Bibliographic record
Abstract
This thesis consists of three chapters in Bayesian financial econometrics. The first chapter proposes\nnew dynamic component models of returns and realized covariance (RCOV) matrices based on timevarying\nWishart distributions. Bayesian estimation and model comparison is conducted with a range of\nmultivariate GARCH models and existing RCOV models from the literature. The main method of model\ncomparison consists of a term-structure of density forecasts of returns for multiple forecast horizons. The\nnew joint return-RCOV models provide superior density forecasts for returns from forecast horizons of\n1 day to 3 months ahead as well as improved point forecasts for realized covariances. Global minimum\nvariance portfolio selection is improved for forecast horizons up to 3 weeks out. The second chapter\nproposes a full Bayesian nonparametric procedure to investigate the predictive power of exchange rates on\ncommodity prices for 3 commodity-exporting countries: Canada, Australia and New Zealand. I examine\nthe predictive effect of exchange rates on the entire distribution of commodity prices and how this effect\nchanges over time. A time-dependent infinite mixture of normal linear regression model is proposed for\nthe conditional distribution of the commodity price index. The mixing weights of the mixture follow a\nProbit stick-breaking prior and are hence time-varying. As a result, I allow the conditional distribution of\nthe commodity price index given exchange rates to change over time nonparametrically. The empirical\nstudy shows some new results on the predictive power of exchange rates on commodity prices. The\nthird chapter proposes a flexible way of modeling heterogeneous breakdowns in the volatility dynamics\nof multivariate financial time series within the framework of MGARCH models. During periods of\nnormal market activities, volatility dynamics are modeled by a MGARCH specification. I refer to any\nsignificant temporary deviation of the conditional covariance matrix from its implied GARCH dynamics\nas a covariance breakdown, which is captured through a stochastic component that allows for changes in\nthe whole conditional covariance matrix. Bayesian inference is used and I propose an efficient posterior\nsampling procedure. Empirical studies show the model can capture complex and erratic temporary\nstructural change in the volatility dynamics.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".