augments the prediction problem by covariate forecasting models. In this paper, we present simple alternatives for multi …

produce statistically and regulatory precise VaR forecasts across forecasting horizons, with the implied volatility being … especially accurate in monthly VaR forecasts. The daily range produces inferior forecasting results in terms of regulatory …-step VaR forecasting context as they balance between statistical or regulatory accuracy and capital efficiency …

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 …

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 …

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 …