Empirical studies in a multivariate non-stationary, nonparametric regression model for financial returns - Marc Gürtler; Ronald Rauh (Technische Universität Braunschweig, Institut für Finanzwirtschaft, 2013)
In this paper we analyze a multivariate non-stationary regression model empirically. With the knowledge about unconditional heteroscedasticty of financial returns, based on univariate studies and a congruent paradigm in Gürtler and Rauh (2009), we test for a time-varying covariance structure firstly. Based on these results, a central component of our non-stationary model is a kernel regression for pairwise covariances and the covariance matrix. Residual terms are fitted with an asymmetric Pearson type VII distribution. In an extensive study we estimate the linear dependence of a broad portfolio of equities and fixed income securities (including credit and currency risks) and fit the whole approach to provide distributional forecasts. Our evaluations verify a reasonable approximation and a satisfactory forecasting quality with an out performance against a traditional risk model.
Keywords: C14; C5; 330; heteroscedasticity; non-stationarity; nonparametric regression; volatility; covariance matrix; innovation modeling; asymmetric heavy-tails; multivariate distributional forecast; empirical studies