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Precondition stock and stock indices volatility modeling based on market diversification potential: Evidence from Russian market

Author

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  • Nagapetyan, Artur

    (Far Eastern Federal University, Vladivostok, Russian Federation)

Abstract

The approaches to modeling stocks volatility, stock indices volatility and financial portfolios volatility which differ from the existing ones by taking into account the dynamics of the Market Diversification Potential Index, are proposed. The presence of a significant effect of the Market Diversification Potential Index on the volatility of stocks, stock indices, and financial portfolios was demonstrated. A model has been developed for predicting the volatility of returns on stocks, stock indices, and financial portfolios, which takes into account the impact of the dynamics of the Market Diversification Potential Index and allows us to significantly increase the forecast qualities of existing models. The results of using a non-sampling forecasting technique for one period when working with financial assets demonstrated realized stock and stock index volatility, simulating realized volatility of random financial portfolios and modeling Markowitz effective financial portfolios.

Suggested Citation

  • Nagapetyan, Artur, 2019. "Precondition stock and stock indices volatility modeling based on market diversification potential: Evidence from Russian market," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 56, pages 45-61.
    • Handle: RePEc:ris:apltrx:0380
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    1. Mensi, Walid & Beljid, Makram & Boubaker, Adel & Managi, Shunsuke, 2013. "Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold," Economic Modelling, Elsevier, vol. 32(C), pages 15-22.
    2. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2008. "Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise," Econometrica, Econometric Society, vol. 76(6), pages 1481-1536, November.
    3. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    4. Mihaela Craioveanu & Eric Hillebrand, 2012. "Why It Is Ok To Use The Har-Rv(1,5,21) Model," Working Papers 1201, University of Central Missouri, Department of Economics & Finance, revised Aug 2012.
    5. Lahaye, Jerome & Shaw, Philip, 2014. "Can we reject linearity in an HAR-RV model for the S&P 500? Insights from a nonparametric HAR-RV," Economics Letters, Elsevier, vol. 125(1), pages 43-46.
    6. Antonakakis, Nikolaos, 2012. "Exchange return co-movements and volatility spillovers before and after the introduction of euro," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(5), pages 1091-1109.
    7. Mollick, André Varella & Assefa, Tibebe Abebe, 2013. "U.S. stock returns and oil prices: The tale from daily data and the 2008–2009 financial crisis," Energy Economics, Elsevier, vol. 36(C), pages 1-18.
    8. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    9. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    10. Ling, Shiqing & McAleer, Michael, 2003. "Asymptotic Theory For A Vector Arma-Garch Model," Econometric Theory, Cambridge University Press, vol. 19(2), pages 280-310, April.
    11. Arouri, Mohamed El Hedi & Jouini, Jamel & Nguyen, Duc Khuong, 2012. "On the impacts of oil price fluctuations on European equity markets: Volatility spillover and hedging effectiveness," Energy Economics, Elsevier, vol. 34(2), pages 611-617.
    12. Zhang, Lan & Mykland, Per A. & Ait-Sahalia, Yacine, 2005. "A Tale of Two Time Scales: Determining Integrated Volatility With Noisy High-Frequency Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1394-1411, December.
    13. Giovanni Barone Adesi & Robert F. Engle & Loriano Mancini, 2014. "A GARCH Option Pricing Model with Filtered Historical Simulation," Palgrave Macmillan Books, in: Giovanni Barone Adesi (ed.), Simulating Security Returns: A Filtered Historical Simulation Approach, chapter 4, pages 66-108, Palgrave Macmillan.
    14. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    15. Markowitz, Harry M, 1991. "Foundations of Portfolio Theory," Journal of Finance, American Finance Association, vol. 46(2), pages 469-477, June.
    16. Aganin, Artem, 2017. "Forecast comparison of volatility models on Russian stock market," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 48, pages 63-84.
    17. Ceylan, Ozcan, 2012. "Time-Varying Volatility Asymmetry: A Conditioned HAR-RV(CJ) EGARCH-M Model," GIAM Working Papers 12-4, Galatasaray University Economic Research Center.
    18. Markku Lanne & Pentti Saikkonen, 2005. "Non-linear GARCH models for highly persistent volatility," Econometrics Journal, Royal Economic Society, vol. 8(2), pages 251-276, July.
    19. Shcherba, Alexandr, 2014. "Comparing «Realized volatility» models in the VaR calculation for the Russian equity market," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 34(2), pages 120-136.
    20. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    21. Nelson M. P. C. Areal & Stephen J. Taylor, 2002. "The realized volatility of FTSE‐100 futures prices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 22(7), pages 627-648, July.
    22. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    23. Subbotin, Alexandre, 2009. "Volatility Models: from Conditional Heteroscedasticity to Cascades at Multiple Horizons," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 15(3), pages 94-138.
    24. Hansen, Peter R. & Lunde, Asger, 2006. "Realized Variance and Market Microstructure Noise," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 127-161, April.
    25. repec:wsr:wpaper:y:2012:i:080 is not listed on IDEAS
    26. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    27. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.
    28. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    29. Jin‐Chuan Duan, 1995. "The Garch Option Pricing Model," Mathematical Finance, Wiley Blackwell, vol. 5(1), pages 13-32, January.
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    Cited by:

    1. Kopaliani, R. & Denisov, N., 2023. "Composite option pricing and the volatility surface construction," Journal of the New Economic Association, New Economic Association, vol. 60(3), pages 27-48.
    2. Vladimir Pyrlik & Pavel Elizarov & Aleksandra Leonova, 2021. "Forecasting Realized Volatility Using Machine Learning and Mixed-Frequency Data (the Case of the Russian Stock Market)," CERGE-EI Working Papers wp713, The Center for Economic Research and Graduate Education - Economics Institute, Prague.

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    More about this item

    Keywords

    volatility clustering; realized correlation; DCC; MEWMA; MGARCH; market diversification potential index; MCS; correlation simulation; realized volatility; effective portfolio; out-of-sample forecasting.;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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