Value-at-Risk (VaR) analysis. We propose a semi-parametric method for VaRevaluation. The largest risks are modelled …

dynamics adapts to the non-normal nature of financial data, which helps to robustify the volatility estimates. The new model … dynamics of higher-order moments, and to the other preferred choice of forecasting distribution. We apply our method to Value-at-Risk … volatility forecasting of stock returns and exchange rates. …

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 …

source of international tourism. In order to understand the risk persistence of Chinese tourists, the paper investigates the … volatility models, namely GARCH(1,1), GJR(1,1) and EGARCH(1,1), are used to measure the short-run and long-run persistence of …. The mean equations associated with GARCH(1,1), GJR(1,1) and EGARCH(1,1) are used to analyse the risk persistence of the …

univariate volatility models (including HEAVY and Realized GARCH models), using daily returns from the S&P 500, DJIA, FTSE and …

relationship between the S&P500 Composite Index and the Volatility Index (VIX), but few empirical studies have focused on the … affect ETF returns. The ARCH-LM test shows conditional heteroskedasticity in the estimation of ETF returns, so that the …

In this paper we compare the predictive abilility of Stochastic Volatility (SV)models to that of volatility forecasts … implied by option prices. We develop anSV model with implied volatility as an exogeneous var able in the varianceequation … stochastic shocks incorporated in the SVXmodels. The out-of-sample volatility forecasts are evaluated against dailysquared …