Authors: Guhr, Thomas
Schäfer, Rudi
Schmitt, Thilo A.
Wied, Dominik
Title: Spatial dependence in stock returns - Local normalization and VaR forecasts
Language (ISO): en
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.
Issue Date: 2013-05-07
Appears in Collections:Sonderforschungsbereich (SFB) 823

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