Smooth Transition Realized Stochastic Volatility Model

Abstract

Realized Volatility (RV), which is computed as a squared sum of intraday returns, is a precise estimator of latent voaltility but is biased due to market microstructure effects. Takahashi et al. (2009) proposed a realized stochastic volatility (RSV), which models daily returns and RV simultaneously and adjusts the bias in RV\null. The RSV model assumes a constant mean of volatility despite we observe low and high volatilities in a boom-and-bust cycle. This article proposes a smooth transition RSV (STRSV), which models a time-varying mean of volatility by a smooth transition function, and shows a Bayesian estimation method via Markov chain Monte Carlo. Empirical analysis with the data of Japanese and the U.S. stock indices shows that the STRSV model captures the volatility dynamics appropriately and provides better fit to the data compared to the standard RSV model.