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 …

-varying parameter models that incorporate both stochastic volatility and a Heckman-type two-step estimation procedure that deals with …

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 …

The paper examines the relative performance of Stochastic Volatility (SV) and Generalised Autoregressive Conditional … Heteroscedasticity (GARCH) (1,1) models fitted to ten years of daily data for FTSE. As a benchmark, we used the realized volatility (RV … two standard volatility models if the simple expedient of using lagged squared demeaned daily returns provides a better RV …

Particle Filter algorithms for filtering latent states (volatility and jumps) of Stochastic-Volatility Jump …

We are comparing two approaches for stochastic volatility and jumps estimation in the EUR/USD time series - the non …) using daily returns. We find that both of the methods do identify continuous stochastic volatility similarly, but they do … the continuous volatility) on the daily frequency. As an additional result we find strong evidence for jump size …

evidence for stochastic intensity and stochastic volatility models based on Ornstein-Uhlenbeck processes. For our empirical …

-memory stochastic volatility model. We develop a new Bayesian estimator based on the Markov chain Monte Carlo sampler and the wavelet … joint posterior distribution. Unlike short-memory stochastic volatility models, long-memory stochastic volatility models do … quickly and efficiently from the near independent multivariate distribution of the long-memory volatility's wavelet …

Time-varying volatility is common in macroeconomic data and has been incorporated into macroeconomic models in recent … countries or regions. This paper estimates dynamic panel data models with stochastic volatility by maximizing an approximate … particle filter-based estimator. When the volatility of volatility is high, or when regressors are absent but stochastic …

accounts for time variation in macroeconomic volatility, known as the great moderation. In particular, we consider an … volatility processes and mixture distributions for the irregular components and the common cycle disturbances enable us to … that time-varying volatility is only present in the a selection of idiosyncratic components while the coefficients driving …