run volatility function. Our estimation is based on a two-step LAD procedure. We establish the relevant asymptotic theory …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 …

run volatility function. Our estimation is based on a two-step LAD procedure. We establish the relevant asymptotic theory …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 …

run volatility function. Our estimation is based on a two-step LAD procedure. We establish the relevant asymptotic theory …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 …

This paper presents a method for Bayesian nonparametric analysis of the return distribution in a stochastic volatility … series and two stock index return series. We find that estimates of volatility using the model can differ dramatically from …

A Bayesian semiparametric stochastic volatility model for financial data is developed. This estimates the return … between the returns and changes in volatility, the leverage effect. An efficient MCMC algorithm for inference is described …

We propose a new semiparametric observation-driven volatility model where the form of the error density directly … influences the volatility dynamics. This feature distinguishes our model from standard semiparametric GARCH models. The link … between the estimated error density and the volatility dynamics follows from the application of the generalized autoregressive …

In recent years, numerous volatility-based derivative products have been engineered. This has led to interest in … constructing conditional predictive densities and confidence intervals for integrated volatility. In this paper, we propose … realized volatility measures, which are constructed using the ex post variation of asset prices. A set of sufficient conditions …

parametric approach utilizing a Stochastic-Volatility-Jump-Diffusion (SVJD) model, estimated with MCMC and extended with Particle … method may be biased in the case when large outlier jumps occur in the time series as well as when the stochastic volatility …

A kernel weighted version of the standard realised integrated volatility estimator is proposed. By different choices of … the kernel and bandwidth, the measure allows us to focus on specific characteristics of the volatility process. In … particular, as the bandwidth vanishes, an estimator of the realised spot volatility is obtained. We denote this the filtered spot …