Study finds cheapest possible payoff under ambiguity, linking to maxmin expected utility.
problem Finding cost-efficient payoffs in uncertain market conditions.
method Developed a new concept of robust cost-efficient payoff and linked it to maxmin expected utility.
result Solutions to maxmin robust expected utility are robust cost-efficient.
The paper extends cost-efficiency analysis to incomplete markets.
problem Cost-efficiency in incomplete financial markets.
method Extends results from complete markets to incomplete markets, introduces new preferences.
result Optimal portfolios in non-decreasing preferences are perfectly cost-efficient.
Abstract and counterexamples show limitations of cost-efficiency in incomplete markets.
problem Understanding cost-efficiency in incomplete financial markets.
method Simple 3-state model and expected utility maximization problem.
result Characterization of perfectly cost-efficient claims and its application to incomplete markets.
Intertemporal model for cost-efficient consumption using copulas.
problem Optimizing consumption over time considering risk preferences.
method Copulas for intertemporal structure, Distribution Builder for risk preferences, Black-Scholes and CEV models for demonstration.
result Demonstrates cost-efficient consumption model using copulas.
New optimal transport divergences derived from scoring functions.
problem Developing new divergences for optimal transport.
method Using scoring functions as cost functions in optimal transport.
result Comonotonic coupling is optimal for many new divergences.
A cost-efficient method for hyperparameter tuning using multi-fidelity Bayesian optimization.
problem Expensive hyperparameter tuning with limited knowledge transfer methods.
method Amortized Auto-Tuning (AT2) framework for multi-task, multi-fidelity Bayesian optimization.
result AT2 leads to the best hyperparameter recommendation and is more cost-efficient.
New algorithm reduces online hyperparameter optimization costs.
problem High cost of evaluating validation examples in online HPO.
method Modeling online HPO as a time-varying Bayesian optimization problem, proposing a costly feedback setting.
result Cost-efficient GP-UCB algorithm reaches human expert-level performance.
RACER optimizes LLM-as-judge accuracy with dynamic reasoning selection.
problem Balancing reasoning accuracy with computational cost in LLM-as-judge settings.
method Formulates routing as a constrained distributionally robust optimization problem, accounting for distribution shift via KL-divergence uncertainty set.
result RACER achieves superior accuracy-cost trade-offs under distribution shift.
Cer-Eval saves LLM evaluation costs while maintaining accuracy.
problem Challenges in evaluating large language models due to large dataset requirements.
method Adapts to different evaluation objectives, uses test sample complexity, and develops a partition-based algorithm.
result Cer-Eval can save 20-40% test points with comparable accuracy and 95% confidence guarantee.
Cost-efficient distributed learning via combinatorial bandits.
problem Trade-off between error and runtime in distributed SGD.
method Combinatorial multi-armed bandit model to learn fastest workers.
result Significantly lower errors with same computational efforts and less communication.
Customer scoring models are the core of scalable direct marketing. Uplift models provide an estimate of the incremental benefit from a treatment that is used for operational decision-making. Training and monitoring of uplift models require experimental data. However, the collection of data under randomized treatment as…
We present a generic framework for trading off fidelity and cost in computing stochastic gradients when the costs of acquiring stochastic gradients of different quality are not known a priori. We consider a mini-batch oracle that distributes a limited query budget over a number of stochastic gradients and aggregates th…
Method constructs CFMMs matching desired payoffs.
problem Creating CFMMs with specific payoff functions.
method Uses convex analysis and Fenchel conjugacy.
result Every concave, nonnegative, nondecreasing, 1-homogeneous payoff has a corresponding convex CFMM.
Optimal payoff choice constrained by Bregman-Wasserstein divergence.
problem Maximizing utility under a deviation constraint from a benchmark.
method Solving the problem using Bregman-Wasserstein divergence with a convex function φ.
result Provided the optimal payoff choice in this setting.
Optimal portfolio yields a digital option payoff.
problem Portfolio optimization under generalized dual theory of choice.
method Characterized optimal solution and derived it in closed form.
result Payoff is a digital option that yields in-the-money payoff in good market scenarios.
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.
problem Hidden dependence of asset pricing models on price and payoff autocorrelations.
method Obtained approximations of the basic pricing equation describing various parameters.
result Valid results for other pricing models like ICAPM and APM.
Optimizes user marketing campaigns to balance cost and effectiveness.
problem Lack of methods to optimize marketing campaigns considering cost and effectiveness.
method Proposes a treatment effect optimization algorithm using deep learning to balance cost and effectiveness.
result Demonstrates superior performance in cost-efficiency and real-world business value.
Hybrid model uses GNNs and pathfinding to optimize portfolio rebalancing costs.
problem Optimizing transaction costs in dynamic portfolio rebalancing.
method Combines GNNs for cost prediction and Dijkstra's algorithm for pathfinding.
result Significantly reduces transaction costs in financial asset graphs.
New method uses neural networks for better financial hedging.
problem Spanning multi-asset payoffs with vanilla options.
method One-hidden-layer feedforward neural networks for numerical solution.
result Better hedging results with neural networks compared to single-asset approaches.
Paper shows how to replicate payoffs without oracles in CFMMs.
problem Replicating payoffs without oracles in CFMMs.
method Using liquidity provider shares in CFMMs to match any monotonic payoff.
result Explicit method and formula for trading functions and earnings.
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…
The pricing, hedging, optimal exercise and optimal cancellation of game or Israeli options are considered in a multi-currency model with proportional transaction costs. Efficient constructions for optimal hedging, cancellation and exercise strategies are presented, together with numerical examples, as well as probabili…
Machine learning has automated much of financial fraud detection, notifying firms of, or even blocking, questionable transactions instantly. However, data imbalance starves traditionally trained models of the content necessary to detect fraud. This study examines three separate factors of credit card fraud detection vi…
Develops a new method for robust risk measurement by averaging nearby payoffs.
problem Measuring risk under uncertainty with a focus on robustness.
method Averaging nearby payoffs weighted by a chosen metric.
result The method leads to a convex risk measure and provides stability under large neighborhoods.
Agent optimizes perpetual contract liquidation with transaction costs and risk.
problem Optimizing perpetual contract liquidation with transaction costs and risk.
method Solving stochastic control problem for optimal trading strategy.
result Closed-form expression and approximations for optimal strategy.
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…
New findings show pure strategy equilibria are more robust in a war of attrition game.
problem Analyzing a game of war of attrition under complete information.
method Examined the stability of equilibria in pure and mixed strategies under varying payoffs.
result Pure strategy equilibria are more robust to perturbations of the canonical model.
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.
problem Stochastic linear bandits with heavy-tailed payoffs.
method Median of means and dynamic truncation.
result Sublinear regret bound of O(d21T1+ε1) for ε∈(0,1]. 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 2imes2 zero-sum games with noisy observations and commitments.
problem Analyzing 2imes2 zero-sum games with noisy observations and commitments. method Modeling a 2imes2 zero-sum game with a leader committing to a strategy and a follower observing a noisy version of the leader's action. result Observing the leader's action is either beneficial or immaterial for the follower, and the equilibrium payoff is bounded.
This paper studies robust payoff allocation in submodular games, especially against replication.
problem Payoff allocation in submodular games, especially robustness against replication.
method Systematically studied replication manipulation in submodular games, introduced replication robustness metric, and validated with empirical ML data market.
result Conditions characterizing robustness of semivalues in submodular games.
We study the problem of repeated play in a zero-sum game in which the payoff matrix may change, in a possibly adversarial fashion, on each round; we call these Online Matrix Games. Finding the Nash Equilibrium (NE) of a two player zero-sum game is core to many problems in statistics, optimization, and economics, and fo…
Study on optimal information acquisition in Kyle model with entropy cost.
problem Optimal information acquisition in Kyle model with entropy cost.
method Continuous signals are optimal, and any signal with a logit posterior distribution yields the same ex-ante value.
result Posterior expected payoff becomes normally distributed as information acquisition cost increases.
Flood forecasts are crucial for effective individual and governmental protective action. The vast majority of flood-related casualties occur in developing countries, where providing spatially accurate forecasts is a challenge due to scarcity of data and lack of funding. This paper describes an operational system provid…
We derive a formula for liquidity providers' payoff on DEXs, linking it to volatility.
problem Liquidity providers on DEXs are undercompensated for their service.
method We derive a payoff formula for liquidity providers on DEXs, assuming geometric Brownian price movements and zero arbitrage.
result The payoff from liquidity fees is a near-linear function of 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.
problem Bounding stop-loss payoffs for a difference of two random variables.
method Analyzes crossing points of cdfs of original and transformed random variables.
result Unique pairwise crossing points for mortality-linked securities under symmetric copulas.
New decision-theoretic calibration error metric improves prediction reliability.
problem Improving the reliability of predictions for decision-making.
method Proposed Calibration Decision Loss (CDL) and an efficient algorithm to achieve near-optimal CDL.
result Near-optimal CDL guarantees vanishing payoff loss from miscalibration.
Quantum Monte Carlo speeds up option pricing for complex payoff functions.
problem Efficiently pricing options with complex payoff functions using quantum computing.
method Developed a quantum Monte Carlo algorithm for multidimensional Black-Scholes PDEs.
result Proved polynomial computational complexity and speed-up over classical methods.
New method uses DistRL to estimate entire payoff distribution for financial derivatives.
problem Traditional methods focus on expected option value; this tackles risk-aware pricing.
method Reinterprets and proposes a framework using Distributional Reinforcement Learning (DistRL).
result Demonstrates enhanced risk-aware pricing and uncertainty quantification on Asian options.
We consider a sequential learning problem with Gaussian payoffs and side information: after selecting an action i, the learner receives information about the payoff of every action j in the form of Gaussian observations whose mean is the same as the mean payoff, but the variance depends on the pair (i,j) (and may…
CAPO optimizes LLM prompts more efficiently and cost-effectively.
problem Costly and inefficient automatic prompt optimization for LLMs.
method Integrates AutoML techniques for evolutionary optimization of instructions and few-shot examples.
result Significantly improves prompt optimization efficiency and accuracy.
Novel approach to financial derivatives pricing using rough path theory.
problem No-arbitrage conditions in financial markets necessitating precise integration methods.
method Developed a polynomial-based approximation class for rough path functionals, extending to non-geometric rough paths.
result Motivated a hypothesis for payoff functionals in financial markets, facilitating analysis.
New algorithms improve on bandit feedback in matrix games with unknown payoff matrices.
problem Improving performance in matrix games with unknown payoff matrices and bandit feedback.
method Regret analyses of variants of UCB and K-learning.
result New algorithms achieve lower regret compared to adversarial bandit algorithms.
In the spirit of Arrow-Debreu, we introduce a family of financial derivatives that act as primitive securities in that exotic derivatives can be approximated by their linear combinations. We call these financial derivatives signature payoffs. We show that signature payoffs can be used to nonparametrically price and hed…
Paper defines saddle points in asymmetric Dynkin games using martingale theory.
problem Tackles saddle point conditions in asymmetric Dynkin games with partial information.
method Uses martingale theory to identify super and submartingales related to equilibrium payoffs.
result Characterizes saddle point strategies in terms of equilibrium payoffs' dynamics and Doob-Meyer decompositions.