Spatial dependence in stock returns - Local normalization and VaR forecasts
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Date
2013-05-07
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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.