markov model of switching-regime ARCH. Journal of Business & Economic Statistics, 12(3), 309-316. Retrieved from https://www.tandfonline.com/
  • Çağıl, G. ve Okur, M. (2010). 2008 küresel krizinin İMKB hisse senedi piyasası üzerindeki etkilerinin GARCH modelleri ile analizi [The analysis of the impact of 2009 global crisis on the ISE stock market using GARCH models]. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 28(1), 573-585. Retrieved from https://dergipark.org.tr/en/pub/muiibd
  • Çavdar, Ş. Ç. ve Aydın, A. D. (2017). Borsa İstanbul Kurumsal Yönetim Endeksi’nde (XKURY) volatilitenin etkisi: ARCH, GARCH ve SWARCH modelleri ile bir inceleme [The effect of volatility in the Borsa Istanbul Corporate Governance Index (XKURY): an examination with the ARCH, GARCH AND SWARCH models]. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 22(3), 697-711. Retrieved from https://dergipark.org.tr/en/pub/sduiibfd/
  • Dueker, M. J. (1997). Markov switching in GARCH processes and mean-reverting stock-market volatility. Journal of Business & Economic Statistics, 15(1), 26-34. https://doi.org/10.1080/07350015.1997.10524683
  • Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007. https://doi.org/10.2307/1912773
  • Francq, C. and Zakoian, J. M. (2008). Deriving the autocovariances of powers of Markov-switching GARCH models, with applications to statistical inference. Computational Statistics & Data Analysis, 52(6), 3027-3046. https://doi.org/10.1016/j.csda.2007.08.003
  • Gray, S. F. (1996). Modeling the conditional distribution of interest rates as a regime-switching process. Journal of Financial Economics, 42(1), 27-62. https://doi.org/10.1016/0304-405X(96)00875-6
  • Güriş, S. ve Saçıldı, İ. S. (2011). İstanbul Menkul Kıymetler Borsası’nda hisse senedi getiri volatilitesinin klasik ve Bayesyen GARCH modelleri ile analizi [Analysis of stock return volatility using classical and Bayesian GARCH models in Istanbul Stock Exchange]. Trakya Üniversitesi Sosyal Bilimler Dergisi, 13(2), 153-171. Retrieved from https://dergipark.org.tr/en/pub/trakyasobed/
  • Gürsoy, M. ve Balaban, M. (2014). Hisse senedi getirilerindeki volatilitenin tahminlenmesinde destek vektör makinelerine dayalı GARCH modellerinin kullanımı [Volatility forecasting in stock returns using support vector machines based GARCH models]. Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 5(8), 167-186. Retrieved from https://dergipark.org.tr/tr/pub/kauiibf
  • Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica: Journal of the Econometric Society, 357-384. https://doi.org/10.2307/1912559
  • Hamilton, J. D. (1990). Analysis of time series subject to changes in regime. Journal of econometrics, 45(1-2), 39-70. https://doi.org/10.1016/0304-4076(90)90093-9
  • Hamilton, J. D. and Susmel, R. (1994). Autoregressive conditional heteroskedasticity and changes in regime. Journal of econometrics, 64(1-2), 307-333. https://doi.org/10.1016/0304-4076(94)90067-1
  • Henkel, S. J., Martin, J. S. and Nardari, F. (2011). Time-varying short-horizon predictability. Journal of financial economics, 99(3), 560-580. https://doi.org/10.1016/j.jfineco.2010.09.008
  • Hu, L. and Shin, Y. (2008). Optimal test for Markov switching GARCH models. Studies in Nonlinear Dynamics & Econometrics, 12(3). Retrieved from http://citeseerx.ist.psu.edu/
  • Karabacak, M., Meçik, O. ve Genç, E. (2014). Koşullu değişen varyans modelleri ile BİST 100 endeks getirisi ve altın getiri serisi volatilitesinin tahmini. Journal of Alanya Faculty of Business/Alanya Isletme Fakültesi Dergisi, 6(1). Retrieved from https://web.a.ebscohost.com/
  • Kiliç, R. (2007). Conditional volatility and distribution of exchange rates: GARCH and FIGARCH models with NIG distribution. Studies in Nonlinear Dynamics & Econometrics, 11(3). https://doi.org/10.2202/1558-3708.1430
  • Klaassen, F. (2002). Improving GARCH volatility forecasts with regime-switching GARCH. In J. D. Hamilton and B. Raj (Eds.), Advances in Markov-switching models (pp. 223-254). https://doi.org/10.1007/978-3-642-51182-0
  • Korkpoe, C. H. and Howard, N. (2019). Volatility Model Choice for Sub-Saharan frontier equity markets-a Markov Regime Switching Bayesian approach. EMAJ: Emerging Markets Journal, 9(1), 69-79. doi:10.5195/emaj.2019.172
  • Kula, V. ve Baykut, E. (2017). BIST Banka Endeksi’nin (XBANK) volatilite yapısının Markov rejim değişimi GARCH modeli (MSGARCH) ile analizi [An analysis of the volatility structure of BIST Bank (XBANK) index by a Markov regime switching GARCH (MSGARCH) model]. Bankacılar Dergisi, 28(2), 89-110. Retrieved from https://www.tbb.org.tr/
  • Kuzu, S. (2018). Borsa İstanbul Endeksi (BİST 100) getiri volatiletesinin ARCH ve GARCH modeli ile tahmin edilmesi [Prediction of stock exchange Istanbul Index (BIST 100) return volatility with ARCH and GARCH models]. Muhasebe ve Vergi Uygulamaları Dergisi, 608-624. Retrieved from https://dergipark.org.tr/en/pub/muvu/
  • Lamoureux, C. G. and Lastrapes, W. D. (1990). Heteroskedasticity in stock return data: Volume versus GARCH effects. The journal of finance, 45(1), 221-229. https://doi.org/10.1111/j.1540-6261.1990.tb05088.x
  • Lolea, I. C. and Vilcu, L. C. (2018). Measures of volatility for the Romanian Stock Exchange: a regime switching approach. Paper presented at the Proceedings of the International Conference on Business Excellence (pp. 544-556). Sciendo. https://doi.org/10.2478/picbe-2018-0049
  • Maheu, J. M. and McCurdy, T. H. (2000). Identifying bull and bear markets in stock returns. Journal of Business & Economic Statistics, 18(1), 100-112. Retrieved from https://www.tandfonline.com/
  • Marcucci, J. (2005). Forecasting stock market volatility with regime-switching GARC models. Studies in Nonlinear Dynamics & Econometrics, 9(4), s. 1-53. https://doi.org/10.2202/1558-3708.1145
  • Mazibas, M. (2005). IMKB piyasalarındaki volatilitenin modellenmesi ve öngörülmesi: asimetrik GARCH modelleri ile bir uygulama. Retrieved from https://papers.ssrn.com/
  • McNeil, A. J., Frey, R. and Embrechts, P. (2015). Quantitative risk management: concepts, techniques and tools-revised edition. New Jersey: Princeton university press.
  • Moore, T. and Wang, P. (2007). Volatility in stock returns for new EU member states: Markov regime switching model. International Review of Financial Analysis, 16(3), 282-292. https://doi.org/10.1016/j.irfa.2007.03.006
  • Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 59(2), 347-370. https://doi.org/10.2307/2938260
  • Paye, B. S. and Timmermann, A. (2006). Instability of return prediction models. Journal of Empirical Finance, 13(3), 274-315. https://doi.org/10.1016/j.jempfin.2005.11.001
  • Poon, S. H. and Granger, C. W. (2003). Forecasting volatility in financial markets: A review. Journal of economic literature, 41(2), 478-539. doi:10.1257/002205103765762743
  • Satchell, S. and Knight, J. (2011). Forecasting volatility in the financial markets. Oxford: Elsevier.
  • Satoyoshi, K. and Mitsui, H. (2012). Option valuation under bulls and bears market conditions (Working papers series 12-01). Retrieved from https://www.eco.nihon-u.ac.jp/center/economic/publication/pdf/12-01.pdf
  • Schaller, H. And Norden, S. V. (1997). Regime switching in stock market returns. Applied Financial Economics, 7(2), 177-191. https://doi.org/10.1080/096031097333745
  • Schwert, G. W. (1989). Why does stock market volatility change over time? The journal of finance, 44(5), 1115-1153. https://doi.org/10.1111/j.1540-6261.1989.tb02647.x
  • Sevüktekin, M. ve Nargeleçekenler, M. (2006). İstanbul Menkul Kıymetler Borsasında getiri volatilitesinin modellenmesi ve önraporlanması [Modeling and forecasting of return volatility at Istanbul Stock Exchange]. Ankara Üniversitesi SBF Dergisi, 61(4), 243-265. Retrieved from https://dergipark.org.tr/en/pub/ausbf/
  • Škrinjarić, T. and Šego, B. (2016). Asset allocation and regime switching on Croatian financial market. Croatian Operational Research Review, 7(2), 201-215. https://doi.org/10.17535/crorr.2016.0014
  • Şahin, Ö. (2016). Güniçi fiyat anomalisi’nin ARCH ailesi modelleri ile test edilmesi; Borsa İstanbul 100 ve kurumsal yönetim endeksi üzerine bir uygulama [Testing intra-day anomalies by ARCH family models; an application on BIST 100 and BIST corporate governance indexes]. Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 19(36), 329-360. Retrieved from https://dergipark.org.tr/en/pub/baunsobed/
  • Tu, J. (2010). Is regime switching in stock returns important in portfolio decisions?. Management Science, 56(7), 1198-1215. https://doi.org/10.1287/mnsc.1100.1181
  • Turner, C. M., Startz, R. and Nelson, C. R. (1989). A Markov model of heteroskedasticity, risk, and learning in the stock market. Journal of Financial Economics, 25(1), 3-22. https://doi.org/10.1016/0304-405X(89)90094-9
  • Ural, M. ve Adakale, T. (2009). Beklenen kayıp yöntemi ile riske maruz değer analizi [Value at risk analysis with expected shortfall]. Akdeniz İ.İ.B.F. Dergisi, 9(17), s. 23-39. Retrieved from https://dergipark.org.tr/en/pub/auiibfd
  • Visković, J., Arnerić, J. and Rozga, A. (2014). Volatility switching between two regimes. International Journal of Economics and Management Engineering, 8(3), 699-703. https://doi.org/10.5281/zenodo.1091336
  • Wang, P. and Theobald, M. (2008). Regime-switching volatility of six East Asian emerging markets. Research in International Business and Finance, 22(3), 267-283. https://doi.org/10.1016/j.ribaf.2007.07.001
Birincil Dil en
Konular İşletme Finans
Yayınlanma Tarihi Nisan 2021
Bölüm Makaleler
Yazarlar

Orcid: 0000-0001-7789-5461
Yazar: Abdulkadir KAYA
Kurum: ERZURUM TEKNİK ÜNİVERSİTESİ
Ülke: Turkey


Orcid: 0000-0003-4689-5121
Yazar: İkram Yusuf YARBAŞI (Sorumlu Yazar)
Kurum: ERZURUM TEKNİK ÜNİVERSİTESİ
Ülke: Turkey


Tarihler

Yayımlanma Tarihi : 30 Nisan 2021

APA Kaya, A , Yarbaşı, İ . (2021). Forecasting of Volatility in Stock Exchange Markets by MS-GARCH Approach: An Application of Borsa Istanbul . Ekonomi Politika ve Finans Araştırmaları Dergisi , 6 (1) , 16-35 . DOI: 10.30784/epfad.740815