Bibliographic record
Abstract
This research motivates the use of Markov chains in modeling financial time series. Then, it explains the returns and the volatility on the Toronto Stock Exchange (TSX) using some Markov-switching models. These models are: the conditional capital asset pricing model, the conditional Sharpe model, and the exponential autoregressive model with state-dependent heteroscedasticity. It also tests for cointégration between the TSX and some other major exchanges, relying on the first-order and the second-order Markov chains. \n \nThe asymmetry, the multiple peaks, or the fat tails in the distribution of the returns on the TSX and on the other exchanges indicates they could not be modelled as random realizations from a single normal distribution. The switching regressions turn out to have a greater explanatory power and provide further understanding of the TSX. \n \nABSTRACT IN FRENCH- \nCette recherché justifie l’utilisation des chaînes de Markov dans la modélisation des séries chronologiques financières. Ensuite, elle explique les rendements et la volatilité sur la Bourse de Toronto (TSX) en utilisant quelques modèles de Markov à changement de régime. Ces modèles sont : le modèle conditionnel d’évaluation des actifs financiers, le modèle conditionnel de Sharpe et le modèle autorégressif exponentiel avec une hétéroscedasdacité conditionnelle qui dépend du régime.Elle teste également la cointégration entre le TSX et d’autres bourses, en s’appuyant sur les chaînes de Markov de premier et de second ordre. \n \nL’asymétrie, les nombreux pics ou l’épaisseur des queues de la distribution des rendements sur la TSX et sur les autres bourses indiquent qu’ils ne peuvent pas être modélisés comme étant des réalisations aléatoires provenant d’une seule distribution normale. Il s’avère que les régressions avec changement de régime ont un plus grand pouvoir explicatif et fournissent une meilleure compréhension du TSX.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".