Model Complexity and Out-of-Sample Performance: Evidence from S&P 500 Index Returns
Paulo Rodrigues and
Norman J. Seeger
Journal of Economic Dynamics and Control, 2018, vol. 90, issue C, 1-29
We apply a range of out-of-sample specification tests to more than forty competing stochastic volatility models to address how model complexity affects out-of-sample performance. Using daily S&P 500 index returns, model confidence set estimations provide strong evidence that the most important model feature is the non-affinity of the variance process. Despite testing alternative specifications during the turbulent market regime of the global financial crisis of 2008, we find no evidence that either finite- or infinite-activity jump models or other previously proposed model extensions improve the out-of-sample performance further. Applications to Value-at-Risk demonstrate the economic significance of our results. Furthermore, the out-of-sample results suggest that standard jump diffusion models are misspecified.
Keywords: Out-of-sample specification tests; Jump-diffusion models; Lévy-jump models; Non-affine variance models; Forecasting (search for similar items in EconPapers)
JEL-codes: G12 G15 C53 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:90:y:2018:i:c:p:1-29
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