Bibliographic record
Abstract
This paper provides a comprehensive analysis of the day of the week, non-trading day (post-holiday), and month of the year seasonal effects in the daily returns and volatility of the S&P 500 index. We use bootstrapping to demonstrate that, surprisingly, these three prominent calendar effects are statistically significant in daily volatility, but not in daily average returns. We model this form of seasonal heteroscedasticity by introducing the periodic stochastic volatility (PSV) model for characterizing the seasonal patterns of daily financial market volatility. We analyze the interaction of seasonal heteroscedasticity with fat tails by comparing the performance of Gaussian PSV and fat-tailed PSVt specifications to the plain vanilla SV and SV t benchmarks. Consistent with the bootstrapping results, we find strong evidence of seasonal periodicity in volatility, which substantially reduces the need for fat tails, and is robust to the exclusion of the Crash of 1987 outliers. The SV parameters are estimated by implementing the Bayesian MCMC methods developed by Chib, Nardari and Shephard (2002), with the addition of a Gibbs step for sampling the seasonal volatility level effects. We compute in-sample and outof-sample density forecasts for assessing the adequacy of the conditional distribution. We conclude by using Bayes factors as a likelihood-based framework for ranking the SV specifications.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".