A Semiparametric Stochastic Volatility Model

By Jun Yu. 2012

63 citations recorded by the source corpus.

In this paper the correlation structure in the classical leverage stochastic volatility (SV) model is generalized based on a linear spline. In the new model the correlation between the return and volatility innovations is time varying and depends nonparametrically on the type of news arrived to the market. Theoretical properties of the proposed model are examined. The model estimation and comparison are conducted by Bayesian methods. The performance of the estimates are examined in simulations. The new model is fitted to daily and weekly US data and compared with the classical SV and GARCH models in terms of their in-sample and out-of-sample performances. Empirical results suggest evidence in favor of the proposed model. In particular, the new model finds strong evidence of time varying leverage effect in individual stocks when the classical model fails to identify the leverage effect.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/5e9894ea96c2e43ed6c2a34240ac9a6bfbe03523

Citations in the corpus

• Forecasting global stock market implied volatility indices (2018, unknown): “…on the use of models which are variants of GARCH models (see, inter alia, Bollerslev et al., 1994; Degiannakis, 2004; Hansen and Lunde, 2005), stochastic volatility models (see, among others, Deo, 2006; Yu, 2012) or realized volatility models (Andersen et al., 2003, Andersen et al., 2005).”