The paper examines the relative performance of Stochastic Volatility (SV) and GARCH(1,1) models fitted to twenty plus … years of daily data for three indices. As a benchmark, I use the realized volatility (RV) for the S&P 500, DOW JONES and … volatility models, if the simple expedient of using lagged squared demeaned daily returns provides a better RV predictor, at …

daily data for FTSE. As a benchmark, we use the realized volatility (RV) of FTSE sampled at 5-minute intervals, taken from …The paper examines the relative performance of Stochastic Volatility (SV) and GARCH(1,1) models fitted to ten years of … demeaned daily returns on FTSE, appears to predict the daily RV of FTSE better than either of the two models. Quantile …

This paper develops a method to improve the estimation of jump variation using high frequency data with the existence … of market microstructure noises. Accurate estimation of jump variation is in high demand, as it is an important component … of volatility in finance for portfolio allocation, derivative pricing and risk management. The method has a two …

This paper considers spot variance path estimation from datasets of intraday high frequency asset prices in the … microstructure noise has an adverse effect on both spot variance estimation and jump detection. In our approach we can analyze high …

forecasting the volatility of equity prices, using high-frequency data from 2000 to 2016. We consider the SPY and 20 stocks that …, 60 and 300 seconds), forecast horizons (1, 5, 22 and 66 days) and the use of standard and robust-to-noise volatility and …-time forecasts than the HAR-RV model, although no single extended model dominates. In general, standard volatility measures at the …

In this paper, we review the most common specifications of discrete-time stochastic volatility (SV) models and … indices and foreign exchange rates. -- Stochastic volatility ; Markov chain Monte Carlo ; Metropolis-Hastings algorithm Jump …