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

Market expectations of future return volatility play a crucial role in finance; so too does our understanding of the … informativeness of volatility forecasts produced by ARCH models versus the volatility forecasts derived from option prices and in … improving volatility forecasts produced by ARCH and option models and combinations of models. Daily and monthly data are …

an approach to model spot prices that combines mean-reversion, spikes and stochastic volatility. Thereby we use different …

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 …

one-factor and stochastic volatility models. Then, we construct accuracy assessment tests that are in the spirit of …

aggregation of GARCH processes of Drost and Nijman (1993). Using Swedish data, our estimation method produces an overall larger …

This paper applies the Model Confidence Set (MCS) procedure of Hansen, Lunde, and Nason (2003) to a set of volatility …. The empirical exercise is based on 55 volatility models and the MCS includes about a third of these when evaluated by mean …

The long memory characteristic of financial market volatility is well documentedand has important implications for … volatility forecasting and optionpricing. When fitted to the same data, different volatility models calculate theunconditional … variance differently and could have very different volatility persistentparameters. Hence, they produce very different …