We propose a discrete-time econometric model that combines autoregressivefilters with factor regressions to predict stock returns for portfoliooptimisation purposes. In particular, we test both robust linear regressionsand general additive models on two different investment universes composed ofthe Dow Jones Industrial Average and the Standard & Poor's 500 indexes, and wecompare the out-of-sample performances of mean-CVaR optimal portfolios over ahorizon of six years. The results show a substantial improvement in portfolioperformances when the factor model is estimated with general additive models.
Enhancing CVaR portfolio optimisation performance with GAM factor models
Davide Lauria
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2023-01-01
Abstract
We propose a discrete-time econometric model that combines autoregressivefilters with factor regressions to predict stock returns for portfoliooptimisation purposes. In particular, we test both robust linear regressionsand general additive models on two different investment universes composed ofthe Dow Jones Industrial Average and the Standard & Poor's 500 indexes, and wecompare the out-of-sample performances of mean-CVaR optimal portfolios over ahorizon of six years. The results show a substantial improvement in portfolioperformances when the factor model is estimated with general additive models.File in questo prodotto:
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