on high-frequency stock trading volumes and realized volatility forecasts demonstrate the usefulness of the proposed …

We propose exible models for multivariate realized volatility dynamics which involve generalizations of the Box …

We study nonparametric estimation of the volatility function of a diffusion process from discrete data, when the data … constraints of Besov type. Since the underlying signal (the volatility) is genuinely random, we propose a new criterion to assess … volatility …

's dynamic properties may lead to misestimation of the intraday spot volatility …

of volatility in finance for portfolio allocation, derivative pricing and risk management. The method has a two … average realized volatility processes can achieve a convergence rate close to OP(n−4/9) , which is better than the convergence … based on average realized volatility processes indeed performs better than that based on the price processes. Empirically …

the Efficient Method of Moments implemented to estimatestochastic volatility models this will surely be the case … method of momentstechnique for a broad range of univariate stochastic volatility models. As a side effect of the … volatility models. It describes the program. Some examples are given from other workof the author. Technicalities are given in …

We investigate a model in which we connect slowly time varying unconditional long-run volatility with short …-run conditional volatility whose representation is given as a semi-strong GARCH (1,1) process with heavy tailed errors. We focus on … robust estimation of both long-run and short-run volatilities. Our estimation is semiparametric since the long-run volatility …

We investigate a model in which we connect slowly time varying unconditional long-run volatility with short …-run conditional volatility whose representation is given as a semi-strong GARCH (1,1) process with heavy tailed errors. We focus on … robust estimation of both long-run and short-run volatilities. Our estimation is semiparametric since the long-run volatility …

We investigate a model in which we connect slowly time varying unconditional long-run volatility with short …-run conditional volatility whose representation is given as a semi-strong GARCH (1,1) process with heavy tailed errors. We focus on … robust estimation of both long-run and short-run volatilities. Our estimation is semiparametric since the long-run volatility …