Volatility permeates modern financial theories and decision making processes. As such, accurate measures and good … forecasts of future volatility are critical for the implementation and evaluation of asset pricing theories. In response to this …, a voluminous literature has emerged for modeling the temporal dependencies in financial market volatility at the daily …

reservations about the impact of foreign speculators on both expectedquot; returns and market volatility. We propose a cross … depositary receipts country funds and other financial instruments, in an extranational market and market volatility in emerging … market returns and the world market return. quot; Our empirical approach is designed to control for other economic events …

We propose a novel method to estimate dynamic equilibrium models with stochastic volatility. First, we characterize the … exploits the profusion of shocks in stochastic volatility models, is versatile and computationally tractable even in large … methods to estimate a business cycle model of the U.S. economy with both stochastic volatility and parameter drifting in …

This paper analyzes optimal portfolio choice and consumption with stochastic volatility in incomplete markets. Using … for stocks under stochastic volatility varies strongly with the investor's coefficient of relative risk aversion, but only … preference parameters. This paper also shows that stochastic variation in volatility produces an optimal intertemporal hedging …

We develop and implement a new method for maximum likelihood estimation in closed-form of stochastic volatility models … unobservable volatility state, to an approximate likelihood procedure where the volatility state is replaced by the implied … volatility of a short dated at-the-money option. We find that the approximation results in a negligible loss of accuracy. We …

We develop new procedures for maximum likelihood estimation of affine term structure models with spanned or unspanned … stochastic volatility. Our approach uses linear regression to reduce the dimension of the numerical optimization problem yet it … produces the same estimator as maximizing the likelihood. It improves the numerical behavior of estimation by eliminating …