In this paper, we propose a Markov Chain Quasi-Monte Carlo (MCQMC) approach for Bayesian estimation of a discrete …-time version of the stochastic volatility (SV) model. The Bayesian approach represents a feasible way to estimate SV models. Under … the conventional Bayesian estimation method for SV models, pseudo-random numbers are usually used. Here we develop an …

In this paper, we review the most common specifications of discrete-time stochastic volatility (SV) models and … indices and foreign exchange rates. -- Stochastic volatility ; Markov chain Monte Carlo ; Metropolis-Hastings algorithm Jump …

evidence for stochastic intensity and stochastic volatility models based on Ornstein-Uhlenbeck processes. For our empirical …

simulation evidence for stochastic volatility and stochastic intensity models. For our empirical study, we analyse the …

Intraday high-frequency data of stock returns exhibit not only typical characteristics (e.g., volatility clustering and … the leverage effect) but also a cyclical pattern of return volatility that is known as intraday seasonality. In this paper …, we extend the stochastic volatility (SV) model for application with such intraday high-frequency data and develop an …

Empirical volatility studies have discovered nonstationary, long-memory dynamics in the volatility of the stock market … found with nonparametric estimates of the fractional differencing parameter d, for financial volatility. In this paper, a …, stochastic volatility (SV-FIAR) model. Joint estimates of the autoregressive and fractional differencing parameters of volatility …

This paper develops a method to improve the estimation of jump variation using high frequency data with the existence … of market microstructure noises. Accurate estimation of jump variation is in high demand, as it is an important component … of volatility in finance for portfolio allocation, derivative pricing and risk management. The method has a two …