A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study finds cheapest possible payoff under ambiguity, linking to maxmin expected utility.
Optimal payoff choice constrained by Bregman-Wasserstein divergence.
In this work, we expand the idea of Samuelson[3] and Shepp[2,5,6] for stock optimization using the Bachelier model [4] as our models for the stock price at the money (X[stock price]= K[strike price]) for the American call and put options [1]. At the money (X= K) for American options, the expected payoff of both the cal…
Game theory model shows optimal investment strategy for wealth growth.
Study on optimal information acquisition in Kyle model with entropy cost.
We analyze the relation between earning forecast accuracy and expected profitability of financial analysts. Modeling forecast errors with a multivariate Gaussian distribution, a complete characterization of the payoff of each analyst is provided. In particular, closed-form expressions for the probability density functi…
New decision-theoretic calibration error metric improves prediction reliability.
A new method for calculating ES from VaR under Solvency II.
We study the use of the multilevel Monte Carlo technique in the context of the calculation of Greeks. The pathwise sensitivity analysis differentiates the path evolution and reduces the payoff's smoothness. This leads to new challenges: the inapplicability of pathwise sensitivities to non-Lipschitz payoffs often makes …
A new Bayesian method optimizes time-dependent expensive functions with lookahead.
New method uses DistRL to estimate entire payoff distribution for financial derivatives.
Most decision theories, including expected utility theory, rank dependent utility theory and cumulative prospect theory, assume that investors are only interested in the distribution of returns and not in the states of the economy in which income is received. Optimal payoffs have their lowest outcomes when the economy …
Choquet and minimax expectations are equivalent in European option pricing.
We first study an optimal stopping problem in which a player (an agent) uses a discrete stopping time in order to stop optimally a payoff process whose risk is evaluated by a (non-linear) -expectation. We then consider a non-zero-sum game on discrete stopping times with two agents who aim at minimizing their respect…
We derive a formula for liquidity providers' payoff on DEXs, linking it to volatility.
Analysts use vague language in reports to convey useful information about future payoffs.
The paper examines bounds for stop-loss payoffs using transformed random variables.
We study optimal double stopping problems driven by a Brownian bridge. The objective is to maximize the expected spread between the payoffs achieved at the two stopping times. We study several cases where the solutions can be solved explicitly by strategies of threshold type.
We study the optimal stopping problem of pricing an American Put option on a Zero Coupon Bond (ZCB) in the Musiela's parametrization of the Heath-Jarrow-Morton (HJM) model for forward interest rates. First we show regularity properties of the price function by probabilistic methods. Then we find an infinite dimensional…
The paper extends cost-efficiency analysis to incomplete markets.
We consider the problem of pricing basket options in a multivariate Black Scholes or Variance Gamma model. From a numerical point of view, pricing such options corresponds to moderate and high dimensional numerical integration problems with non-smooth integrands. Due to this lack of regularity, higher order numerical i…
New algorithms reduce regret in non-stationary bandits with increasing payoffs.
This paper develops a learning framework for optimal strategies in multi-stage decentralized matching markets.
We consider the problem faced by a service platform that needs to match limited supply with demand but also to learn the attributes of new users in order to match them better in the future. We introduce a benchmark model with heterogeneous "workers" (demand) and a limited supply of "jobs" that arrive over time. Job typ…
Study optimal stopping times under regime-switching models with constraints.
Real life hedging in the Black-Scholes model must be imperfect and if the stock's drift is higher than the risk free rate, leads to a profit on average. Hence the option price is examined as a fair game agreement between the parties, based on expected payoffs and a simple measure of risk. The resulting prices result in…
Consider an investor trading dynamically to maximize expected utility from terminal wealth. Our aim is to study the dependence between her risk aversion and the distribution of the optimal terminal payoff. Economic intuition suggests that high risk aversion leads to a rather concentrated distribution, whereas lower ris…
We analyze an optimal stopping problem with random maturity under a nonlinear expectation with respect to a weakly compact set of mutually singular probabilities . The maturity is specified as the hitting time to level of some continuous index process at which the payoff process is even allowed to have…
We consider evaluation methods for payoffs with an inherent financial risk as encountered for instance for portfolios held by pension funds and insurance companies. Pricing such payoffs in a way consistent to market prices typically involves combining actuarial techniques with methods from mathematical finance. We prop…
A game-theoretic approach selects features by testing their marginal contributions.
The paper calculates the value of information in high-dimensional decision making.
The paper generalizes Feynman-Kac formula for volatility uncertainty.
Method constructs CFMMs matching desired payoffs.
It has been recently shown that numerical semiparametric bounds on the expected payoff of fi- nancial or actuarial instruments can be computed using semidefinite programming. However, this approach has practical limitations. Here we use column generation, a classical optimization technique, to address these limitations…
Data-driven method for option pricing using historical asset prices.
Researchers find a way to price American options without relying on specific asset price models.
Optimal portfolio yields a digital option payoff.
ARC algorithm optimizes dynamic pricing with correlated observations.
We consider fractional Black-Scholes market with proportional transaction costs. When transaction costs are present, one trades periodically i.e. we have the discrete trading with equidistance between trading times. We derive a non trivial hedging error for a class of European options with convex payoff in the…
We introduce signature payoffs, a family of path-dependent derivatives that are given in terms of the signature of the price path of the underlying asset. We show that these derivatives are dense in the space of continuous payoffs, a result that is exploited to quickly price arbitrary continuous payoffs. This approach …
The paper uncovers the impact of price and payoff autocorrelations in multi-period asset pricing models.
Paper tackles multiplayer symmetric games, securing equal share for n players.
New method uses neural networks for better financial hedging.
Paper shows how to replicate payoffs without oracles in CFMMs.
Optimizes non-linear outcomes from summed contributions.
Investment strategy optimization from discrete to continuous models.
In recent years there has been an advent of quanto options in energy markets. The structure of the payoff is rather a different type from other markets since it is written as a product of an underlying energy index and a measure of temperature. In the HJM framework, by adopting the futures energy dynamics, we use the M…
We study a non-parametric multi-armed bandit problem with stochastic covariates, where a key complexity driver is the smoothness of payoff functions with respect to covariates. Previous studies have focused on deriving minimax-optimal algorithms in cases where it is a priori known how smooth the payoff functions are. I…