Abstract
We conduct out-of-sample density forecast evaluations of the affine jump diffusion models for the S&P 500 stock index and its options' contracts. We also examine the time-series consistency between the model-implied spot volatilities using options & returns and only returns. In particular, we focus on the role of the time-varying jump risk premia. Particle filters are used to estimate the model-implied spot volatilities. We also propose the beta transformation approach for recursive parameter updating. Our empirical analysis shows that the inconsistencies between options & returns and only returns are resolved by the introduction of the time-varying jump risk premia. For density forecasts, the time-varying jump risk premia models dominate the other models in terms of likelihood criteria. We also find that for medium-term horizons, the beta transformation can weaken the systematic effect of misspecified AJD models using options & returns.
Original language | English |
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Pages (from-to) | 74-87 |
Number of pages | 14 |
Journal | Journal of Banking and Finance |
Volume | 47 |
Issue number | 1 |
DOIs | |
State | Published - Oct 2014 |
Keywords
- Affine jump diffusion
- Beta transformation
- Density forecasts
- Particle filters
- Time-series consistency
- Time-varying jump risk premia