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Factor multivariate stochastic volatility models of high dimension

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  • Benjamin Poignard
  • Manabu Asai

Abstract

Building upon factor decomposition to overcome the curse of dimensionality inherent in multivariate volatility processes, we develop a factor model-based multivariate stochastic volatility (fMSV) framework. We propose a two-stage estimation procedure for the fMSV model: in the first stage, estimators of the factor model are obtained, and in the second stage, the MSV component is estimated using the estimated common factor variables. We derive the asymptotic properties of the estimators, taking into account the estimation of the factor variables. The prediction performances are illustrated by finite-sample simulation experiments and applications to portfolio allocation.

Suggested Citation

  • Benjamin Poignard & Manabu Asai, 2024. "Factor multivariate stochastic volatility models of high dimension," Papers 2406.19033, arXiv.org, revised Feb 2026.
    • Handle: RePEc:arx:papers:2406.19033
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    References listed on IDEAS

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    1. Jushan Bai & Kunpeng Li, 2016. "Maximum Likelihood Estimation and Inference for Approximate Factor Models of High Dimension," The Review of Economics and Statistics, MIT Press, vol. 98(2), pages 298-309, May.
    2. Chang, Yoosoon & Park, Joon Y. & Song, Kevin, 2006. "Bootstrapping cointegrating regressions," Journal of Econometrics, Elsevier, vol. 133(2), pages 703-739, August.
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