characterized by volatility clustering and asymmetry. Also revealed as a stylized fact is Long memory or long range dependence in … market volatility, with significant impact on pricing and forecasting of market volatility. The implication is that models … that accomodate long memory hold the promise of improved long-run volatility forecast as well as accurate pricing of long …

We propose a methodology to include night volatility estimates in the day volatility modeling problem with high … the natural relationship between the realized measure and the conditional variance. This improves volatility modeling by … leverage effect and maintains a mathematical structure that facilitates volatility estimation. A class of bivariate models that …

volatility …

frequency volatilities and correlations ; Dynamic conditional correlation ; Spline-GARCH ; Idiosyncratic volatility ; Long …

theory assumes that return shocks can be caused by changes in conditional volatility through a time-varying risk premium. On … leverage effect and the volatility feedback effect. We stress the importance of distinguishing between realized volatility and … implied volatility, and find that implied volatilities are essential for assessing the volatility feedback effect. The …

In this paper, an attempt has been made to model the volatility of NIFTY index of National Stock Exchange (NSE) and … comprises 3736 data points for the analysis by using Box-Jenkins or ARIMA model. The volatility in the Indian stock market … suggested by Hannan-Rissanen. As per the analysis, ARIMA (1,0,1) model was found to be the best fit to forecast the volatility …

Several novel large volatility matrix estimation methods have been developed based on the high-frequency financial data …. They often employ the approximate factor model that leads to a low-rank plus sparse structure for the integrated volatility … matrix and facilitates estimation of large volatility matrices. However, for predicting future volatility matrices, these …