Unconstrained Hedging within a Regime-Switching Market Model
Bibliographic record
Abstract
We address a problem of unconstrained hedging within a regime-switching market model. The essence of the problem is as follows: a random variable B (called a contingent claim) is stipulated and an agent trades in a market over a fixed finite interval t ∈ [0, T]. The goal of hedging is to determine the least initial wealth (called the price of the contingent claim) such that, starting from this wealth, the agent can trade in such a way that, at close of trade t = T, the wealth of the agent is almost-surely greater than or equal to the contingent claim B (enabling the agent to “pay off” the contingent claim). The problem of hedging (constrained as well as unconstrained) has been addressed within the framework of Brownian motion market models (see [1] and [2]). Our goal is to study this problem for market models which also include regime-switching in the sense that the market parameters are adapted not only to the filtration of a given Brownian motion (as is the case in Brownian motion market models) but to the joint filtration of a Brownian motion together with a regime-switching Markov chain.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".