Bibliographic record
Abstract
The third essay empirically studies a jump-diffusion model for stock price movements using high-frequency data. The stock price is assumed to follow a jump-diffusion process which may exhibit time-varying volatilities. An econometric technique is then developed for this model and applied to high-frequency time series of stock prices that are subject to microstructure noises. The estimation method is based on first devising a localized particle filter and then employing fixed-lag smoothing technique in the Monte Carlo EM algorithm to perform the maximum likelihood estimation and inference. Evidence based on the intra-day IBM stock prices in 2004 suggests that high-frequency data is crucial to disentangling frequent small jumps from infrequent large jumps. Furthermore, accounting for microstructure noises becomes important as the sampling frequency increases. The first essay studies whether credit rating downgrades feed back on the asset value of the downgraded companies and thus cause real losses. To investigate this issue, I construct a structural credit risk model incorporating rating changes and their associated feedback losses. A maximum likelihood estimation method based on time series of equity prices and credit ratings is then developed for the credit rating feedback model. Evidence from a sample of US public firms downgraded from investment grade to junk shows strong support for the existence of feedback losses. The estimated feedback losses are significant for a third of our sample, and the cross-sectional mean of the feedback loss is 7%. In the second essay, the transformed-data maximum likelihood estimation (MLE) method for structural credit risk models developed by Duan (1994) is extended to account for the fact that observed equity prices are likely contaminated by trading noises. With the presence of trading noises, the likelihood function based on the observed equity prices can only be evaluated via some nonlinear filtering scheme. A localized particle filtering algorithm is devised for the structural credit risk model of Merton (1974) to execute this task. Applying the estimation method to the Dow Jones 30 firms and 100 randomly selected US public firms, the findings suggest that ignoring trading noises can lead to significant over-estimation of the firm's asset volatility.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".