agentsclimarketplace

Mathfin contribution framing

Skill brycewang-stanford/Awesome-Journal-Skills/Mathematical-Finance-Skills/skills/mathfin-contribution-framing

Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. | 覆盖主流期刊的 Claude Code/Codex 期刊技能包,从选题、识别策略到表格规范与审稿回复全流程,助你快速发论文。

Install
npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill mathfin-contribution-framing

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

Use when articulating the contribution of a Mathematical Finance (Wiley) manuscript — frame the methodological novelty and its payoff for financial modelling (pricing, hedging, risk, portfolio, microstructure) so editor and referees see why the theorem matters, not just that it is true.

SKILL.md

5.5 KB, as published. Nobody here has run it

Contribution Framing (mathfin-contribution-framing)

When to trigger

  • The math is correct but the introduction does not say why it advances financial modelling
  • A referee might ask "this is rigorous, but what is the new modelling insight?"
  • Reviewing whether the stated contribution matches what the theorems actually deliver

The Mathematical Finance contribution bar

The journal evaluates papers on methodological novelty and contribution to financial modelling. Rigor is necessary but not sufficient: a correct theorem with no modelling payoff reads as a math paper sent to the wrong venue, while a modelling claim without proof reads as informal finance. The contribution must be both mathematically novel and consequential for a financial-modelling problem (pricing, hedging, risk measurement, portfolio choice, optimal execution, arbitrage theory).

How to frame the contribution

  1. Lead with the modelling problem, then the obstruction prior methods hit, then your theorem as the resolution.
  2. Name the novelty axis explicitly: a new tractable model, a weaker assumption set, a constructive solution where only existence was known, a sharper rate/bound, a new representation (e.g., of a risk measure), or a unifying framework.
  3. Translate the theorem into a modelling statement: "hence the option price solves...", "hence the optimal strategy is...", "hence the risk measure admits the representation..."
  4. Bound the claim to the hypotheses — state where the result holds and where it does not, consistent with the rigor culture (over-claiming is penalized).
  5. Place numerics in service of the claim: if you include experiments, frame them as illustrating the theorem (convergence, qualitative behavior), never as the contribution itself — routine computation on data is out of scope.

Novelty-axis evidence table

For each novelty axis the introduction must put specific evidence on the page; referees test the claim against the theorem statements themselves:

Novelty axisWhat the introduction must showHow a referee tests it
New tractable modelThe closed-form or characterizing equation (PDE/BSDE/transform) the model admitsRe-derive the characterization from the stated dynamics
Weaker assumption setThe exact hypothesis removed (e.g., no dominating measure, unbounded coefficients)Search the proof for a hidden reinstatement of the dropped condition
Constructive solutionThe object built (optimal strategy, hedging portfolio, stopping boundary)Check the construction is admissible and attains the value
Sharper rate/boundOld rate vs. new rate, with the regime where the gain bitesCompare against known lower bounds or counterexamples
New representationThe dual/variational formula and the space it lives onVerify both inequalities of the duality, not just one
Unifying frameworkAt least two prior results recovered as corollariesConfirm the corollaries follow without extra hypotheses

Worked vignette: a robust superhedging paper

Hypothetical manuscript: pathwise superhedging duality for path-dependent claims under volatility uncertainty. Applying the framing rules:

  • 【Modelling problem】price and superhedge a claim when no single probabilistic model is trusted.
  • 【Obstruction】classical duality needs a reference measure; under non-dominated uncertainty the martingale measures are mutually singular, so measurable-selection and capacity issues block the standard argument.
  • 【Theorem as resolution】duality between the cheapest superhedge and the supremum of martingale expectations holds for upper semicontinuous claims, with dual attainment.
  • 【Novelty axis】weaker assumption set (no dominating measure) plus a new representation.
  • 【Modelling payoff】robust price bounds and an explicit hedge that works model-free.
  • 【Scope of claim】upper semicontinuity is essential; the introduction says so and points to the counterexample section.

Calibration against accepted introductions

Accepted Mathematical Finance papers typically open in one to two pages: modelling problem and obstruction in the first paragraphs, the main theorem stated informally (or by number) by page two, a short literature paragraph keyed to assumptions, then a roadmap. Long motivational essays are rare; so are introductions that postpone the main result past the model setup. When unsure of current norms, calibrate against the latest issues rather than older volumes.

Anti-patterns

  • A theorem-dump introduction with no modelling "so what."
  • Claiming practical/empirical impact the paper does not establish.
  • Selling generality the proofs do not support.
  • Framing numerical results as the core contribution.
  • Burying the actual novelty under restated background.

Output format

【Modelling problem】one sentence
【Obstruction in prior work】what prevented it
【Theorem as resolution】one sentence
【Novelty axis】model / assumption / constructive / rate / representation / unification
【Modelling payoff】the financial statement the theorem licenses
【Scope of claim】where it holds / does not
【Next step】mathfin-data-analysis (if numerics) or mathfin-writing-style

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.