Spatial Dependence in Stock Returns - Local Normalization and VaR Forecasts

20 Pages Posted: 6 Sep 2013 Last revised: 31 Jan 2015

See all articles by Thilo Schmitt

Thilo Schmitt

University of Duisburg-Essen

Rudi Schäfer

University of Duisburg-Essen

Dominik Wied

University of Cologne

Thomas Guhr

University of Duisburg-Essen

Date Written: January 30, 2015

Abstract

We analyze a recently proposed spatial autoregressive model for stock returns and compare it to a one-factor model and the sample covariance matrix. The influence of refinements to these covariance estimation methods is studied. We employ power mapping as a noise reduction technique for the correlations. Further, we address the empirically observed non-stationary behavior of stock returns. Local normalization strips the time series of changing trends and fluctuating volatilities. As an alternative method, we consider a GARCH fit. In the context of portfolio optimization, we find that the spatial model has the best match between the estimated and realized risk measures.

JEL Classification: G17, C33

Suggested Citation

Schmitt, Thilo and Schäfer, Rudi and Wied, Dominik and Guhr, Thomas, Spatial Dependence in Stock Returns - Local Normalization and VaR Forecasts (January 30, 2015). Forthcoming in Empirical Economics. Available at SSRN: https://ssrn.com/abstract=2320675 or http://dx.doi.org/10.2139/ssrn.2320675

Thilo Schmitt (Contact Author)

University of Duisburg-Essen ( email )

Lotharstrasse 1
Duisburg, 47048
Germany

Rudi Schäfer

University of Duisburg-Essen ( email )

Lotharstrasse 1
Duisburg, 47048
Germany

Dominik Wied

University of Cologne ( email )

Albertus-Magnus-Platz
Cologne, 50923
Germany

Thomas Guhr

University of Duisburg-Essen ( email )

Lotharstrasse 1
Duisburg, 47048
Germany

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