Estimating Merton’s Model by Maximum Likelihood with Survivorship Consideration, University of Toronto working paper
Bibliographic record
Abstract
One critical difficulty in implementing Merton’s (1974) credit risk model is that the underlying asset valuecannotbedirectlyobserved. Themodelrequirestheunobservedassetvalueandtheunknown volatility parameter as inputs. The estimation problem is further complicated by the fact that typical data samples are for the survived firms. This paper applies the maximum likelihood principle to develop an estimation procedure and study its properties. The maximum likelihood estimator for the mean and volatility parameters, asset value, credit spread and default probability are derived for Merton’s model. To our knowledge, this paper is the first to address the survivorship issue as well as the first to apply the maximum likelihood method to credit risk assessment in a portfolio context. A Monte Carlo study is conducted to examine the performance of this maximum likelihood method. An application to real data is also presented.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".