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Volatility forecasting using global stochastic financial trends extracted from non-synchronous data

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  • Grigoryeva, Lyudmila
  • Ortega, Juan-Pablo
  • Peresetsky, Anatoly

Abstract

A method based on various linear and nonlinear state space models used to extract global stochastic financial trends (GST) out of non-synchronous financial data is introduced. These models are constructed in order to take advantage of the intraday arrival of closing information coming from different international markets so that volatility description and forecasting is improved. A set of three major asynchronous international stock market indices is considered in order to empirically show that this forecasting scheme is capable of significant performance gains when compared to standard parametric models like the dynamic conditional correlation (DCC) family.

Suggested Citation

  • Grigoryeva, Lyudmila & Ortega, Juan-Pablo & Peresetsky, Anatoly, 2018. "Volatility forecasting using global stochastic financial trends extracted from non-synchronous data," Econometrics and Statistics, Elsevier, vol. 5(C), pages 67-82.
    • Handle: RePEc:eee:ecosta:v:5:y:2018:i:c:p:67-82
    DOI: 10.1016/j.ecosta.2017.01.003
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    1. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    2. Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2003. "Choosing the Best Volatility Models: The Model Confidence Set Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 839-861, December.
    3. Chang, Yoosoon & Isaac Miller, J. & Park, Joon Y., 2009. "Extracting a common stochastic trend: Theory with some applications," Journal of Econometrics, Elsevier, vol. 150(2), pages 231-247, June.
    4. Bauwens, Luc & Grigoryeva, Lyudmila & Ortega, Juan-Pablo, 2016. "Estimation and empirical performance of non-scalar dynamic conditional correlation models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 17-36.
    5. Anatoly A. Peresetsky & Ruslan I. Yakubov, 2017. "Autocorrelation in an unobservable global trend: does it help to forecast market returns?," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 7(1/2), pages 152-169.
    6. Byung Yoon Bae & Dong Heon Kim, 2011. "Global and Regional Yield Curve Dynamics and Interactions: The Case of Some Asian Countries," International Economic Journal, Taylor & Francis Journals, vol. 25(4), pages 717-738, December.
    7. Kasa, Kenneth, 1992. "Common stochastic trends in international stock markets," Journal of Monetary Economics, Elsevier, vol. 29(1), pages 95-124, February.
    8. Durdyev, Ruslan & Peresetsky, Anatoly, 2014. "Autocorrelation in the global stochastic trend," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 35(3), pages 39-58.
    9. Choudhry, Taufiq & Lu, Lin & Peng, Ke, 2007. "Common stochastic trends among Far East stock prices: Effects of the Asian financial crisis," International Review of Financial Analysis, Elsevier, vol. 16(3), pages 242-261.
    10. Jeon, Bang Nam & Chiang, Thomas C., 1991. "A system of stock prices in world stock exchanges: Common stochastic trends for 1975-1990," Journal of Economics and Business, Elsevier, vol. 43(4), pages 329-338, November.
    11. Gian Piero Aielli, 2013. "Dynamic Conditional Correlation: On Properties and Estimation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 282-299, July.
    12. Chung, Pin J. & Liu, Donald J., 1994. "Common stochastic trends in pacific rim stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 34(3), pages 241-259.
    13. repec:zbw:bofitp:2013_015 is not listed on IDEAS
    14. 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.
    15. Lucey, Brian M. & Muckley, Cal, 2011. "Robust global stock market interdependencies," International Review of Financial Analysis, Elsevier, vol. 20(4), pages 215-224, August.
    16. Cartea, Álvaro & Karyampas, Dimitrios, 2011. "Volatility and covariation of financial assets: A high-frequency analysis," Journal of Banking & Finance, Elsevier, vol. 35(12), pages 3319-3334.
    17. Bentes, Sónia R., 2015. "On the integration of financial markets: How strong is the evidence from five international stock markets?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 205-214.
    18. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178, December.
    19. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    20. Jesper Rangvid & Carsten Sørensen, 2002. "Convergence in the ERM and Declining Numbers of Common Stochastic Trends," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 1(2), pages 183-213, September.
    21. Felices, Guillermo & Wieladek, Tomasz, 2012. "Are emerging market indicators of vulnerability to financial crises decoupling from global factors?," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 321-331.
    22. Chrétien, Stéphane & Ortega, Juan-Pablo, 2014. "Multivariate GARCH estimation via a Bregman-proximal trust-region method," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 210-236.
    23. Neelabh Rohan & T. V. Ramanathan, 2013. "Nonparametric estimation of a time-varying GARCH model," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(1), pages 33-52, March.
    24. Phengpis, Chanwit & Apilado, Vince P., 2004. "Economic interdependence and common stochastic trends: A comparative analysis between EMU and non-EMU stock markets," International Review of Financial Analysis, Elsevier, vol. 13(3), pages 245-263.
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    Cited by:

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    2. Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2018. "On the determinants of bitcoin returns: A LASSO approach," Finance Research Letters, Elsevier, vol. 27(C), pages 235-240.
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    4. Conlon, Thomas & Cotter, John & Gençay, Ramazan, 2018. "Long-run wavelet-based correlation for financial time series," European Journal of Operational Research, Elsevier, vol. 271(2), pages 676-696.
    5. Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2019. "The effects of markets, uncertainty and search intensity on bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 220-242.

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    More about this item

    Keywords

    Multivariate volatility modeling and forecasting; Global stochastic trend; Extended Kalman filter; Dynamic conditional correlations (DCC); Non-synchronous data;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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