We propose a new method for pricing options based on GARCH models with filtered historical innovations. In an incomplete market framework we allow for different distributions of the historical and the pricing return dynamics enhancing the model flexibility to fit market option prices. An...

We propose a simple class of semiparametric multivariate GARCH models, allowing for asymmetric volatilities and time-varying conditional correlations. Estimates for time-varying conditional correlations are constructed by means of a convex combination of estimates for averaged correlations...

The first four conditional moments of the integrated variance implied by the GARCH diffusion process are derived analytically. Based on these moments and on a power series method an analytical approximation formula to price European options under the GARCH diffusion model is obtained. Monte...

This empirical study analyzes market and currency risk premia during financial and political crises within the theoretical framework of the international asset pricing model of Adler and Dumas (1983). The econometric specification extends the multivariate GARCH approach of De Santis and Gerard...

We propose a multivariate nonparametric technique for generating reliable scenarios and confidence intervals for the term structure of interest rates from historical data. The approach is based on a functional gradient descent (FGD) estimation of the conditional mean vector and the conditional...

Empirically, we show that the proportion of stocks exhibiting conditional heteroscedastic residuals, is high. We suggested to use the market model with GARCH(1,1) residuals in order to describe daily stock returns and derived a test statistics for the null hypothesis of no abnormal returns,...

We present a multivariate, non-parametric technique for constructing reliable daily VaR predictions for individual assets belonging to a common equity market segment, which takes also into account the possible dependence structure between the assets and is still computationally feasible in large...