We extend the asymmetric, stochastic, volatility model by modeling the return-volatility distribution nonparametrically … markets, expected volatility rises (declines, then rises as the shock increases) when the market shock is negative (positive … bad times, neither good nor bad news matters with regards to volatility. …

In this paper we extend the parametric, asymmetric, stochastic volatility model (ASV), where returns are correlated … with volatility, by flexibly modeling the bivariate distribution of the return and volatility innovations nonparametrically …. Its novelty is in modeling the joint, conditional, return-volatility, distribution with a infinite mixture of bivariate …

In this paper we extend the parametric, asymmetric, stochastic volatility model (ASV), where returns are correlated … with volatility, by flexibly modeling the bivariate distribution of the return and volatility innovations nonparametrically …. Its novelty is in modeling the joint, conditional, return-volatility, distribution with a infinite mixture of bivariate …

This paper designs a Particle Learning (PL) algorithm for estimation of Bayesian nonparametric Stochastic Volatility …

Discrete-time stochastic volatility (SV) models have generated a considerable literature in financial econometrics …

The empirical relationship between the return of an asset and the volatility of the asset has been well documented in …, when the return of the asset decreases, the volatility increases and vice versa. …

. In this framework stochastic volatility with properties that are similar to those actually observed arises naturally. The … volatility. It also incorporates possible default of an asset and thus models credit risk. …

index and its volatility. …