Essay on semiparametric efficient adaptive estimation and empirical applications in finance
Bibliographic record
Abstract
This thesis is composed of three chapters. The first two chapters construct semiparametric efficient adaptive estimators: one is for asymmetric GARCH-in-mean models; the other is for single-index models. However, estimation methodologies are different. The first chapter is related to maximum likelihood; and the second chapter minimizes mean square errors. In Chapter 1, without imposing additional restrictions on the distribution of disturbances other than some regular assumptions, I show that parameters appearing in the conditional standard deviation function can be adaptively estimated. The kernel smoothing parameter is calculated by minimizing mean squared errors between estimated score functions and target score functions. Significant asymmetric effects are identified with daily value-weighted stock index returns on NYSE/AMEX. Monte Carlo simulation results support my theory. In Chapter 2, conditional expectation function is estimated by k − nearest neighbor method. I show that semiparametric efficient estimate of parameters of single-index models and k can be calculated simultaneously. Monte Carlo experiments results show that the semiparametric estimator performs equally well across different data generating mechanisms even for small samples. In Chapter 3, catastrophe-linked securities whose payoffs are tied to the occurrence of natural disasters allow insurers to better diversify their risks through capital markets while at the same time offering an attractive new asset class to investors. Up to now, this class of assets is still under development, and few empirical applications have been carefully analyzed. This chapter examines one of the catastrophe-linked instruments—PCS options traded at the CBOT. Two main issues are explored: (a) Theoretical prices are derived by assuming complete and arbitrage free market. Comparing market prices to theoretical prices, three puzzles are observed. (b) Comparing PCS call spread option contracts to traditional excess-of-loss catastrophe reinsurance contracts; I find that pricing patterns of these two types of assets are similar. Other factors, not limited capacity, are capable of explaining these special pricing patterns.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".