Model Complexity and Out-of-Sample Performance: Evidence from S&P 500 Index Returns

Andreas Kaeck, Paulo Rodrigues, Norman J. Seeger*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Web of Science)

Abstract

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.
Original languageEnglish
Pages (from-to)1-29
Number of pages29
JournalJournal of Economic Dynamics & Control
Volume90
DOIs
Publication statusPublished - May 2018

Keywords

  • Out-of-sample specification tests
  • Jump-diffusion models
  • Levy-jump models
  • Non-affine variance models
  • Forecasting
  • STOCHASTIC VOLATILITY MODELS
  • JUMP-DIFFUSION-MODELS
  • DENSITY FORECASTS
  • REALIZED VOLATILITY
  • RISK PREMIA
  • OPTION PRICES
  • DYNAMICS
  • AFFINE
  • EQUITY
  • SIMULATION

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