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This study proposes a class of realized non-linear stochastic volatility models with asymmetric effects and generalized Student’s t-error distributions by applying three families of power transformation—exponential, modulus, and Yeo-Johnson—to lagged log volatility. The proposed class encompasses a raw version of the realized stochastic volatility model. In the Markov chain Monte Carlo algorithm, an efficient Hamiltonian Monte Carlo (HMC) method is developed to update the latent log volatility and transformation parameter, whereas the other parameters that could not be sampled directly are updated by an efficient Riemann manifold HMC method. Empirical studies on daily returns and four realized volatility estimators of the Tokyo Stock Price Index (TOPIX) over 4-year and 8-year periods demonstrate statistical evidence supporting the incorporation of skew distribution into the error density in the returns and the use of power transformations of lagged log volatility.

Realized Non-linear Stochastic Volatility Models with Asymmetric Effects and Generalized Student&apos;s <i>t</i>-Distributions

Realized Non-linear Stochastic Volatility Models with Asymmetric Effects and Generalized Student's <i>t</i>-Distributions