Jump and volatility dynamics for the s&p500: evidence for infinite-activity jumps with non-affine volatility dynamics from stock and option markets
Research output: Contribution to journal › Article › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Pages (from-to) | 811–844 |
| Journal | REVIEW OF FINANCE |
| Volume | 21 |
| Issue number | 2 |
| Early online date | 2016 |
| DOIs | |
| Publication status | Published - Mar 2017 |
| Publication type | A1 Journal article-refereed |
Abstract
Relatively little is known about the empirical performance of infinite-activity Levy jump models, especially with non-affine volatility dynamics. We use extensive empirical data sets to study how infinite-activity Variance Gamma and Normal Inverse Gaussian jumps with affine and non-affine volatility dynamics improve goodness of fit and option pricing performance. With Markov Chain Monte Carlo, different model specifications are estimated using the joint information of the S&P 500 index and the VIX. Our paper provides clear evidence that a parsimonious non-affine model with Normal Inverse Gaussian return jumps and a linear variance specification is particularly competitive, even during the recent crisis.