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 …

GARCH systems to model the volatility of the FTSE 100 Implied Volatility Index (IV). We use GARCH, EGARCH, GJR-GARCH and …Modelling the volatility (or kurtosis) of the implied volatility is an important aspect of financial markets when … GARCH-MIDAS to model variance. We also introduce FTSE 100 returns and several macroeconomic variables (UK industrial …

market uncertainty and volatility of the investment instruments. Thus, the prediction of the uncertainty and volatilities of … to identify the best fit model that can predict the volatility of return of Bitcoin, which is in high demand as an … the residuals of the average equation model selected have ARCH effect. Volatility of Bitcoin return series after detection …

daily realized volatility data of Standard & Poor's 500 (S&P 500) and several other indices, we obtained good performance … heterogeneous autoregressive and other models of realized volatility. …

findings support the arguments of risk return tradeoff, volatility feedback and statistical balance. It is reasoned that the …

findings support the arguments of risk return tradeoff, volatility feedback and statistical balance. It is reasoned that the …

improved ex-post volatility measurements but has also inspired research into their potential value as an informa-tion source … for longer horizon volatility forecasts. In this paper we explore the forecasting value of these high fre-quency series in … conjunction with a variety of volatility models for returns on the Standard & Poor's 100 stock index. We consider two so …

in return volatility of Bollerslev and Mikkelsen (1996) by introducing a possible volatility-in-mean effect. To avoid … that the long memory property of volatility carries over to returns, we consider a filtered FIEGARCH-in-mean (FIEGARCH …-M) effect in the return equation. The filtering of the volatility-in-mean component thus allows the co-existence of long memory …