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 …

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 …

the total volatility function in a continuous-time jump diffusion model …

alone. The second approach uses the data on stock prices as well as a certain volatility instrument, such as the CBOE … volatility index (VIX) or the Black-Scholes implied volatility. The theoretical justification for the instrument-based estimator …-only estimator is more robust since it is valid under weaker assumptions. However, in the presence of a valid volatility instrument …

parametric short-memory models, can be used to estimate the long-memory stochastic volatility model parameters in the presence of …-memory. -- stochastic volatility ; frequency domain estimation ; robust estimation ; spurious persistence ; long-memory ; level shifts …

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

Empirical volatility studies have discovered nonstationary, long-memory dynamics in the volatility of the stock market … found with nonparametric estimates of the fractional differencing parameter d, for financial volatility. In this paper, a …, stochastic volatility (SV-FIAR) model. Joint estimates of the autoregressive and fractional differencing parameters of volatility …

Empirical volatility studies have discovered nonstationary, long-memory dynamics in the volatility of the stock market … with nonparametric estimates of the fractional differencing parameter d, for financial volatility. In this paper, a fully … volatility (SV-FIAR) model. Joint estimates of the autoregressive and fractional differencing parameters of volatility are found …