This study predicts stock market volatility and applies them to the standard problem in finance, namely, asset … forecast market volatilities. Using various evaluation methods, we verify that those high-dimensional models have better … predictive performance relative to the standard volatility models. Furthermore, we construct volatility timing portfolios and …

improved ex-post volatility measurements but has also inspired research into their potential value as an informa-tion source … for longer horizon volatility forecasts. In this paper we explore the forecasting value of these high fre-quency series in … conjunction with a variety of volatility models for returns on the Standard & Poor's 100 stock index. We consider two so …

The contributions of error distributions have been ignored while modeling stock market volatility in Nigeria and … studies have shown that the application of appropriate error distribution in volatility model enhances efficiency of the model … asymmetric volatility models each in Normal, Student's-t and generalized error distributions with the view to selecting the best …

framework is a bivariate volatility model, where volatility spillovers of either positive or negative sign are allowed for. Our … countries. Regarding the volatility spillovers, such spillovers from bond returns to those of stocks are stronger than the other … results show that by considering time-varying return and volatility spillovers when calculating the risk-minimising portfolio …

to forecast financial markets volatility. The real data in this study uses British Pound-US Dollar (GBP) daily exchange … financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH … examined to the free parameters. Keywords: recurrent support vector regression ; GARCH model ; volatility forecasting …

to forecast financial markets volatility. The real data in this study uses British Pound-US Dollar (GBP) daily exchange … financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH …

In this paper, we use factor-augmented HAR-type models to predict the daily integrated volatility of asset returns. Our … approach is based on a proposed two-step dimension reduction procedure designed to extract latent common volatility factors …, we apply either LASSO or elastic net shrinkage on estimates of integrated volatility of all constituents in the dataset …

that accomodate long memory hold the promise of improved long-run volatility forecast as well as accurate pricing of long … 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 … 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 …