Tse (1998) proposes a model which combines the fractionally integrated GARCH formulation of Baillie, Bollerslev and …

This paper investigates the forecasting performance of three popular variants of the non-linear GARCH models, namely VS-GARCH …, GJR-GARCH and Q-GARCH, with the symmetric GARCH(1,1) model as a benchmark. The application involves ten European stock … price indexes. Forecasts produced by each non-linear GARCH model and each index are evaluated using a common set of …

We use an information-theoretic approach to interpret Engle's (1982) and Bollerslev's (1986) GARCH model as a model for … may be generalized, if we use alternative measures of volatility. We choose one feasible alternative and derive a … generalized volatility model. Applying this model to some exemplary market indices, we are able to give some empirical evidence …

Generalized autoregressive conditional heteroskedasticity (GARCH) processes have become very popular as models for … financial return data because they are able to capture volatility clustering as well as leptokurtic unconditional distributions … densities and their parametric competitors within different generalized GARCH models such as APARCH and GJR-GARCH. …

In this paper we present an exact maximum likelihood treatment forthe estimation of a Stochastic Volatility in Mean …(SVM) model based on Monte Carlo simulation methods. The SVM modelincorporates the unobserved volatility as anexplanatory variable …) models, known as the ARCH in Mean (ARCH-M)model. The estimation of ARCH models isrelatively easy compared with that of the …

ARCH modelling framework of Engle (1982) and its GARCH generalization of Bollerslev (1986) gave a huge impetus to … describe the most typical features of capital markets like volatility clustering, excess kurtosis and fat tails. As empirical … evidence shows asymmetry is also a prominent feature of stock market returns volatility. The reaction of risk if stock returns …

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 … 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 …

provide some evidence that GARCH-t models provide good density forecasts. The results further suggest that extensions of … praktischen Fällen keine Macht. Die empirischen Ergebnisse deuten darauf hin, dass GARCH-t-Modelle gute Dichte-Prognosen liefern …