Bayesian Cointegrated Vector Autoregression Models Incorporating alpha-stable Noise for\n Inter-day Price Movements Via Approximate Bayesian Computation
Bibliographic record
Abstract
We consider a statistical model for pairs of traded assets, based on a Cointegrated Vector\nAuto Regression (CVAR) Model. We extend standard CVAR models to incorporate estimation of\nmodel parameters in the presence of price series level shifts which are not accurately modeled\nin the standard Gaussian error correction model (ECM) framework. This involves developing a\nnovel matrix-variate Bayesian CVAR mixture model, comprised of Gaussian errors intra-day and\n$\\alpha$-stable errors inter-day in the ECM framework. To achieve this we derive conjugate\nposterior models for the Scale Mixtures of Normals (SMiN CVAR) representation of\n$\\alpha$-stable inter-day innovations. These results are generalized to asymmetric intractable\nmodels for the innovation noise at inter-day boundaries allowing for skewed $\\alpha$-stable\nmodels via Approximate Bayesian computation.\n¶ Our proposed model and sampling methodology is general, incorporating the current CVAR\nliterature on Gaussian models, whilst allowing for price series level shifts to occur either\nat random estimated time points or known \\textit{a priori} time points. We focus analysis on\nregularly observed non-Gaussian level shifts that can have significant effect on estimation\nperformance in statistical models failing to account for such level shifts, such as at the\nclose and open times of markets. We illustrate our model and the corresponding estimation\nprocedures we develop on both synthetic and real data. The real data analysis investigates\nAustralian dollar, Canadian dollar, five and ten year notes (bonds) and NASDAQ price series.\nIn two studies we demonstrate the suitability of statistically modeling the heavy tailed noise\nprocesses for inter-day price shifts via an $\\alpha$-stable model. Then we fit the novel\nBayesian matrix variate CVAR model developed, which incorporates a composite noise model for\n$\\alpha$-stable and matrix variate Gaussian errors, under both symmetric and non-symmetric\n$\\alpha$-stable assumptions.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".