Asset pricing with Lévy jump processes
Bibliographic record
Abstract
This thesis comprises of three essays that explore the theoretical development as well as the empirical applications of asset pricing models with Lévy jump processes. The first essay presents a new discrete-time framework that combines heteroskedastic processes with rich specifications of jumps in returns and volatility. Our models can be estimated with ease using standard maximum likelihood techniques. We evaluate the models by fitting a long sample of S&P500 index returns, and by valuing a large sample of options. We find strong empirical support for time-varying jump intensities. A model with jump intensity that is affine in the conditional variance performs particularly well both in return fitting and option valuation. In the second essay, we develop a new class of asset pricing model that combines the flexibility of Lévy processes with the ease of implementation of affine GARCH dynamics. This framework produces a large class of asset return processes that have analytical solutions for their pricing transform, and lead to a simple valuation of derivatives. We apply this newly proposed framework to various two-factor models consisting of a normal and a pure jump Lévy component. The results from joint estimation of options and returns on the market index reveal the important economic role of jumps. We find that models without jumps cannot reconcile the difference between market-realized returns and investors' ex-ante expectations of returns with an economically justifiable equity premium level. In the third essay, we provide evidence that the market crash risk is priced in individual equity options. We proceed by developing a factor model for equity returns and option pricing that takes into account the market's systematic risk factors, namely the market volatility and jump risks. The probability of large negative jumps in the market return produces the "crash fear" effect. In addition to the market risk factors, we
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".