with volatility …

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 …

Most affine models of the term structure with stochastic volatility (SV) predict that the variance of the short rate is …;unspanned stochastic volatility (USV).quot; Of the models tested, only the A1(4) USV model is found to generate both realistic volatility … estimates and a good cross-sectional fit. Our findings suggests that interest rate volatility cannot be extracted from the cross …

tackle several measurement issues assessing a plethora of state-of-the-art volatility forecasting models. We then examine the …

It is sometimes argued that an increase in stock market volatility raises required stock returns, and thus lowers stock … for this volatility feedback effect. The resulting model is asymmetric, because volatility feedback amplifies large … for large crashes. The model also implies that volatility feedback is more important when volatility is high. In U …

-shifts of heterogeneous durations affect the volatility of dividend news. We estimate tightly parameterized specifications with … likelihood than the classic Campbell and Hentschel (1992) specification, while generating volatility feedback effects 6 to 12 … times larger. We show in an extension that Bayesian learning about stochastic volatility is faster for bad states than good …

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 …