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Title: Realized stochastic volatility models with skew-t distributions Authors:  Makoto Takahashi - Hosei University (Japan) [presenting]
Yasuhiro Omori - University of Tokyo (Japan)
Toshiaki Watanabe - Hitotsubashi University (Japan)
Yuta Yamauchi - Nagoya University (Japan)
Abstract: Predicting volatility and quantiles of financial returns is essential to measure the financial tail risk such as value-at-risk and expected shortfall. There are two important aspects of volatility and quantile forecasts: the distribution of financial returns and the estimation of the volatility. Building on the traditional stochastic volatility model, the realized stochastic volatility model incorporates realized volatility as the precise estimator of the volatility. Using three types of skew-t distributions, the model is extended to capture the well-known characteristics of the return distribution, namely skewness and heavy tails. In addition to the normal and Student's t distributions, included as the special cases of the skew-t distributions, two of them contain the skew-normal, and hence allow more flexible modeling of the return distribution. The Bayesian estimation scheme via a Markov chain Monte Carlo method is developed and applied to major stock indices. The empirical study using the US and Japanese stock indices data suggests that incorporating both skewness and heavy tail to daily returns is important for volatility and quantile forecasts.