A0702
Title: Modeling tail-dependence of stock returns and news sentiments with copulas
Authors: Feng Li - Central University of Finance and Economics (China) [presenting]
Anastasios Panagiotelis - Monash University (Australia)
Yanfei Kang - Beihang University (China)
Abstract: Tail-dependence modeling based on copula with flexible marginal distributions is widely used in financial time series. Most of the available copula approaches for estimating tail-dependence are restricted within certain types of bivariate copulas due to computational complexity. We propose a general Bayesian approach for jointly modeling high-dimensional tail-dependence for financial returns and related news information.Our method allows for variable selection among the key words in news in the copula tail-dependence parameters. We apply an efficient sampling technique into the posterior inference where the likelihood function is estimated from a random subset of the data, resulting in substantially fewer density MCMC evaluations.
