The study examines the discrepancies between binary forecasts and real-world outcomes, revealing their often misleading nature.
problem The confusion between binary forecasts and real-world payoffs in decision-making and prediction.
method Comparative analysis of binary forecasts, bets, and real-world continuous payoffs under different tail conditions.
result Binary forecasting abilities do not translate to better real-world performance, and vice versa, especially under nonlinearities.
New algorithms reduce regret in non-stationary bandits with increasing payoffs.
problem Non-stationary bandits with monotonically increasing payoffs.
method R-ed-UCB for rested case and R-less-UCB for restless case.
result Regret bound of O ~ ( T 2 3 ) \widetilde{\mathcal{O}}(T^{\frac{2}{3}}) O ( T 3 2 ) under certain conditions. 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.
Study shows Elo models fail to accurately measure transitive strength in competitive games.
problem Elo models fail to correctly identify the transitive component in real-world competitive games.
method Investigated the challenge of identifying the transitive component in games, proposed an extension of the Elo score.
result Disc ranking system assigns two scores: skill and consistency.
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.
A new Bayesian method optimizes time-dependent expensive functions with lookahead.
problem Maximizing a time-dependent, expensive oracle with limited evaluations.
method Recursive, two-step lookahead expected payoff (r2LEY) acquisition function.
result r2LEY outperforms myopic methods in synthetic and real-world datasets.
A new algorithm for personalized recommendations adapts to changing user interests.
problem Adapting to time-varying user interests in recommendation systems.
method Contextual bandit approach with models for disjoint and hybrid payoffs.
result Sublinear regret in time length T for abrupt reward changes.
Analysts use vague language in reports to convey useful information about future payoffs.
problem Lack of precise numerical forecasts in analyst reports.
method Empirical analysis of analyst reports to assess the predictive power of linguistic tone.
result The textual tone of analyst reports has predictive power for forecast errors and subsequent revisions, especially when language is vague and uncertainty is high.
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.
SpectralTS improves efficiency of Thompson Sampling for graph-based bandits.
problem Efficiently solving bandit problems with smooth payoffs on graphs.
method SpectralTS algorithm for a graph-based bandit problem with effective dimension d.
result SpectralTS offers a computationally more efficient alternative with regret scaling as d*sqrt(T ln N).
SISR improves feature attribution in complex payoff schemes.
problem Distorted feature attributions due to non-additive payoff functions and high-dimensional feature spaces.
method Sparse Isotonic Shapley Regression (SISR) learns a monotonic transformation to restore additivity and enforces L0 sparsity.
result SISR achieves strong support recovery and stable attributions across various payoff schemes.
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.
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.
Improved regret bounds for contextual combinatorial semi-bandits with linear payoffs.
problem Maximizing rewards in decision-making problems with feature vectors and constraints.
method Proposed C^2UCB algorithm and modified reward estimates for general constraints.
result Optimal regret bounds of C^2UCB algorithm and modified algorithm for various constraints.
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.
This paper develops a learning framework for optimal strategies in multi-stage decentralized matching markets.
problem Optimal strategies in multi-stage decentralized matching markets with uncertain preferences.
method Nonparametric statistical approach and variational analysis.
result Participants can be better off with multi-stage matching compared to single-stage matching.
Nonparametric pricing and hedging of exotic derivatives using signature payoffs.
problem Pricing and hedging exotic derivatives accurately and efficiently.
method Introducing signature payoffs and using them to approximate and price exotic derivatives nonparametrically.
result Signature payoffs enable accurate and computationally tractable pricing and hedging of exotic derivatives.
New algorithm adapts to unknown smoothness in contextual bandits.
problem Adapting to unknown smoothness in non-parametric multi-armed bandits.
method Develops a self-similarity condition-based policy to adapt to unknown smoothness.
result Matches known smoothness case's regret rate for differentiable and non-differentiable payoff functions.
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…
Individuals, or organizations, cooperate with or compete against one another in a wide range of practical situations. Such strategic interactions are often modeled as games played on networks, where an individual's payoff depends not only on her action but also on that of her neighbors. The current literature has large…
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.
Algorithm finds near-optimal strategy in changing zero-sum games.
problem Finding near-optimal strategy in changing zero-sum games.
method Designing an algorithm with small NE regret for online matrix games.
result Achieves near-optimal dependence on the number of rounds and number of actions.
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 ( d 1 2 T 1 1 + ε ) O(d^{\frac{1}{2}}T^{\frac{1}{1+ε}}) O ( d 2 1 T 1 + ε 1 ) for ε ∈ ( 0 , 1 ] ε\in(0,1] 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 2 i m e s 2 2 imes 2 2 im es 2 zero-sum games with noisy observations and commitments.
problem Analyzing 2 i m e s 2 2 imes 2 2 im es 2 zero-sum games with noisy observations and commitments. method Modeling a 2 i m e s 2 2 imes 2 2 im es 2 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.
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.
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 i i , the learner receives information about the payoff of every action j j 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 ) (i,j) ( i , j ) (and may…
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.
The study uses reproducing kernels to model bond discount curves.
problem Estimating bond discount curves under no-arbitrage conditions.
method Introduced reproducing kernels as a regression basis for estimating bond discount curves.
result Reproducing kernels provide a tractable solution for calibrating models to market data.
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.
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…
The portfolio optimization problem is a basic problem of financial analysis. In the study, an optimization model for constructing an options portfolio with a certain payoff function has been proposed. The model is formulated as an integer linear programming problem and includes an objective payoff function and a system…
A new algorithm improves top- k k k recommendation accuracy by considering item payoffs uncertainty.
problem Suboptimal performance in top- k k k recommendation rankings due to varying item payoffs. method Proposes a risk-seeking utility function for ranking items based on estimated preference scores.
result Risk-seeking ranking yields the best performance in top- k k k recommendations. The paper bounds payoffs and option prices in discrete models.
problem Measuring risk in discrete models and incomplete markets.
method Analytical and simulated bounds for payoff functions and option prices.
result Analytical and simulated bounds for European and American options.
Game theory model shows optimal investment strategy for wealth growth.
problem Minimizing time to reach large wealth in a stochastic asset market.
method Proved strategy of proportional asset investment minimizes expected time.
result Proportional investment strategy asymptotically minimizes time to large wealth.