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 …

Measuring and modeling financial volatility is the key to derivative pricing, asset allocation and risk management … the daily or lower frequency volatility can be obtained by summing over squared high-frequency returns.In turn, this so …-called realized volatility can be used for more accurate model evaluation and description of the dynamic and distributional structure …

analyzed time series, which leads to a low accuracy of estimation in case of a changing stochastic structure. A time varying …. -- Adaptive Sequential Testing ; Independent Component Analysis ; Local Homogeneity ; Signal Processing ; Realized Volatility. …

As a function of strike and time to maturity the implied volatility estimation is a challenging task in financial … econometrics. Dynamic Semiparametric Factor Models (DSFM) are a model class that allows for the estimation of the implied … volatility surface (IVS) in a dynamic context, employing semiparametric factor functions and time-varying loadings. Because …