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2011), “Modeling and Forecasting Multivariate Realized Volatility
| Content Provider | CiteSeerX |
|---|---|
| Author | Voev, Valeri Chiriac, Roxana |
| Abstract | This paper proposes a methodology for modelling time series of realized covariance matrices in order to forecast multivariate risks. The approach allows for flexible dynamic dependence patterns and guarantees positive definiteness of the resulting forecasts without imposing parameter restrictions. We provide an empirical application of the model, in which we show by means of stochastic dominance tests that the returns from an optimal portfolio based on the model’s forecasts second-order dominate returns of portfolios optimized on the basis of traditional MGARCH models. This result implies that any risk-averse investor, regardless of the type of utility function, would be better-off using our model. |
| File Format | |
| Journal | Journal of Applied Econometrics |
| Access Restriction | Open |
| Subject Keyword | Parameter Restriction Multivariate Realized Volatility Utility Function Flexible Dynamic Dependence Pattern Empirical Application Multivariate Risk Traditional Mgarch Model Resulting Forecast Model Forecast Second-order Dominate Return Stochastic Dominance Test Guarantee Positive Definiteness Realized Covariance Matrix Optimal Portfolio Risk-averse Investor |
| Content Type | Text |
| Resource Type | Article |
