forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumping …Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these … two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer of many …

these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the …Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these … two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer from …

Purpose: This paper examines the volatility of stock return in Dhaka stock exchange, BangladeshMethodology: Using … normality and the return series are volatility clustering. It is also obvious for study that GARCH family can be used to predict … volatility of stock return in Dhaka stock exchange (DSE) …

value robust volatility estimator with respect to the standard robust volatility estimator as proposed in the paper by … Muneer & Maheswaran (2018b). We show that the robust volatility ratio is unbiased both in the population as well as in finite … samples. We empirically test the robust volatility ratio on 9 global stock indices from America, Asia Pacific and EMEA markets …

-correction step to improve Value-at-Risk (VaR) forecasting ability of the n-EGARCH (normal EGARCH) model and correct the VaR for both …

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. …

. The method is applied to Value-at-Risk forecasting with (skewed) Student's t distributions and a time-varying degrees of … volatility of individual stock returns and exchange rate returns. …

negative news and volatility spillover effects, making it an attractive tool for multivariate volatility modeling. Despite … volatility modeling. The first example analyzes the daily returns of three stocks from the DJ30 index, while the second example … investigates volatility spillover effects among the bond, stock, crude oil, and gold markets. Overall, this extended multivariate …