Risk Management and Agency Theory: Role of the Put Option in Corporate Bonds
Bibliographic record
Abstract
This study sets out a new methodology to exemplify, through a set of risk metrics called the Greeks, impact of a bond’s structured provisions (e.g., call, put, and conversion options) on its risk characteristics and its propensity for agency conflicts. The methodology is assessed by applying it to a sample of 159 non-convertible bonds, with time-scheduled call and put provisions issued between 1977 and 2005. A structural contingent-claims valuation model is used to value the bonds and estimate the Greeks. The methodology is used to assess the impact of the call and put provisions on the bond’s credit risk and interest-rate risk, as well as the provisions’ ability to mitigate the agency conflict associated with over-investment, under-investment, asset-substitution, and information asymmetry about the firm’s true risk among stakeholders. The main findings of this study are that the put option plays a key role in reducing credit risk, mitigating agency conflict, and protecting against volatility shocks; conversely, the call option plays a key role in reducing interest-rate risk. The methodology is sufficiently general to apply to bonds and preferred stock with any set of structured provisions.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".