Sparse Bayesian time-varying covariance estimation in many dimensions

Gregor Kastner

Journal of Econometrics, 2019, vol. 210, issue 1, 98-115

Abstract: We address the curse of dimensionality in dynamic covariance estimation by modeling the underlying co-volatility dynamics of a time series vector through latent time-varying stochastic factors. The use of a global–local shrinkage prior for the elements of the factor loadings matrix pulls loadings on superfluous factors towards zero. To demonstrate the merits of the proposed framework, the model is applied to simulated data as well as to daily log-returns of 300 S&P 500 members. Our approach yields precise correlation estimates, strong implied minimum variance portfolio performance and superior forecasting accuracy in terms of log predictive scores when compared to typical benchmarks.

Keywords: Dynamic correlation; Factor stochastic volatility; Curse of dimensionality; Shrinkage; Minimum variance portfolio (search for similar items in EconPapers)
JEL-codes: C32 C38 C53 C58 G11 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (49)

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Working Paper: Sparse Bayesian time-varying covariance estimation in many dimensions (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:210:y:2019:i:1:p:98-115

DOI: 10.1016/j.jeconom.2018.11.007

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