Université de Montréal and CREST
Bibliographic record
Abstract
Recent developments in asset return modeling have shown evidence for time-variation not only in conditional variance, but also especially in conditional skewness and leverage effects. We develop a discrete time affine multifactor latent variable model of asset returns which allows for both stochastic volatility and stochastic skewness (SVS model). Importantly, we disentangle the dynamics of conditional volatility and conditional skewness in a coherent way. Our approach allows the distribution of current daily returns conditional on current volatility to be asymmetric. In our model, time-varying conditional skewness is driven by the conditional leverage effect and the asymmetry of the distribution of current returns conditional on current volatility. We derive analytical formulas for various moment conditions that we use for GMM inference. Applying our approach to several equity and index daily returns, we show that the conditional distribution of current daily returns, conditional on current volatility, is positively skewed and helps to match sample return skewness as well as negative cross-correlations between returns and squared returns. The conditional leverage effect is significant and negative. The conditional skewness is positive, implying that the asymmetry of the distribution of current returns conditional on current volatility dominates the leverage effect in determining the conditional skewness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.603 | 0.232 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".