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 language | English |
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Pages (from-to) | 1-29 |
Number of pages | 29 |
Journal | Journal of Economic Dynamics & Control |
Volume | 90 |
DOIs | |
Publication status | Published - 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