measures, resulting from the new model, can be used to implemennt joint risk scenario analysis. …

incorporates stochastic volatility, long-run risks in consumption and dividends, and Epstein-Zin preferences. Utilizing Bayesian …-term real risk-free interest rate, real consumption growth, and real dividend growth. Our results indicate that, over short and …

We investigate high-frequency volatility models for analyzing intra-day tick by tick stock price changes using Bayesian … estimation procedures. Our key interest is the extraction of intra-day volatility patterns from high-frequency integer price … distributions. We allow for stochastic volatility by modeling the variance as a stochastic function of time, with intra-day periodic …

This work deals with multivariate stochastic volatility models, which account for a time-varying variance … the volatility level. We apply a full Bayesian inference approach, which relies upon Sequential Monte Carlo (SMC) for …

-time version of the stochastic volatility (SV) model. The Bayesian approach represents a feasible way to estimate SV models. Under …

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 …

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

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 …