arXiv research

This paper develops a learning framework for optimal strategies in multi-stage decentralized matching markets.

A new algorithm improves top-$k$ recommendation accuracy by considering item payoffs uncertainty.

The valuation process that economic agents undergo for investments with uncertain payoff typically depends on their statistical views on possible future outcomes, their attitudes toward risk, and, of course, the payoff structure itself. Yields vary across different investment opportunities and their interrelations are …

Novel pricing method for equity-indexed annuities under uncertain volatility and stochastic interest rate.

Study on learning strategies in matching markets with uncertain preferences.

Study examines strategic exit timing in uncertain competition.

This study measures price risk aversion using indirect utility functions in a lab experiment.

New method optimizes resource allocation for uncertain tasks.

Study path-dependent affine models under uncertain parameters for financial applications.

Geometrically convex return risk measures on AM-algebras

Method constructs CFMMs matching desired payoffs.

The paper solves investment problems with uncertain factors using game theory.

Optimal payoff choice constrained by Bregman-Wasserstein divergence.

Optimal portfolio yields a digital option payoff.

Study finds cheapest possible payoff under ambiguity, linking to maxmin expected utility.

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.

New method uses neural networks for better financial hedging.

The paper explores how to handle uncertain evidence in probabilistic models.

Paper shows how to replicate payoffs without oracles in CFMMs.

New method calculates Shapley values for uncertain functions.

Nonparametric pricing and hedging of exotic derivatives using signature payoffs.

Paper establishes robust asset pricing theorems under uncertainty.

New algorithm adapts to unknown smoothness in contextual bandits.

In this paper, within the framework of uncertainty theory, the valuation of equity warrants is investigated. Different from the methods of probability theory, the equity warrants pricing problem is solved by using the method of uncertain calculus. Based on the assumption that the firm price follows an uncertain differe…

Develops a new method for robust risk measurement by averaging nearby payoffs.

Agent optimizes perpetual contract liquidation with transaction costs and risk.

Multi-armed bandit problems are the most basic examples of sequential decision problems with an exploration-exploitation trade-off. This is the balance between staying with the option that gave highest payoffs in the past and exploring new options that might give higher payoffs in the future. Although the study of band…

Quantum methods model uncertain volatility in financial markets.

New model predicts dynamic volatility in uncertain financial markets.

The target of this paper is to consider model the risky asset price on the financial market under the Knightian uncertainty, and pricing the ask and bid prices of the uncertain risk. We use the nonlinear analysis tool, i.e., G-frame work [26], to construct the model of the risky asset price and bid-ask pricing for the …

New findings show pure strategy equilibria are more robust in a war of attrition game.

Algorithm finds near-optimal strategy in changing zero-sum games.

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 …

New algorithms for stochastic linear bandits with heavy-tailed payoffs achieve nearly optimal regret.

Study calculates Bayes risk for semi-supervised learning with uncertain labels.

The game-theoretic risk management framework put forth in the precursor work "Towards a Theory of Games with Payoffs that are Probability-Distributions" (arXiv:1506.07368 [q-fin.EC]) is herein extended by algorithmic details on how to compute equilibria in games where the payoffs are probability distributions. Our appr…

Study of $2 imes 2$ zero-sum games with noisy observations and commitments.

New algorithm for uncertain time series classification.

This paper studies robust payoff allocation in submodular games, especially against replication.

Study on optimal information acquisition in Kyle model with entropy cost.

New algorithm for reinforcement learning in uncertain environments with unknown thresholds.

We derive a formula for liquidity providers' payoff on DEXs, linking it to volatility.

In an online contract selection problem there is a seller which offers a set of contracts to sequentially arriving buyers whose types are drawn from an unknown distribution. If there exists a profitable contract for the buyer in the offered set, i.e., a contract with payoff higher than the payoff of not accepting any c…

The paper examines bounds for stop-loss payoffs using transformed random variables.

New decision-theoretic calibration error metric improves prediction reliability.

Quantum Monte Carlo speeds up option pricing for complex payoff functions.

New method uses DistRL to estimate entire payoff distribution for financial derivatives.