Hedging financial and business risks in agriculture with commodity-linked bonds
Bibliographic record
Abstract
The total risk faced by any business is defined as the total variability in the returns to equity. Generally speaking, regardless of the nature of the firm (e.g. corporate form, industry), the sources of total risk are universally equated to the sum of business risk and financial risk. In order to reduce total risk, the best approach requires the firm to consider the two risks simultaneously. Unfortunately the use of the usual financial hedging instruments (Forward Contracts, Futures Contracts, and Options etc.) is often regarded independently of the financing function of the firm. The symmetrical argument is that a firm that is increasing its use of debt should consider some form of hedging strategy in order to reduce business risk and financial risk (Turvey (1989), Turvey and Baker (1989, 1990). Commodity-linked bonds seem to be a candidate for hedging financial risk and business risk simultaneously. In this dissertation, I will study commodity-linked bonds and their applications in agricultural finance. The first order of business in my dissertation is to provide an introduction to commodity-linked bonds, a brief history of their use and related experiences with the issue of various indexed securities. A discussion of the possibility of using commodity-linked bonds in agriculture, the characteristics and categories of such bonds, follows. The second issue addressed in my dissertation is the design of various kinds of commodity-linked bonds (loans) in agriculture, their valuation, using a one-factor model, and a discussion of issues related to commodity-linked bonds (loans) in agriculture. The third issue in my dissertation is the development of a new general theoretical model for valuing commodity-linked bonds based on the Heath-Jarrow-Morton (HJM) framework. The model deals with four dimensions of uncertainty: the prices of the underlying commodity, the value of firm that issues bonds, interest rates, and convenience yields. A mathematical formula for the price of commodity-linked bonds is derived. The results from earlier studies (Black and Scholes (1973), Merton (1973), Schwartz (1982), Atta-Mensah (1992)) can be obtained by specifying appropriate restrictions in the general model. Using similar assumptions, as found in Miura and Yamauchi (1998) and Carr (1987), more reasonable results can be obtained through the application of the present model. The final issue in my dissertation is the presentation of Monte Carlo simulation results for commodity-linked bonds under various assumptions, the sensitivity analysis of the bond value to the volatility of the underlying variable, and the application of this model to value corn-linked bonds (loans) in Ontario using land value, corn price, interest rates, and convenience yields.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".