With the recent availability of high-frequency Financial data the long range dependence of volatility regained … researchers' interest and has lead to the consideration of long memory models for realized volatility. The long range diagnosis of … volatility, however, is usually stated for long sample periods, while for small sample sizes, such as e.g. one year, the …

Stochastic Volatility (SV) models are widely used in financial applications. To decide whether standard parametric …

different lexica sentiment variables. These are employed for an analysis of stock reactions: volatility, volume and returns. An … increased (negative) sentiment will influence volatility as well as volume. This influence is contingent on the lexical … produce stock reaction indicators, including volatility, detrended log trading volume and return? (ii) To which degree is …

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 …

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