Sparse Bayesian time-varying covariance estimation in many dimensions

Gregor Kastner

111 citations0/0 files storedJournal of Econometrics

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Financial Risk and Volatility Modeling Statistical Methods and Inference Spatial and Panel Data Analysis

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.

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References

1993Common risk factors in the returns on stocks and bonds 1997On Persistence in Mutual Fund Performance 2002Determining the Number of Factors in Approximate Factor Models 1966Mutual Fund Performance 1998Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models 1994Multivariate Stochastic Variance Models 2004Honey, I Shrunk the Sample Covariance Matrix 2009Large Bayesian vector auto regressions

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2019Large-scale portfolio allocation under transaction costs and model uncertainty 2023Large Order-Invariant Bayesian VARs with Stochastic Volatility 2021Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol 2011Bayesian Analysis of Coefficient Instability in Dynamic Regressions 2022Comparing stochastic volatility specifications for large Bayesian VARs 2020Comparing high-dimensional conditional covariance matrices: Implications for portfolio selection

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