Spatial Dependence in Stock Returns - Local Normalization and VaR Forecasts
20 Pages Posted: 6 Sep 2013 Last revised: 31 Jan 2015
Date Written: January 30, 2015
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: Suggested Citation