Browse > Article
http://dx.doi.org/10.13106/jafeb.2021.vol8.no8.0297

Regime Dependent Volatility Spillover Effects in Stock Markets Between Kazakhstan and Russia  

CHUNG, Sang Kuck (Department of International Trade, College of Business, Inje University)
ABDULLAEVA, Vasila Shukhratovna (Department of International Trade, College of Business, Inje University)
Publication Information
The Journal of Asian Finance, Economics and Business / v.8, no.8, 2021 , pp. 297-309 More about this Journal
Abstract
In this study, to capture the skewness and kurtosis detected in both conditional and unconditional return distributions of the stock markets of Kazakhstan and Russia, two versions of normal mixture GARCH models are employed. The data set consists of daily observations of the Kazakhstan and Russia stock prices, and world crude oil price, covering the period from 1 June 2006 through 1 March 2021. From the empirical results, incorporating the long memory effect on the returns not only provides better descriptions of dynamic behaviors of the stock market prices but also plays a significant role in improving a better understanding of the return dynamics. In addition, normal mixture models for time-varying volatility provide a better fit to the conditional densities than the usual GARCH specifications and has an important advantage that the conditional higher moments are time-varying. This implies that the volatility skews implied by normal mixture models are more likely to exhibit the features of risk and the direction of the information flow is regime-dependent. The findings of this study contain useful information for diverse purposes of cross-border stock market players such as asset allocation, portfolio management, risk management, and market regulations.
Keywords
Stock Markets; Long Memory Process; Asymmetries; Regime Dependent Spillovers; Bivariate Normal Mixture GARCH Models;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Cheung, Y. W., & Chung, S. K. (2011). A long memory model with normal mixture GARCH. Computational Economics, 38, 517-539. https://doi.org/10.1007/s10614-011-9274-y   DOI
2 Chiang, T. C., Jeon, B. N., & Li, H. (2007). Dynamic correlation analysis of financial contagion: Evidence from Asian markets. Journal of International Money and Finance, 26(7), 1026-1228. https://doi.org/10.1016/j.jimonfin.2007.06.005   DOI
3 Chung, S. K. (2009). Bivariate mixed normal GARCH models and out-of-sample hedge performances. Finance Research Letters, 6(3), 130-137. https://doi.org/10.1016/j.frl.2009.04.001   DOI
4 Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate GARCH models. Journal of Business and Economic Statistics, 20(3), 339-350. https://doi.org/10.1198/073500102 288618487   DOI
5 Filis, G., Degiannakis, S., & Floros, C. (2011). Dynamic correlation between the stock market and oil prices: The case of oil-importing and oil-exporting countries. International Review of Financial Analysis, 20, 152-164. https://doi.org/10.1016/j.irfa.2011.02.014   DOI
6 Granger, C. W. J. (1986). Developments in the study of cointegrated economic variables. Oxford Bulletin of Economics and Statistics, 48(3), 213-228. https://doi.org/10.1111/j.1468-0084.1986.mp48003002.x   DOI
7 Granger, C. W. J., & Joyeux, R. (1980). An introduction to long memory series. Journal of Time Series Analysis, 1, 15-30. https://doi.org/10.1111/j.1467-9892.1980.tb00297.x   DOI
8 Hansen, B. E. (1994). Autoregressive conditional density estimation. International Economic Review, 35(3), 705-730. https://doi.org/10.2307/2527081   DOI
9 Bartlett, M. S. (1946). On the theoretical specification of sampling properties of autocorrelated time series. Journal of the Royal Statistical Society, 8(1), 27-41. https://doi.org/10.2307/2983611   DOI
10 Bierens, H. J., & Guo, S. (1993). Testing stationarity and trend stationarity against the unit root hypothesis. Econometric Reviews, 12(1), 1-32. https://doi.org/10.1080/07474939308800252   DOI
11 Lo, A. W. (1991). Long memory in stock market prices. Econometrica, 59(5), 1279-1313. https://doi.org/10.2307/2938368   DOI
12 Cho, J. H., & Parhizgari, A. M. (2008). East Asian financial contagion under DCC-GARCH. International Journal of Banking and Finance, 6(1), 17-20. https://doi.org/10.32890/ijbf2009.6.1.8380   DOI
13 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   DOI
14 Geweke, J., & Porter-Hudak, S. (1983). The estimation and application of long memory time series models. Journal of Time Series Analysis, 4(4), 221-238. https://doi.org/10.1111/j.1467-9892.1983.tb00371.x   DOI
15 Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461-464. https://doi.org/10.1214/aos/1176344136   DOI
16 Hosking, J. R. M. (1981). Fractional differencing. Biometrika, 68, 165-176. https://doi.org/10.2307/2335817   DOI
17 Jones, C. M., & Kaul, G. (1996). Oil and the stock markets. Journal of Finance, 51, 463-491. https://doi.org/10.1111/j.1540-6261.1996.tb02691.x   DOI
18 Kwiatkowski, D, Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics, 54(1-3), 159-178. https://doi.org/10.1016/0304-4076(92)90104-Y   DOI
19 Mohanty, S. K., Nandha, M., Turkistani, A. Q., & Alaitani, M. Y. (2011). Oil price movements and stock market returns: Evidence from Gulf Cooperation Council (GCC) countries. Global Finance Journal, 22, 42-55. https://doi.org/10.1016/j.gfj.2011.05.004   DOI
20 Sadorsky, P. (1999). Oil price shocks and stock market activity. Energy Economics, 21, 449-469. https://doi.org/10.1016/S0140-9883(99)00020-1   DOI
21 Khan, K., Zhao, H., Zhang, H., Yang, H., Shah, M. H., & Jahanger, A. (2020). The impact of COVID-19 pandemic on stock markets: An empirical analysis of world major stock indices. Journal of Asian Finance, Economics, and Business, 7(7), 463-474. http://doi.org/10.13106/jafeb.2020.vol7.no7.463   DOI
22 Wang, Y., Wu, C., & Yang, L. 2013. Oil price shocks and stock market activities: Evidence from oil-importing and oil-exporting countries. Journal of Comparative Economics, 41, 1220-1239. https://doi.org/10.1016/j.jce.2012.12.004   DOI
23 Boldanov, R., Degiannakis, S., & Filis, G. (2016). Time-varying correlation between oil and stock market volatilities: Evidence from oil-importing and oil-exporting countries. International Review of Financial Analysis, 48, 209-220. https://doi.org/10.1016/j.irfa.2016.10.002   DOI
24 Alexander, C., & Lazar, E. (2006). Normal Mixture GARCH(1,1): Applications to exchange rate modelling. Journal of Applied Econometrics, 21(3), 307-336. https://doi.org/10.1002/jae.849   DOI
25 Bashar, A. Z. (2006). Wild oil prices, but brave stock markets! The case of GCC stock markets. Operational Research, 6, 145-162. https://doi.org/10.1007/BF02941229   DOI
26 Bekaert, G., Harvey, C. R., & Ng, A. (2005). Market integration and contagion. Journal of Business, 8(1), 39-69. http://doi.org/10.1086/426519   DOI
27 Black, F. (1976). Studies of stock price volatility changes. Washington DC: American Statistical Association.
28 Bollerslev, T. (1990). Modeling the coherence in short-run nominal exchange rates: A multivariate generalized ARCH model. The Review of Economics and Statistics, 72(3), 498-505. https://doi.org/10.2307/2109358   DOI
29 Huseynov, F. (2010). Review of CIS stock markets: Future perspectives. Transition Studies Review, 17, 63-79. http://doi.org/10.1007/s11300-010-0136-4   DOI
30 Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2-3), 231-254. https://doi.org/10.1016/0165-1889(88)90041-3   DOI
31 Kon, S. J. (1984). Models of stock returns: A comparison. Journal of Finance, 39(1), 147-165. https://doi.org/10.2307/2327673   DOI
32 Le, T., & Tran H. (2021). The contagion effect from U.S. stock market to the Vietnamese and the Philippine stock markets: The evidence of DCC-GARCH model. Journal of Asian Finance, Economics, and Finance, 8(2), 759-770. https://doi.org/10.13106/jafeb.2021.vol8.no2.0759   DOI
33 Ljung G. M., & Box, G. E. P. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297-303. https://doi.org/10.2307/2335207   DOI
34 MacKinnon, J. G., Haug, A. A., & Michelis, L. (1999). Numerical distribution functions of likelihood ratio tests for cointegration. Journal of Applied Econometrics 14(5), 563-577. https://www.jstor.org/stable/i302160   DOI
35 Breitung, J. (2002). Nonparametric tests for unit roots and cointegration. Journal of Econometrics, 108(2), 343-363. https://doi.org/10.1016/S0304-4076(01)00139-7   DOI
36 Daskalaki, C., & Skiadopoulos, G. (2011). Should investors include commodities in their portfolios after all? New evidence. Journal of Banking & Finance, 35, 2606-2626. https://doi.org/10.1016/j.jbankfin.2011.02.022   DOI
37 Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: a new approach. Econometrica, 59(2), 347-370. https://doi.org/10.2307/2938260   DOI
38 Zhu, H., Su, X, You, W., & Ren, Y. (2017). Asymmetric effects of oil price shocks on stock returns: Evidence from two-stage Markov regime-switching approach. Applied Economics, 49, 2491-2507. https://doi.org/10.1080/00036846.2016.1240351   DOI
39 Miller, J. I., & Ronald, A. R. (2009). Crude oil and stock markets: Stability, instability, and bubbles. Energy Economics, 31, 559-568. https://doi.org/10.1016/j.eneco.2009.01.009   DOI
40 Harvey, C. R., & Siddique, A. (1999). Autoregressive conditional skewness. Journal of Financial and Quantitative Studies, 34(4), 465-487. https://doi.org/10.2307/2676230   DOI
41 Reboredo, J. C. (2010). Nonlinear effects of oil shocks on stock returns: A Markov regime-switching approach. Applied Economics, 42, 3735-3744. https://doi.org/10.1080/00036840802314606   DOI
42 Celik, S. (2012). The more contagion effect on emerging markets: The evidence of DCC-GARCH model. Economic Modelling, 29(5), 1946-1959. https://doi.org/10.1016/j.econmod.2012.06.011   DOI
43 Ball, C. A., & Torous, W. N. (1983). A simplified jump process for common stock returns. Journal of Financial and Quantitative Analysis, 18(1), 53-65. https://doi.org/10.2307/2330804   DOI
44 Haas, M., Mittnik, S., & Paolella, M. S. (2009). Asymmetric multivariate normal mixture GARCH. Computational Statistics and Data Analysis, 53(6), 2129-2154. https://doi.org/10.1016/j.csda.2007.12.018   DOI
45 Chang, K. L., & Yu, S. T. (2013). Does crude oil price play an important role in explaining stock return behavior? Energy Economics, 39, 159-168. https://doi.org/10.1016/j.eneco.2013.05.008   DOI
46 Alshammari, T. S., Ismail, M. T., Al-wadi, S., Saleh, M. H., & Jaber, J. J. (2020). Modeling and forecasting Saudi stock market volatility using wavelet methods. Journal of Asian Finance, Economics, and Business, 7(11), 83-93. https://doi.org/10.13106/jafeb.2020.vol7.no11.083   DOI
47 Akaike, H. (1998). Information theory and an extension of the maximum likelihood principle. In: Parzen, E., Tanabe, K., & Kitagawa, G., (Eds.), Selected papers of Hirotugu Akaike (pp. 199-213). New York: Springer. https://doi.org/10.1007/978-1-4612-1694-0_15
48 Aydogan, B., Gokce, T., & Yelkenci, T. (2017). The impact of oil price volatility on net-oil exporter and importer countries' stock markets. Eurasian Economic Review, 7, 231-253. https://doi.org/10.1007/s40822-017-0065-1   DOI