Research Article
BibTex RIS Cite

Volatility Modeling and Spillover: The Turkish and Russian Stock Markets

Year 2024, Volume: 53 Issue: 1, 81 - 101, 30.04.2024
https://doi.org/10.26650/ibr.2024.53.162811

Abstract

This study investigates the internal and external (spillover) characteristics of the volatility of the Turkish and Russian stock market indices. To this end, generalized autoregressive conditional heteroskedasticity models that are classified as short memory (GARCH, EGARCH, GJR-GARCH, APARCH) and long memory (FIGARCH, FIEGARCH, FIAPARCH, HYGARCH) considering adaptive structure (Fourier series), and the rolling Hong causality methods are used. The analysis spans the years 2003–2020, revealing that the asymmetric power autoregressive conditional heteroskedasticity model is the most appropriate method in terms of both stock indices and leverage and long memory effects are evident in the volatility series. Bidirectional volatility spillovers between Turkish and Russian stock market indices are also evident in all time horizons. Investors can use volatility results for stock valuation, risk management, portfolio diversification, and hedging, and policymakers can consider the volatility results to evaluate the fragility of financial markets.

References

  • Al-Hajieh, H. (2017). Evaluated the success of Fractional Integrated GARCH models on prediction stock market return volatility in Gulf Arab stock markets. International Journal of Economics and Finance, 9(7), 200-213. google scholar
  • Altuntas Taspunar, S. & Colak, F. D. (2015). Modelling and estimating volatility with ARCH Models At ISE-100 Index. İstanbul Management Journal, 26 (79), 208-223. google scholar
  • Andersen, T. G. & Bollerslev, T. (1997). Heterogeneous information arrivals and return volatility dynamics: Uncovering the long-run in high frequency returns. The Journal Of Finance, 52(3), 975-1005. google scholar
  • Ay, G. & Gün, M. (2020). Volatility modelling in Borsa Istanbul Stock Market: An application on BIST Banking Index. Business & Management Studies: An International Journal, 8(5), 3795-3814. https://doi. org/10.15295/bmij.v8i5.1547 google scholar
  • Baillie, R., Bollerslev, T. & Mikkelsen, H. O. (1996). Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity. Journal Of Econometrics, 74(1), 3-30. https://doi.org/10.1016/S0304-4076(95)01749-6 google scholar
  • Baillie, R. T. & Morana, C. (2009). Modelling long memory and structural breaks in conditional variances: An adaptive FIGARCH approach. Journal of Economic Dynamics & Control, 33, 1577-1592. google scholar
  • Bayramoglu, M. F. & Abasız, T. (2017). The analysis of volatility spillover effect between emerging market indices. Journal of Accounting and Finance, 183-199. google scholar
  • Beirne,J., Caporale, G. M., Schulze-Ghattas, M. & Spagnolo, N. (2010). Global and regional spillovers in emerging stock markets: A multivariate GARCH-in-mean analysis. Emerging Markets Review, 11 (3), 250-260. https://doi.org/10.1016/j.ememar.2010.05.002 google scholar
  • Bentes, S. R. (2014). Measuring persistence in stock market volatility using the FIGARCH approach. Physi-ca A: Statistical Mechanics and its Applications, 408, 190-197. google scholar
  • Bentes, S. R., Menezes, R. & Mendes, D. (2008). Long Memory and Volatility Clustering: Is the Empirical Evidence Consistent Across Stock Markets ? Physica A, 387: 3826-3830. google scholar
  • Berberoglu, M. (2020). The investigation of volatility spillover effect between stock markets of Türkiye, Italy, Greece and Russia. Business & Management Studies: An international Journal, 8(2), 1576-1598. https://doi.org/10.15295/bmij.v8i2.1475 google scholar
  • Berger, D., Chaboud, A., & Hjalmarsson, E. (2009). What drives volatility persistence in the foreign exchan-ge market? Journal of Financial Economics, 94(2), 192-213. google scholar
  • Bhowmik, D .(2013). Stock Market Volatility: An Evaluation. International Journal of Scientific and Rese-arch Publications, 3 (10), 1-17. google scholar
  • Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics, 31, 307-327. https://doi.org/10.1016/0304-4076(86)90063-1 google scholar
  • Bollerslev, T., Engle, R. F. & Nelson, D. B. (1994). ARCH models. Handbook of Econometrics, 4, 29593038. google scholar
  • Bollerslev, T. & Mikkelsen, H. (1996). Modeling and pricing long memory in stock market volatility. Jour-nal of Econometrics, 73(1), 151-184. https://doi.org/10.1016/0304-4076(95)01736-4 google scholar
  • Bose, S. (2007). Understanding the Volatility Characteristics and Transmisson Effect in the Indian Stock Index and Index Futures Market, ICRA Bulletin- Money & Finance, 139-162 google scholar
  • Brooks, R. (2007). Power ARCH modelling of the volatility of emerging equity markets. Emerging Markets Review, 8, 124-133. google scholar
  • Brooks, C. (2008). Introductory Econometrics for Finance. Second Edition. Cambridge: Cambridge Uni-versity Press. google scholar
  • Büberkökü, Ö. & Kızıldere, C. (2017). Examining the properties of BIST100 Index return volatility. V. Anadolu International Conference in Economics, Eskişehir/Türkiye. google scholar
  • Caglı, E. Ç., Mandacı, P. E. & Kahyaoğlu, H. (2011). Volatility shifts and persistence in variance: Evidence from the sector indices of Istanbul Stock Exchange. International Journal of Economic Sciences and Applied Research, 4(3), 119-140. google scholar
  • Campbell, J. Y., & Hentschel, L. (1992). No news is good news: An asymmetric model of changing volatility in stock returns. Journal of Financial Economics, 31(3), 281-318. google scholar
  • Carroll, R. & Kearney, C. (2009), “GARCH modeling of stock market volatility”, Gregoriou, G. N. (Ed), Stock Market Volatility, Chapman & Hall/CRC, pp. 71-90. google scholar
  • Celik, İ., Özdemir, A. & Gülbahar Demir, S. (2018). Return and volatility spillover between Islamic stock indices: An application of multivarite VAR-EGARCH on developed and emerging markets. Bulletin of Accounting and Finance Reviews, 1(2), 89-100. google scholar
  • Çevik, E. İ. (2012). The testing of effıcient market hypothesis ın the Istanbul stock exchange by using long memory models: A sector-specific analysis. Journal of Yaşar University, 7(26), 4437-4454. google scholar
  • Çevik, E. İ. &Topaloglu, G. (2014). Long memory and structural breaks on volatility: Evidence from Borsa Istanbul. Balkan Journal of Social Sciences, 3(6), 40-55. google scholar
  • Chang, C-L., McALeer, M. & Tansuchat, R. (2012). Modelling long memory volatility in agricultu-ral commodity futures returns. Annals of Financial Economics, 7(2),1-27. https://doi.org/10.1142/ S2010495212500108 google scholar
  • Chikli, W., Aloui, C. & Nguyen, D. K. (2012). Asymmetric Effects and Long Memory in Dynamic Volatility Relationships Between Stock Returns and Exchange Rates. Int. Fin. Markets Inst. and Money. 22, 738757 google scholar
  • Chong, C. W., Ahmad, M. I., & Abdullah, M. Y. (1999). Performance of GARCH models in forecasting stock market volatility. Journal of forecasting, 18(5), 333-343. https://doi.org/10.1002/(SICI)1099-131X(199909)18:5<333::AID-FOR742>3.0.CO;2-K google scholar
  • Christensen, B. J., Nielsen, M. O. & Zhu, J. (2010). Long Memory in Stock Market Volatility and the Volatility-in-Mean Effect: The FIEGARCH-M Model. Journal of Empirical Finance. 17, 460-47 google scholar
  • Davidson, J. (2004). Moment and memory properties of linear conditional heteroscedasticity models and a new model. Journal of Business & Economic Statistics, 22(1), 16-29. google scholar
  • Dedi, L. & Yavas, B.F. (2016). Return and volatility spillovers in equity markets: An investigation using various GARCH methodologies. Cogent Economics& Finance, 4, 1-18. https://doi.org/10.1080/233220 39.2016.1266788 google scholar
  • Ding, Z., Granger, C.W. J. & Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1(1), 83-106. https://doi.org/10.1016/0927-5398(93)90006-D google scholar
  • Engle, R. F., Ito, T. & Lın, W-L. (1990). Meteor showers or heat waves? Heteroskedastic intra-daily volatility in the foreign exchange market. Econometrica, 58(3), 525-542. https://doi.org/10.2307/2938189 google scholar
  • Engle, R. F. & Ng, V. K. (1993). Measuring and testing the impact of news on volatility. The Journal of Finance, 48(5), 1749-1778. https://doi.org/10.1111/j.1540-6261.1993.tb05127.x google scholar
  • Ewing, T. B. & Malik, F. (2017). Modelling asymmetric volatility in oil prices under structural breaks. Energy Economics, 63, 227-233. https://doi.org/10.1016/j.eneco.2017.03.001 google scholar
  • French, K. R., Schwert, G. W., & Stambaugh, R. F. (1987). Expected stock returns and volatility. Journal of financial Economics, 19(1), 3-29. google scholar
  • Gaye Gencer, H., & Demiralay, S. (2016). Volatility modeling and value-at-risk (var) forecasting of emerging stock markets in the presence of long memory, asymmetry, and skewed heavy tails. Emerging Markets Finance and Trade, 52(3), 639-657. google scholar
  • Glosten, L. R., Jagannathan, R. & Runkle, D. E. (1993). On the Relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779- 1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x google scholar
  • Gökbulut, R. İ. (2017). An empirical analysis of volatility transmission between BIST and international stock markets. The International Journal of Economic and Social Research, 13(1), 141-159. google scholar
  • Günay, S. (2014). Long memory property and structural breaks in volatility: Evidence from Türkiye and Brazil. International Journal of Economics and Finance, 6(12), 119. google scholar
  • Gürsoy, S. & Eroglu, Ö. (2016). Return and volatility spillovers among the share markets of emerging eco-nomies: an analysis from 2006 to 2015 years. Mehmet Akif Ersoy University Journal of Economics and Administrative Sciences, 3(5), 16-33 google scholar
  • Hong, Y. (2001). A test for volatility spillover with application to exchange rates. Journal of Economet-rics, 103 (1-2), 183-224. https://doi.org/10.1016/S0304-4076(01)00043-4 google scholar
  • Kalotychou, E., & Staikouras, K. S. (2009), “An overview of the issues surrounding stock market volatility”, Gregoriou, G. N. (Ed), Stock Market Volatility, Plattsburgh/New York: A Chapman & Hall /CRC Finance, pp. 3-29. google scholar
  • Karabacak, M., Mecik, O. & Genc, E. (2014). Estimating the volatility of BIST 100 Index return and gold return index with conditional heteroscedasticity models. International Journal of Alanya Faculty of Bu-siness, 6(1), 79-90. google scholar
  • Koutmos, G. & Booth, G.G. (1995). Asymmetric volatility transmission in international stock markets. Jour-nal of International Money and Finance, 14(6), 747-762. google scholar
  • Koy, A. & Ekim. (2016). Modelling the volatility of Istanbul Stock Exchange Sector Indexes. E- Journal of the Faculty of Economic and Administrative Sciences, 5(2), 1-23. google scholar
  • Kula, V. & Baykut, E. (2017). Volatility in emerging stock markets: Measuring and comparing volatility structure of BRIC and BIST. V. Anadolu International Conference in Economics, Eskişehir/Türkiye. google scholar
  • Kutlar, A. & Torun, P. (2012). Selecting An appropriate Generalized Conditional Heteroscedastic Model for the Daily ISE 100 Index Returns. Third International Economy Conference, Çeşme/İzmir/Türkiye, pp. 1-24. google scholar
  • Kutlu, M. & Karakaya, A. (2020). Return and volatility spillover effects between the Türkiye and the Russia stock market. Journal of Economic and Administrative Sciences, 37(4), 456-470. https://doi.org/10.1108/ JEAS-10-2019-0114 google scholar
  • Kuttu, S. (2018). Modelling Long Memory in Volatility in Sub-Saharan African Equity Markets. Research in International Business and Finance, 44, 176-185. google scholar
  • Lahmiri, S., & Bekiros, S. (2021). The effect of COVID-19 on long memory in returns and volatility of cryptocurrency and stock markets. Chaos, Solitons & Fractals, 151, 111221. google scholar
  • Liesenfeld, R. (2001). A generalized bivariate mixture model for stock price volatility and trading volume. Journal of Econometrics, 104, 141-178. google scholar
  • Lin, W.K., Engle, R. & Ito, T. (1994). Do bulls and bears move across borders? International transmission of stock returns and volatility. The Review of Financial Studies, 7(3), 507-538. google scholar
  • Liu, M. (2000). Modeling long memory in stock market volatility. Journal of Econometrics, 99, 139-171. google scholar
  • Liu, X., H, An., Li, H., Chen, Z., Feng, S. & Wen, S. (2017). Features of spillover networks in international financial markets: Evidence from the G20 Countries. Physica A: Statistical Mechanics and Its Applicati-ons, 479, 265-78. https://doi.Org/10.1016/j.physa.2017.03.016 google scholar
  • Lu, F-B., Hong, Y-M., Wang, S-Y., Lai, K-K. & Liu, J. (2014). Time-varying Granger causality tests for applications in global crude oil markets. Energy Economics, 42, 289-298. https://doi.org/10.1016/j.ene-co.2014.01.002 google scholar
  • Lyocsa, S., Molnar, P & Vyrost, T. (2021). Stock market volatility forecasting: Do we need high-frequency data? International Journal of Forecasting, 37(3), 1092-1110. https://doi.org/10.1016/j.ijfore-cast.2020.12.001 google scholar
  • Mandelbrot, B. (1963). The variation of certain speculative prices. The Journal of Business. 36 (4), 394-419. google scholar
  • Mclver, R. P. & Kang, S. H. (2020). Financial crises and the dynamics of the spillovers between the U.S. and BRICS stock markets. Research in International Business and Finance, 54, 1-45. https://doi. org/10.1016/j.ribaf.2020.101276 google scholar
  • Mensi, W., Maitra, D., Vo, X. V. & Kang, S. H. (2020). Asymmetric volatility connectedness among main international stock markets: A high frequency analysis. Borsa İstanbul Review, 21(3), 291-306. https:// doi.org/10.1016/j.bir.2020.12.003 google scholar
  • Nasr, A. B., Lux, T., Ajmi, A. N. & Gupta, R. (2016). Forecasting the Volatility of the Dow Jones Islamic Stock Market Index: Long memory vs. Regime Switching. International Review of Economics and Fi-nance, 45, 559-571. google scholar
  • Nelson, D. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347-370. https://doi.org/10.2307/2938260 google scholar
  • Özden, H. Ü. (2008). Analysis of Istanbul Stock Exchange 100 Index’s return volatility. İstanbul Commerce University Journal of Social Sciences, 7(13), 339-350. google scholar
  • Pantelidis, T. & Pittis, N. (2004). Testing for Granger causality in variance in the presence of causality in mean. Economic Letters, 85, 201-207. https://doi.org/10.1016/j.econlet.2004.04.006 google scholar
  • Pindyck, R.S., (2004). Volatility in natural gas and oil markets. Unpublished Manuscript, Massachusetts Institute of Technology, Cambridge, MA. google scholar
  • Poon, S.-H. (2005). A Practical Guide to Forecasting Financial Market Volatility. West Sussex: John Wiley & Sons Inc. google scholar
  • Poon, S. H., & Granger, C. W. (2003). Forecasting volatility in financial markets: A review. Journal of Eco-nomic Literature, 41(2), 478-539. google scholar
  • Pong, S., Shackleton, M. B. &Taylor, S. J. (2008). Distinguishing short and long memory volatility specifi-cations”, The Econometrics Journal, 11(3), 617-637. https://doi.org/10.1111/j.1368-423X.2008.00251.x google scholar
  • Rodrigues, P.M.M. & Rubia, A. (2007). Testing for causality in variance under non-stationarity in variance. Econ. Lett, 97, 133-137. google scholar
  • Ross, S. A. (1989). Information and volatility: The no-arbitrage martingale approach to timing and resolution irrelevancy. The Journal of Finance, 44(1), 1-17. google scholar
  • Saleem, K. (2009). International linkage of the Russian market and the Russian financial crisis: a multivariate GARCH analysis. Research in International Business and Finance, 23, 243-256. google scholar
  • Xekalaki, E. and Degiannakis, S. (2010). ARCH Models for Financial Applications, West Sussex: John Wi-ley & Son Ltd. google scholar
  • Terasvirta, T. (2009), “An introduction to univariate GARC”, Andersen, G.T., Davis, R. A., Kreib, J-P., Mi-kosch, T. (Ed), Handbook of Financial Time Series, Heidelberg: Springer Berlin, pp. 17-42. google scholar
  • Topaloglu, E. E. (2020). Volatility structure and volatility spillover of Borsa Istanbul Stock Indexes: The case of BIST Industrial and Financial Indexes with GARCH And MGARCH Models. Dumlupınar University Journal of Social Sciences, 63, 17-38. google scholar
  • Tse, Y. K. (1998). The conditional heteroscedasticity of the Yen-Dollar exchange rate. Journal of Applied Econometrics, 13(1), 49-55. google scholar
  • Van Dijk, D., Osborn, D.R. & Sensier, M. (2005). Testing for causality invariance in the presence of breaks. Econ. Lett, 89, 193-199. google scholar
  • Wang, L., Ma, F., Liu, J. & Yang, L. (2020). Forecasting stock price volatility: New evidence from the GARCH-MIDAS model. International Journal of Forecasting, 36(2), 684-694. https://doi.org/10.1016/j. ijforecast.2019.08.005 google scholar
  • Wu, X. & Wang, X. (2020). Forecasting volatility using realized stochastic volatility model with time-var-ying leverage effect”, Finance Research Letters, 34, 101271. https://doi.org/10.1016/j.frl.2019.08.019 google scholar
  • Yarovaya,L., Brzeszczynski,J. & Lau, M. (2016). Intra- and inter-regional return and volatility spillovers ac-ross emerging and developed markets: Evidence from stock indices and stock index futures. International Review of Financial Analysis, 43, 96-114. https://doi.org/10.1016/j.irfa.2015.09.004 google scholar
  • Yıldırım, D.C. Cevik, E. İ. & Esen, Ö. (2020). Time-varying volatility spillovers between oil prices and pre-cious metal prices. Resources Policy, 68, 1-14. https://doi.org/10.1016/j.resourpol.2020.101783 google scholar
  • Yıldız, B. (2016). Volatility forecasting with symmetric and asymmetric GARCH Models: An application on selected ISE sub-sectors. Journal of Accounting and Finance, 83-105 google scholar
  • Yu, J. (2002). Forecasting volatility in the New Zealand stock market. Applied Financial Economics, 12(3), 193-202. https://doi.org/10.1080/09603100110090118 google scholar
  • Zhang, Y., Ma, F. & Liao, Y. (2020). Forecasting global equity market volatilities. International Journal of Forecasting, 36(4), 1454-1475. https://doi.org/10.1016/j.ijforecast.2020.02.007 google scholar
Year 2024, Volume: 53 Issue: 1, 81 - 101, 30.04.2024
https://doi.org/10.26650/ibr.2024.53.162811

Abstract

References

  • Al-Hajieh, H. (2017). Evaluated the success of Fractional Integrated GARCH models on prediction stock market return volatility in Gulf Arab stock markets. International Journal of Economics and Finance, 9(7), 200-213. google scholar
  • Altuntas Taspunar, S. & Colak, F. D. (2015). Modelling and estimating volatility with ARCH Models At ISE-100 Index. İstanbul Management Journal, 26 (79), 208-223. google scholar
  • Andersen, T. G. & Bollerslev, T. (1997). Heterogeneous information arrivals and return volatility dynamics: Uncovering the long-run in high frequency returns. The Journal Of Finance, 52(3), 975-1005. google scholar
  • Ay, G. & Gün, M. (2020). Volatility modelling in Borsa Istanbul Stock Market: An application on BIST Banking Index. Business & Management Studies: An International Journal, 8(5), 3795-3814. https://doi. org/10.15295/bmij.v8i5.1547 google scholar
  • Baillie, R., Bollerslev, T. & Mikkelsen, H. O. (1996). Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity. Journal Of Econometrics, 74(1), 3-30. https://doi.org/10.1016/S0304-4076(95)01749-6 google scholar
  • Baillie, R. T. & Morana, C. (2009). Modelling long memory and structural breaks in conditional variances: An adaptive FIGARCH approach. Journal of Economic Dynamics & Control, 33, 1577-1592. google scholar
  • Bayramoglu, M. F. & Abasız, T. (2017). The analysis of volatility spillover effect between emerging market indices. Journal of Accounting and Finance, 183-199. google scholar
  • Beirne,J., Caporale, G. M., Schulze-Ghattas, M. & Spagnolo, N. (2010). Global and regional spillovers in emerging stock markets: A multivariate GARCH-in-mean analysis. Emerging Markets Review, 11 (3), 250-260. https://doi.org/10.1016/j.ememar.2010.05.002 google scholar
  • Bentes, S. R. (2014). Measuring persistence in stock market volatility using the FIGARCH approach. Physi-ca A: Statistical Mechanics and its Applications, 408, 190-197. google scholar
  • Bentes, S. R., Menezes, R. & Mendes, D. (2008). Long Memory and Volatility Clustering: Is the Empirical Evidence Consistent Across Stock Markets ? Physica A, 387: 3826-3830. google scholar
  • Berberoglu, M. (2020). The investigation of volatility spillover effect between stock markets of Türkiye, Italy, Greece and Russia. Business & Management Studies: An international Journal, 8(2), 1576-1598. https://doi.org/10.15295/bmij.v8i2.1475 google scholar
  • Berger, D., Chaboud, A., & Hjalmarsson, E. (2009). What drives volatility persistence in the foreign exchan-ge market? Journal of Financial Economics, 94(2), 192-213. google scholar
  • Bhowmik, D .(2013). Stock Market Volatility: An Evaluation. International Journal of Scientific and Rese-arch Publications, 3 (10), 1-17. google scholar
  • Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics, 31, 307-327. https://doi.org/10.1016/0304-4076(86)90063-1 google scholar
  • Bollerslev, T., Engle, R. F. & Nelson, D. B. (1994). ARCH models. Handbook of Econometrics, 4, 29593038. google scholar
  • Bollerslev, T. & Mikkelsen, H. (1996). Modeling and pricing long memory in stock market volatility. Jour-nal of Econometrics, 73(1), 151-184. https://doi.org/10.1016/0304-4076(95)01736-4 google scholar
  • Bose, S. (2007). Understanding the Volatility Characteristics and Transmisson Effect in the Indian Stock Index and Index Futures Market, ICRA Bulletin- Money & Finance, 139-162 google scholar
  • Brooks, R. (2007). Power ARCH modelling of the volatility of emerging equity markets. Emerging Markets Review, 8, 124-133. google scholar
  • Brooks, C. (2008). Introductory Econometrics for Finance. Second Edition. Cambridge: Cambridge Uni-versity Press. google scholar
  • Büberkökü, Ö. & Kızıldere, C. (2017). Examining the properties of BIST100 Index return volatility. V. Anadolu International Conference in Economics, Eskişehir/Türkiye. google scholar
  • Caglı, E. Ç., Mandacı, P. E. & Kahyaoğlu, H. (2011). Volatility shifts and persistence in variance: Evidence from the sector indices of Istanbul Stock Exchange. International Journal of Economic Sciences and Applied Research, 4(3), 119-140. google scholar
  • Campbell, J. Y., & Hentschel, L. (1992). No news is good news: An asymmetric model of changing volatility in stock returns. Journal of Financial Economics, 31(3), 281-318. google scholar
  • Carroll, R. & Kearney, C. (2009), “GARCH modeling of stock market volatility”, Gregoriou, G. N. (Ed), Stock Market Volatility, Chapman & Hall/CRC, pp. 71-90. google scholar
  • Celik, İ., Özdemir, A. & Gülbahar Demir, S. (2018). Return and volatility spillover between Islamic stock indices: An application of multivarite VAR-EGARCH on developed and emerging markets. Bulletin of Accounting and Finance Reviews, 1(2), 89-100. google scholar
  • Çevik, E. İ. (2012). The testing of effıcient market hypothesis ın the Istanbul stock exchange by using long memory models: A sector-specific analysis. Journal of Yaşar University, 7(26), 4437-4454. google scholar
  • Çevik, E. İ. &Topaloglu, G. (2014). Long memory and structural breaks on volatility: Evidence from Borsa Istanbul. Balkan Journal of Social Sciences, 3(6), 40-55. google scholar
  • Chang, C-L., McALeer, M. & Tansuchat, R. (2012). Modelling long memory volatility in agricultu-ral commodity futures returns. Annals of Financial Economics, 7(2),1-27. https://doi.org/10.1142/ S2010495212500108 google scholar
  • Chikli, W., Aloui, C. & Nguyen, D. K. (2012). Asymmetric Effects and Long Memory in Dynamic Volatility Relationships Between Stock Returns and Exchange Rates. Int. Fin. Markets Inst. and Money. 22, 738757 google scholar
  • Chong, C. W., Ahmad, M. I., & Abdullah, M. Y. (1999). Performance of GARCH models in forecasting stock market volatility. Journal of forecasting, 18(5), 333-343. https://doi.org/10.1002/(SICI)1099-131X(199909)18:5<333::AID-FOR742>3.0.CO;2-K google scholar
  • Christensen, B. J., Nielsen, M. O. & Zhu, J. (2010). Long Memory in Stock Market Volatility and the Volatility-in-Mean Effect: The FIEGARCH-M Model. Journal of Empirical Finance. 17, 460-47 google scholar
  • Davidson, J. (2004). Moment and memory properties of linear conditional heteroscedasticity models and a new model. Journal of Business & Economic Statistics, 22(1), 16-29. google scholar
  • Dedi, L. & Yavas, B.F. (2016). Return and volatility spillovers in equity markets: An investigation using various GARCH methodologies. Cogent Economics& Finance, 4, 1-18. https://doi.org/10.1080/233220 39.2016.1266788 google scholar
  • Ding, Z., Granger, C.W. J. & Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1(1), 83-106. https://doi.org/10.1016/0927-5398(93)90006-D google scholar
  • Engle, R. F., Ito, T. & Lın, W-L. (1990). Meteor showers or heat waves? Heteroskedastic intra-daily volatility in the foreign exchange market. Econometrica, 58(3), 525-542. https://doi.org/10.2307/2938189 google scholar
  • Engle, R. F. & Ng, V. K. (1993). Measuring and testing the impact of news on volatility. The Journal of Finance, 48(5), 1749-1778. https://doi.org/10.1111/j.1540-6261.1993.tb05127.x google scholar
  • Ewing, T. B. & Malik, F. (2017). Modelling asymmetric volatility in oil prices under structural breaks. Energy Economics, 63, 227-233. https://doi.org/10.1016/j.eneco.2017.03.001 google scholar
  • French, K. R., Schwert, G. W., & Stambaugh, R. F. (1987). Expected stock returns and volatility. Journal of financial Economics, 19(1), 3-29. google scholar
  • Gaye Gencer, H., & Demiralay, S. (2016). Volatility modeling and value-at-risk (var) forecasting of emerging stock markets in the presence of long memory, asymmetry, and skewed heavy tails. Emerging Markets Finance and Trade, 52(3), 639-657. google scholar
  • Glosten, L. R., Jagannathan, R. & Runkle, D. E. (1993). On the Relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779- 1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x google scholar
  • Gökbulut, R. İ. (2017). An empirical analysis of volatility transmission between BIST and international stock markets. The International Journal of Economic and Social Research, 13(1), 141-159. google scholar
  • Günay, S. (2014). Long memory property and structural breaks in volatility: Evidence from Türkiye and Brazil. International Journal of Economics and Finance, 6(12), 119. google scholar
  • Gürsoy, S. & Eroglu, Ö. (2016). Return and volatility spillovers among the share markets of emerging eco-nomies: an analysis from 2006 to 2015 years. Mehmet Akif Ersoy University Journal of Economics and Administrative Sciences, 3(5), 16-33 google scholar
  • Hong, Y. (2001). A test for volatility spillover with application to exchange rates. Journal of Economet-rics, 103 (1-2), 183-224. https://doi.org/10.1016/S0304-4076(01)00043-4 google scholar
  • Kalotychou, E., & Staikouras, K. S. (2009), “An overview of the issues surrounding stock market volatility”, Gregoriou, G. N. (Ed), Stock Market Volatility, Plattsburgh/New York: A Chapman & Hall /CRC Finance, pp. 3-29. google scholar
  • Karabacak, M., Mecik, O. & Genc, E. (2014). Estimating the volatility of BIST 100 Index return and gold return index with conditional heteroscedasticity models. International Journal of Alanya Faculty of Bu-siness, 6(1), 79-90. google scholar
  • Koutmos, G. & Booth, G.G. (1995). Asymmetric volatility transmission in international stock markets. Jour-nal of International Money and Finance, 14(6), 747-762. google scholar
  • Koy, A. & Ekim. (2016). Modelling the volatility of Istanbul Stock Exchange Sector Indexes. E- Journal of the Faculty of Economic and Administrative Sciences, 5(2), 1-23. google scholar
  • Kula, V. & Baykut, E. (2017). Volatility in emerging stock markets: Measuring and comparing volatility structure of BRIC and BIST. V. Anadolu International Conference in Economics, Eskişehir/Türkiye. google scholar
  • Kutlar, A. & Torun, P. (2012). Selecting An appropriate Generalized Conditional Heteroscedastic Model for the Daily ISE 100 Index Returns. Third International Economy Conference, Çeşme/İzmir/Türkiye, pp. 1-24. google scholar
  • Kutlu, M. & Karakaya, A. (2020). Return and volatility spillover effects between the Türkiye and the Russia stock market. Journal of Economic and Administrative Sciences, 37(4), 456-470. https://doi.org/10.1108/ JEAS-10-2019-0114 google scholar
  • Kuttu, S. (2018). Modelling Long Memory in Volatility in Sub-Saharan African Equity Markets. Research in International Business and Finance, 44, 176-185. google scholar
  • Lahmiri, S., & Bekiros, S. (2021). The effect of COVID-19 on long memory in returns and volatility of cryptocurrency and stock markets. Chaos, Solitons & Fractals, 151, 111221. google scholar
  • Liesenfeld, R. (2001). A generalized bivariate mixture model for stock price volatility and trading volume. Journal of Econometrics, 104, 141-178. google scholar
  • Lin, W.K., Engle, R. & Ito, T. (1994). Do bulls and bears move across borders? International transmission of stock returns and volatility. The Review of Financial Studies, 7(3), 507-538. google scholar
  • Liu, M. (2000). Modeling long memory in stock market volatility. Journal of Econometrics, 99, 139-171. google scholar
  • Liu, X., H, An., Li, H., Chen, Z., Feng, S. & Wen, S. (2017). Features of spillover networks in international financial markets: Evidence from the G20 Countries. Physica A: Statistical Mechanics and Its Applicati-ons, 479, 265-78. https://doi.Org/10.1016/j.physa.2017.03.016 google scholar
  • Lu, F-B., Hong, Y-M., Wang, S-Y., Lai, K-K. & Liu, J. (2014). Time-varying Granger causality tests for applications in global crude oil markets. Energy Economics, 42, 289-298. https://doi.org/10.1016/j.ene-co.2014.01.002 google scholar
  • Lyocsa, S., Molnar, P & Vyrost, T. (2021). Stock market volatility forecasting: Do we need high-frequency data? International Journal of Forecasting, 37(3), 1092-1110. https://doi.org/10.1016/j.ijfore-cast.2020.12.001 google scholar
  • Mandelbrot, B. (1963). The variation of certain speculative prices. The Journal of Business. 36 (4), 394-419. google scholar
  • Mclver, R. P. & Kang, S. H. (2020). Financial crises and the dynamics of the spillovers between the U.S. and BRICS stock markets. Research in International Business and Finance, 54, 1-45. https://doi. org/10.1016/j.ribaf.2020.101276 google scholar
  • Mensi, W., Maitra, D., Vo, X. V. & Kang, S. H. (2020). Asymmetric volatility connectedness among main international stock markets: A high frequency analysis. Borsa İstanbul Review, 21(3), 291-306. https:// doi.org/10.1016/j.bir.2020.12.003 google scholar
  • Nasr, A. B., Lux, T., Ajmi, A. N. & Gupta, R. (2016). Forecasting the Volatility of the Dow Jones Islamic Stock Market Index: Long memory vs. Regime Switching. International Review of Economics and Fi-nance, 45, 559-571. google scholar
  • Nelson, D. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347-370. https://doi.org/10.2307/2938260 google scholar
  • Özden, H. Ü. (2008). Analysis of Istanbul Stock Exchange 100 Index’s return volatility. İstanbul Commerce University Journal of Social Sciences, 7(13), 339-350. google scholar
  • Pantelidis, T. & Pittis, N. (2004). Testing for Granger causality in variance in the presence of causality in mean. Economic Letters, 85, 201-207. https://doi.org/10.1016/j.econlet.2004.04.006 google scholar
  • Pindyck, R.S., (2004). Volatility in natural gas and oil markets. Unpublished Manuscript, Massachusetts Institute of Technology, Cambridge, MA. google scholar
  • Poon, S.-H. (2005). A Practical Guide to Forecasting Financial Market Volatility. West Sussex: John Wiley & Sons Inc. google scholar
  • Poon, S. H., & Granger, C. W. (2003). Forecasting volatility in financial markets: A review. Journal of Eco-nomic Literature, 41(2), 478-539. google scholar
  • Pong, S., Shackleton, M. B. &Taylor, S. J. (2008). Distinguishing short and long memory volatility specifi-cations”, The Econometrics Journal, 11(3), 617-637. https://doi.org/10.1111/j.1368-423X.2008.00251.x google scholar
  • Rodrigues, P.M.M. & Rubia, A. (2007). Testing for causality in variance under non-stationarity in variance. Econ. Lett, 97, 133-137. google scholar
  • Ross, S. A. (1989). Information and volatility: The no-arbitrage martingale approach to timing and resolution irrelevancy. The Journal of Finance, 44(1), 1-17. google scholar
  • Saleem, K. (2009). International linkage of the Russian market and the Russian financial crisis: a multivariate GARCH analysis. Research in International Business and Finance, 23, 243-256. google scholar
  • Xekalaki, E. and Degiannakis, S. (2010). ARCH Models for Financial Applications, West Sussex: John Wi-ley & Son Ltd. google scholar
  • Terasvirta, T. (2009), “An introduction to univariate GARC”, Andersen, G.T., Davis, R. A., Kreib, J-P., Mi-kosch, T. (Ed), Handbook of Financial Time Series, Heidelberg: Springer Berlin, pp. 17-42. google scholar
  • Topaloglu, E. E. (2020). Volatility structure and volatility spillover of Borsa Istanbul Stock Indexes: The case of BIST Industrial and Financial Indexes with GARCH And MGARCH Models. Dumlupınar University Journal of Social Sciences, 63, 17-38. google scholar
  • Tse, Y. K. (1998). The conditional heteroscedasticity of the Yen-Dollar exchange rate. Journal of Applied Econometrics, 13(1), 49-55. google scholar
  • Van Dijk, D., Osborn, D.R. & Sensier, M. (2005). Testing for causality invariance in the presence of breaks. Econ. Lett, 89, 193-199. google scholar
  • Wang, L., Ma, F., Liu, J. & Yang, L. (2020). Forecasting stock price volatility: New evidence from the GARCH-MIDAS model. International Journal of Forecasting, 36(2), 684-694. https://doi.org/10.1016/j. ijforecast.2019.08.005 google scholar
  • Wu, X. & Wang, X. (2020). Forecasting volatility using realized stochastic volatility model with time-var-ying leverage effect”, Finance Research Letters, 34, 101271. https://doi.org/10.1016/j.frl.2019.08.019 google scholar
  • Yarovaya,L., Brzeszczynski,J. & Lau, M. (2016). Intra- and inter-regional return and volatility spillovers ac-ross emerging and developed markets: Evidence from stock indices and stock index futures. International Review of Financial Analysis, 43, 96-114. https://doi.org/10.1016/j.irfa.2015.09.004 google scholar
  • Yıldırım, D.C. Cevik, E. İ. & Esen, Ö. (2020). Time-varying volatility spillovers between oil prices and pre-cious metal prices. Resources Policy, 68, 1-14. https://doi.org/10.1016/j.resourpol.2020.101783 google scholar
  • Yıldız, B. (2016). Volatility forecasting with symmetric and asymmetric GARCH Models: An application on selected ISE sub-sectors. Journal of Accounting and Finance, 83-105 google scholar
  • Yu, J. (2002). Forecasting volatility in the New Zealand stock market. Applied Financial Economics, 12(3), 193-202. https://doi.org/10.1080/09603100110090118 google scholar
  • Zhang, Y., Ma, F. & Liao, Y. (2020). Forecasting global equity market volatilities. International Journal of Forecasting, 36(4), 1454-1475. https://doi.org/10.1016/j.ijforecast.2020.02.007 google scholar
There are 84 citations in total.

Details

Primary Language English
Subjects Business Administration
Journal Section Articles
Authors

Ahmet Galip Gençyürek 0000-0002-5842-7942

Publication Date April 30, 2024
Submission Date February 2, 2022
Published in Issue Year 2024 Volume: 53 Issue: 1

Cite

APA Gençyürek, A. G. (2024). Volatility Modeling and Spillover: The Turkish and Russian Stock Markets. Istanbul Business Research, 53(1), 81-101. https://doi.org/10.26650/ibr.2024.53.162811

For more information about IBR and recent publications, please visit us at IU Press.