Guhr, ThomasSchäfer, RudiSchmitt, Thilo A.Wied, Dominik2013-05-072013-05-072013-05-07http://hdl.handle.net/2003/3030710.17877/DE290R-5391We 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.enDiscussion Paper / SFB 823;18/2013310330620working paper