market index. The tail loss measure is motivated by the results of the extreme value theory, and it is computed from observed …

Forecasts of stock market volatility is an important input for market participants in measuring and managing investment … Machine Learning methods, and specifically Artificial Neural Network (ANN) models to forecast volatility. The ANN models are …

In this paper, an attempt has been made to model and forecast the short term volatility of the Indian banking sector. A … 2000; a total of 3122 observations up to the period of June 2013, are used in modeling the volatility of the banking stock …-Rissanen. As per the analysis, ARIMA (1,0,2) model was found to be the best fit to forecast the volatility of bank stock returns …

bad) stock market volatility, we show that incorporating the information in lagged industry returns can help improve out …-of sample forecasts of aggregate stock market volatility. While the predictive contribution of industry level returns is not … crisis, highlighting the informational value of real economic activity on stock market volatility dynamics. Finally, we show …

frequency can be obtained almost as precisely as if volatility is observable by simply incorporating the strong information … content of realized volatility measures extracted from high-frequency data. For this purpose, we introduce asymptotically … exact volatility measurement equations in state space form and propose a Bayesian estimation approach. Our highly efficient …

model in several ways, it allows for all the primary stylized facts of financial asset returns, including volatility … volatility, but without the estimation problems associated with the latter, and being applicable in the multivariate setting for …

This paper examines the impact of intraday periodicity on forecasting realized volatility using a heterogeneous … autoregressive model (HAR) framework. We show that periodicity inflates the variance of the realized volatility and biases jump …