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Factor Multivariate Stochastic Volatility via Wishart Processes


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  • Alexander Philipov
  • Mark Glickman
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    This paper proposes a high dimensional factor multivariate stochastic volatility (MSV) model in which factor covariance matrices are driven by Wishart random processes. The framework allows for unrestricted specification of intertemporal sensitivities, which can capture the persistence in volatilities, kurtosis in returns, and correlation breakdowns and contagion effects in volatilities. The factor structure allows addressing high dimensional setups used in portfolio analysis and risk management, as well as modeling conditional means and conditional variances within the model framework. Owing to the complexity of the model, we perform inference using Markov chain Monte Carlo simulation from the posterior distribution. A simulation study is carried out to demonstrate the efficiency of the estimation algorithm. We illustrate our model on a data set that includes 88 individual equity returns and the two Fama-French size and value factors. With this application, we demonstrate the ability of the model to address high dimensional applications suitable for asset allocation, risk management, and asset pricing.

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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

    Volume (Year): 25 (2006)
    Issue (Month): 2-3 ()
    Pages: 311-334

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    Handle: RePEc:taf:emetrv:v:25:y:2006:i:2-3:p:311-334

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    Keywords: Bayesian time series; Factor models; Stochastic covariance; Time-varying correlation;


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    Cited by:
    1. Deschamps, Philippe J., 2009. "Bayesian estimation of an extended local scale stochastic volatility model," DQE Working Papers 15, Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland, revised 12 Nov 2011.
    2. K. Triantafyllopoulos, 2008. "Multivariate stochastic volatility with Bayesian dynamic linear models," Papers 0802.0214,
    3. Asai, Manabu & McAleer, Michael, 2009. "The structure of dynamic correlations in multivariate stochastic volatility models," Journal of Econometrics, Elsevier, vol. 150(2), pages 182-192, June.


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