Empirical studies in a multivariate non-stationary, nonparametric regression model for financial returns

Marc Gürtler and Ronald Rauh

No IF43V1, Working Papers from Technische Universität Braunschweig, Institute of Finance

Abstract: 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: heteroscedasticity; non-stationarity; nonparametric regression; volatility; covariance matrix; innovation modeling; asymmetric heavy-tails; multivariate distributional forecast; empirical studies (search for similar items in EconPapers)
JEL-codes: C14 C5 (search for similar items in EconPapers)
Date: 2013
New Economics Papers: this item is included in nep-ecm, nep-fmk and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:tbsifw:if43v1

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