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A multivariate long memory stochastic volatility model

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  • So, Mike K.P.
  • Kwok, Susanna W.Y.

Abstract

This paper develops a multivariate long-memory stochastic volatility model which allows the multi-asset long-range dependence in the volatility process. The motivation is from the fact that both autocorrelations and cross-correlations of some proxies of exchange rate volatility exhibit strong evidence of long-memory behavior. The statistical properties of the new stochastic volatility model provide theoretical explanation to the common findings that long memory volatility properties are more apparent if we use absolute return as a volatility proxy than squared return. Results of the real data application show that our model outperforms an existing multivariate stochastic volatility model.

Suggested Citation

  • So, Mike K.P. & Kwok, Susanna W.Y., 2006. "A multivariate long memory stochastic volatility model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 362(2), pages 450-464.
  • Handle: RePEc:eee:phsmap:v:362:y:2006:i:2:p:450-464 DOI: 10.1016/j.physa.2005.08.078
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    References listed on IDEAS

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    1. Mike So, 2000. "Long-term memory in stock market volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 10(5), pages 519-524.
    2. Danielsson, Jon, 1998. "Multivariate stochastic volatility models: Estimation and a comparison with VGARCH models," Journal of Empirical Finance, Elsevier, vol. 5(2), pages 155-173, June.
    3. Cheung, Yin-Wong & Diebold, Francis X., 1994. "On maximum likelihood estimation of the differencing parameter of fractionally-integrated noise with unknown mean," Journal of Econometrics, Elsevier, vol. 62(2), pages 301-316, June.
    4. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    5. Ghysels, E. & Harvey, A. & Renault, E., 1995. "Stochastic Volatility," Papers 95.400, Toulouse - GREMAQ.
    6. 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.
    7. 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.
    8. Breidt, F. Jay & Crato, Nuno & de Lima, Pedro, 1998. "The detection and estimation of long memory in stochastic volatility," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 325-348.
    9. Andrew Harvey & Esther Ruiz & Neil Shephard, 1994. "Multivariate Stochastic Variance Models," Review of Economic Studies, Oxford University Press, vol. 61(2), pages 247-264.
    10. Stephen J. Taylor, 1994. "Modeling Stochastic Volatility: A Review And Comparative Study," Mathematical Finance, Wiley Blackwell, vol. 4(2), pages 183-204.
    11. Mike So & K. Lam & W. K. Li, 1999. "Forecasting exchange rate volatility using autoregressive random variance model," Applied Financial Economics, Taylor & Francis Journals, vol. 9(6), pages 583-591.
    12. Mike K.P. So & K. Lam & W.K. Li, 1997. "An Empirical Study of Volatility in Seven Southeast Asian Stock Markets Using ARV Models," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 24(2), pages 261-276.
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    Cited by:

    1. Viviana Fernandez & Brian M Lucey, 2006. "Portfolio management implications of volatility shifts: Evidence from simulated data," Documentos de Trabajo 219, Centro de Economía Aplicada, Universidad de Chile.

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