Title
BAYESIAN SHRINKAGE ESTIMATION OF TIME-VARYING COVARIANCE MATRICES IN FINANCIAL TIME SERIES
Authors
Abstract
Modeling financial returns is challenging because the correlations and variance of returnsare time-varying and the covariance matrices can be quite high-dimensional. In this paper,
we develop a Bayesian shrinkage approach with modified Cholesky decomposition to model
correlations between financial returns. We reparameterize the correlation parameters to
meet their positive definite constraint for Bayesian analysis. To implement an efficient
sampling scheme in posterior inference, hierarchical representation of Bayesian lasso is
used to shrink unknown coefficients in linear regressions. Simulation results show good
sampling properties that iterates from Markov chain Monte Carlo converge quickly. Using a
real data example, we illustrate the application of the proposed Bayesian shrinkage method
in modeling stock returns in Hong Kong.
Keywords
Bayesian shrinkage; dynamic correlations; GARCH; lasso; Markov chain Monte Carlo
Classification-JEL
C11, C32, C58, G17, G32
Pages
369-404