| Abstract: |
Forecasting volatility and quantiles of financial returns is essential for
accurately measuring financial tail risks, such as value-at-risk and expected
shortfall. The critical elements in these forecasts involve understanding the
distribution of financial returns and accurately estimating volatility. This
paper introduces an advancement to the traditional stochastic volatility
model, termed the realized stochastic volatility model, which integrates
realized volatility as a precise estimator of volatility. To capture the
well-known characteristics of return distribution, namely skewness and heavy
tails, we incorporate three types of skew-t distributions. Among these, two
distributions include the skew-normal feature, offering enhanced flexibility
in modeling the return distribution. We employ a Bayesian estimation approach
using the Markov chain Monte Carlo method and apply it to major stock indices.
Our empirical analysis, utilizing data from US and Japanese stock indices,
indicates that the inclusion of both skewness and heavy tails in daily returns
significantly improves the accuracy of volatility and quantile forecasts. |