A Simple Model for Trading Climate Risk
Bibliographic record
Abstract
Short term climate events such as the sea surface temperature anomaly known as El Niño are financial risk sources leading to incomplete markets. To make such risk tradable, we use a market model in which a climate index provides an extra investment option. Given one possible market price of risk each agent can maximize the exponential utility from three sources of income: capital market, additional security, and individual risk exposure. Under an equilibrium condition the market price of risk is uniquely determined by a backward stochastic differential equation. We translate these stochastic equations into semi-linear partial differential equations for the simulation of which numerical schemes are available. We choose two simple models for sea surface temperature, and with ENSO risk exposed fisher and farmer and a non-exposed bank three toy agents. By simulating their optimal investment into the climate index we obtain first insight into the dynamics of the market. Klimaereignisse auf einer kurzen Zeitskala, wie die Anomalie der Oberflächentemperatur im Ozean, bekannt als El Niño, sind finanzielle Risikoquellen, die zu unvollständigen Märkten führen. Um so ein Risiko handelbar zu machen, nutzen wir ein Marktmodell, in dem ein Klimaindex eine zusätzliche Investitionsmöglichkeit schafft. Zu einem gegebenen Marktpreis des Risikos kann jeder Händler seinen exponentiellen Nutzen aus drei Einkommensquellen maximieren: dem Kapitalmarkt, der zusätzlichen Anlagemöglichkeit und der individuellen Risikoexposition. Unter einer Gleichgewichtsbedingung wird der Marktpreis des Klimarisikos durch eine stochastische Rückwärtsgleichung eindeutig bestimmt. Diese stochastischen Gleichungen übertragen wir in semilineare partielle Differentialgleichungen, für die numerische Lösungsmethoden vorhanden sind. Wir wählen zwei einfache Modelle für die Meeresoberflächentemperatur. Dann betrachten wir einen Fischer und einen Farmer, die El Niño- Risiko ausgesetzt sind, sowie eine Bank, die diesem Risiko nicht ausgesetzt ist. Durch Simulation ihrer optimalen Investitionen in den Klimaindex erhalten wir einen ersten Einblick in die Dynamik dieses Marktes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".