virtually any stochastic volatility model model can be approximated arbitrarily well by a carefully chosen continuous time … illustrates these contributions of the paper, estimating a stochastic volatility jump diffusion model …

volatility's hidden state. Stochastic volatility is the unobserved state in a hidden Markov model (HMM), and can be tracked using … density on volatility. Our analysis relies on a specification of the martingale change of measure, which we will refer to as … separability. This specification has a multiplicative component that behaves like a risk premium on volatility-uncertainty in the …

Volatility long memory is a stylized fact that has been documented for a long time. Existing literature have two ways … to model volatility long memory: component volatility models and fractionally integrated volatility models. This paper … GARCH(1, 1) model by generating 37% less option pricing errors. With stronger volatility persistence, it also dominates a …

Brownian motion. -- asymptotic uniformity ; local limit theorem ; volatility …

In this paper we examine the empirical performance of affine jump diffusion models with stochastic volatility in a time … method. The support for a stochastic volatility model including jumps in both prices and volatility is strong and the model …

In this paper we propose a stochastic volatility model for crude oil markets that has the particularity to feature a … OVX volatility data. The model characterizes two states: a normal state with low volatility and negative variance premium … and acrisis state with high volatility and positive variance risk premium. The estimated states are consistent with GDP …

volatility models. We provide a succinct error analysis to demonstrate that we can achieve an exponential convergence rate in the …