We propose a simple class of multivariate GARCH models, allowing for time-varying conditional correlations. Estimates for time-varying conditional correlations are constructed by means of a convex combination of averaged correlations (across all series) and dynamic realized (historical) correlations. Our model is very parsimonious. Estimation is computationally feasible in very large dimensions...

In this paper we introduce a multivariate generalized autoregressive conditional heteroskedastic (GARCH) class of models with time-varying eigenvalues. The dynamics the eigenvalues is derived for cases underlying Gaussian and Student’s t-distributed innovations based on general theory dynamic score by Creal, Koopman Lucas (2013) Harvey (2013). resulting eigenvalue GARCH – labeled ‘?-GARCH’ diff...

In this paper we combine the appealing properties of the stable Paretian distribution to model the heavy tails and the GARCH model to capture the phenomenon of the volatility clustering. We assume the asset-returns to have a particular multivariate stable distribution, i.e., to be sub-Gaussian random vectors. In this way the characteristic function has a tractable expression and the density fun...

The autoregressive conditional heteroskedasticity (ARCH) and generalized autoregressive conditional heteroskedasticity (GARCH) models take the dependency of the conditional second moments. The idea behind ARCH/GARCH model is quite intuitive. For ARCH models, past squared innovations describes the present squared volatility. For GARCH models, both squared innovations and the past squared volatil...

Modeling the dependency between stock market returns is a difficult task when returns follow a complicated dynamics. It is not easy to specify the multivariate distribution relating two or more return series. In this paper, a methodology based on fitting ARIMA, GARCH and ARMA-GARCH models and copula functions is applied. In such methodology, the dependency parameter can easily be rendered condi...

We simulate daily trading of straddles on financial indexes. The straddles are traded based on predictions of daily volatility differences in the indexes. The main predictive models studied are recurrent neural nets (RNN). Such applications have often been studied in isolation. However, due to the special character of daily financial time-series, it is difficult to make full use of RNN represen...