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Using a bondholder who seeks to determine when to sell his bond as our motivating example, we revisit one of Larry Shepp's classical theorems on optimal stopping. We offer a novel proof of Theorem 1 from from \cite{Shepp}. Our approach is…

Mathematical Finance · Quantitative Finance 2016-05-04 Philip Ernst , Larry Shepp

The note studies the problem of selecting a good enough subset out of a finite number of alternatives under a fixed simulation budget. Our work aims to maximize the posterior probability of correctly selecting a good subset. We formulate…

Optimization and Control · Mathematics 2023-05-09 Gongbo Zhang , Bin Chen , Qing-shan Jia , Yijie Peng

We study a version of the classical Cayley-Moser optimal stopping problem, in which a seller must sell an asset by a given deadline, with the offers, which are independent random variables with a known distribution, arriving at random…

Probability · Mathematics 2025-11-05 Guy Katriel

We consider the problem of estimating the expected value of information (the knowledge gradient) for Bayesian learning problems where the belief model is nonlinear in the parameters. Our goal is to maximize some metric, while simultaneously…

Machine Learning · Statistics 2016-11-23 Xinyu He , Warren B. Powell

Estimation of individual treatment effects is commonly used as the basis for contextual decision making in fields such as healthcare, education, and economics. However, it is often sufficient for the decision maker to have estimates of…

Machine Learning · Computer Science 2020-08-13 Maggie Makar , Fredrik D. Johansson , John Guttag , David Sontag

After a brief review of option pricing theory, we introduce various methods proposed for extracting the statistical information implicit in options prices. We discuss the advantages and drawbacks of each method, the interpretation of their…

Condensed Matter · Physics 2007-05-23 Rama Cont

We consider a sequential decision-making problem where an agent can take one action at a time and each action has a stochastic temporal extent, i.e., a new action cannot be taken until the previous one is finished. Upon completion, the…

Machine Learning · Computer Science 2020-03-26 P Sharoff , Nishant A. Mehta , Ravi Ganti

Breakability rate of fragile item depends on the accumulated stress of heaped stock level. So breakablility rate can be considered as dependent parameter of stock variable. The unit production cost is a function of production rate and also…

Optimization and Control · Mathematics 2020-06-03 J. N. Roul , K. Maity , S. Kar , M. Maiti

The problem of stock hedging is reconsidered in this paper, where a put option is chosen from a set of available put options to hedge the market risk of a stock. A formula is proposed to determine the probability that the potential loss…

Risk Management · Quantitative Finance 2011-10-04 Guanghui Huang , Jing Xu , Wenting Xing

We analyze an optimal stopping problem with a series of inequality-type and equality-type expectation constraints in a general non-Markovian framework. We show that the optimal stopping problem with expectation constraints (OSEC) in an…

Optimization and Control · Mathematics 2023-02-10 Erhan Bayraktar , Song Yao

The aim of this short note is to present a solution to the discrete time exponential utility maximization problem in a case where the underlying asset has a multivariate normal distribution. In addition to the usual setting considered in…

Mathematical Finance · Quantitative Finance 2023-06-27 Yan Dolinsky , Or Zuk

A company provides a service at different time slots, each slot being endowed with a capacity. A non-atomic population of users is willing to purchase this service. The population is modeled as a continuous measure over the preferred times.…

Optimization and Control · Mathematics 2026-03-19 Olivier Bilenne , Frédéric Meunier

We consider the problem of sequential sampling from a finite number of independent statistical populations to maximize the expected infinite horizon average outcome per period, under a constraint that the expected average sampling cost does…

Machine Learning · Statistics 2012-01-20 Apostolos Burnetas , Odysseas Kanavetas

Stochastic optimization is one of the central problems in Machine Learning and Theoretical Computer Science. In the standard model, the algorithm is given a fixed distribution known in advance. In practice though, one may acquire at a cost…

Data Structures and Algorithms · Computer Science 2023-06-07 Mingchen Ma , Christos Tzamos

We study a single risky financial asset model subject to price impact and transaction cost over an finite time horizon. An investor needs to execute a long position in the asset affecting the price of the asset and possibly incurring in…

Trading and Market Microstructure · Quantitative Finance 2015-03-19 Mauricio Junca

We investigate the impact of capital gains taxes on optimal investment decisions in a quite simple model. Namely, we consider a risk neutral investor who owns one risky stock from which she assumes that it has a lower expected return than…

Portfolio Management · Quantitative Finance 2015-01-05 Christoph Kühn , Budhi Arta Surya , Björn Ulbricht

In this work we are concerned with the design of efficient mechanisms while eliciting limited information from the agents. First, we study the performance of sampling approximations in facility location games. Our key result is to show that…

Computer Science and Game Theory · Computer Science 2022-08-26 Ioannis Anagnostides , Dimitris Fotakis , Panagiotis Patsilinakos

We introduce an interactive market setup with sequential auctions where agents receive variegated signals with a known deadline. The effects of differential information and mutual learning on the allocation of overall profit \& loss (P\&L)…

Mathematical Finance · Quantitative Finance 2016-10-14 N. Serhan Aydin

Motivated by applications where impatience is pervasive and evaluation times are uncertain, we study a selection model where options may expire at an unknown point in time and evaluation times are stochastic. Initially, the decision-maker…

Optimization and Control · Mathematics 2026-02-05 Yihua Xu , Rohan Ghuge , Sebastian Perez-Salazar

Consider the classical problem of predicting the next bit in a sequence of bits. A standard performance measure is {\em regret} (loss in payoff) with respect to a set of experts. For example if we measure performance with respect to two…

Machine Learning · Computer Science 2013-04-30 Rina Panigrahy , Preyas Popat
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