to overall realized variation and their contribution to predictive regressions of realized volatility. We find evidence …

The topic of volatility measurement and estimation is central to financial and more generally time series econometrics …. In this paper, we begin by surveying models of volatility, both discrete and continuous, and then we summarize some … developments in volatility models, with focus on time varying and stochastic volatility as well as nonparametric volatility …

volatility clustering in clock-time returns, even when trade- time returns are Gaussian. Finally, we highlight conditions on the … directing process which are required in order to generate proper volatility dynamics while simultaneously matching the … unconditional distribution of returns. In-sample fitting and out-of-sample realized volatility forecasting demonstrate the strength …

Many recent modelling advances in finance topics ranging from the pricing of volatility-based derivative products to … number of recent papers have addressed volatility predictability, some from the perspective of the usefulness of jumps in … forecasting volatility. Key papers in this area include Andersen, Bollerslev, Diebold and Labys (2003), Corsi (2004), Andersen …

This study explores the benefits of incorporating fat-tailed innovations, asymmetric volatility response, and an … extended information set into crude oil return modeling and forecasting. To this end, we utilize standard volatility models … Stochastic Volatility (SV), along with Mixed Data Sampling (MIDAS) regressions, which enable us to incorporate the impacts of …

parametric short-memory models, can be used to estimate the long-memory stochastic volatility model parameters in the presence of …