Worldwide equity Risk Prediction
Various GARCH models are applied to daily returns of more than 1200 constituents of major stock indices worldwide. The value-at-risk forecast performance is investigated for different markets and industries, considering the test for correct conditional coverage using the false discovery rate (FDR) methodology. For most of the markets and industries we find the same two conclusions. First, an asymmetric GARCH specification is essential when forecasting the 95% value-at-risk. Second, for both the 95% and 99% value-at-risk it is crucial that the innovations’ distribution is fat-tailed (e.g., Student-t or – even better – a non-parametric kernel density estimate). Then we discuss two applications. First, we use normal Entropy Pooling to estimate a market distribution consistent with the CAPM equilibrium, which improves on the “implied returns” a-la-Black and Litterman (1990) and can be used as the starting point for portfolio construction. Second, we use normal Entropy Pooling to process ranking signals for alpha-generation.
|Date of creation:||2013|
|Date of revision:|
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