Essays in risk modeling, asset pricing and network measurement in finance
Bibliographic record
Abstract
Modelling financial interconnections and forecasting extreme losses are crucial for risk management in financial markets.This thesis studies multivariate risk spillovers at the high-dimensional market network level, as well as univariate extreme risk modelling at the asset level.The first chapter proposes a novel time series econometric method to measure high-dimensional directed and weighted market network structures.Direct and spillover effects at different horizons, between nodes and between groups, are measured in a unified framework.Using a similar network measurement framework, the second chapter investigates the relationship between stock illiquidity spillovers and the cross-section of expected returns.I find that central industries in illiquidity transmission networks earn higher average stock returns (around 4% per year) than other industries.The third chapter proposes a new Dynamic Stable GARCH model, which involves the use of stable distribution with time-dependent tail parameters to model and forecast tail risks in an extremely high volatility environment.We can differentiate extreme risks from normal market fluctuations with this model.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".