realized correlations in the DCC-HEAVY model. The new model removes well known asymptotic bias in DCC-GARCH model estimation … and has more desirable asymptotic properties. We also derive a Quasi-maximum likelihood estimation and provide closed …

We investigate the effect of estimation error on backtests of (multi-period) expected shortfall (ES) forecasts. These … estimation error, and propose robust tests that account for it. Monte Carlo experiments show that the tests that ignore these …, we find that estimation error substantially impacts the outcome of the backtests. …

recently introduced ERP forecasting models, which have been shown to generate econometrically superior ERP forecasts, and find … that their forecasting ability is regime-dependent. They give rise to significant relative losses during market downturns …

forecasting technique with respect to various volatility estimators. The methodology of volatility estimation includes Close … variations in returns. Forecasting volatility had been a stimulating problem in the financial systems. The study examined the …, Garman-Klass, Parkinson, Roger-Satchell and Yang-Zhang methods and forecasting is done through ARIMA technique. The study …

forecasting technique with respect to various volatility estimators. The methodology of volatility estimation included Close … variations in returns. Forecasting volatility has been a stimulating problem in the financial systems. This study examined the …, Garman-Klass, Parkinson, Roger-Satchell, and Yang-Zhang methods and forecasting was done through the ARIMA technique. The …

Volatility forecasting is crucial for portfolio management, risk management, and pricing of derivative securities …

There is evidence that volatility forecasting models that use intraday data provide better forecast accuracy as … fills this gap in the literature and extends previous studies on forecasting stock market volatility in several important …