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 … 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 …

tool for options portfolios using the "Maximum Loss" methodology based on Principal Components. -- Implied Volatility ; DAX …

volatility index was created to capture the investor expectations about the crypto-currency ecosystem. VCRIX is built based on … CRIX and offers a forecast for the mean annualized volatility of the next 30 days, re-estimated daily. The model was back …

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 …