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In this paper, we study an optimal mean-variance investment-reinsurance problem for an insurer (she) under a Cram\'er-Lundberg model with random coefficients. At any time, the insurer can purchase reinsurance or acquire new business and…

Portfolio Management · Quantitative Finance 2024-06-18 Xiaomin Shi , Zuo Quan Xu

We study stochastic multi-armed bandits in which the objective is a statistical functional of the long-run reward distribution, rather than expected reward alone. Under mild continuity assumptions, we show that the infinite-horizon problem…

Machine Learning · Statistics 2026-04-30 Matías Carrasco , Alejandro Cholaquidis

This paper studies an optimal consumption-investment problem for an investor whose instantaneous utility depends on both consumption and wealth, and the investor faces a general borrowing constraint that the investment amount in the risky…

Portfolio Management · Quantitative Finance 2023-12-08 Weidong Tian , Zimu Zhu

We pursue an inverse approach to utility theory and consumption & investment problems. Instead of specifying an agent's utility function and deriving her actions, we assume we observe her actions (i.e. her consumption and investment…

Portfolio Management · Quantitative Finance 2015-03-17 Alexander M. G. Cox , David Hobson , Jan Obloj

We study the optimal solution to a general two-slope ski rental problem with a tail risk, i.e., the chance of the competitive ratio exceeding a value $\gamma$ is bounded by $\delta$. This extends the recent study of tail bounds for ski…

Data Structures and Algorithms · Computer Science 2025-08-14 Qiming Cui , Michael Dinitz

Basel II and Solvency 2 both use the Value-at-Risk (VaR) as the risk measure to compute the Capital Requirements. In practice, to calibrate the VaR, a normal approximation is often chosen for the unknown distribution of the yearly log…

Methodology · Statistics 2013-11-04 Marie Kratz

This paper extends the results of the article [C. Kl\"{u}ppelberg and S. M. Pergamenchtchikov. Optimal consumption and investment with bounded downside risk for power utility functions. In Optimality and Risk: {\it Modern Trends in…

Mathematical Finance · Quantitative Finance 2016-04-20 Thai Nguyen

This article focuses on the work of O. Chanel and G. Chichilnisky (2013) on the flaws of expected utility theory while assessing the value of life. Expected utility is a fundamental tool in decision theory. However, it does not fit with the…

General Finance · Quantitative Finance 2016-04-20 Julien Blasco , Graciela Chichilnisky

We study Spectral Measures of Risk from the perspective of portfolio optimization. We derive exact results which extend to general Spectral Measures M_phi the Pflug--Rockafellar--Uryasev methodology for the minimization of alpha--Expected…

Statistical Mechanics · Physics 2008-12-02 Acerbi Carlo , Simonetti Prospero

This study investigates the influence of risk tolerance on the expected utility in the long run. We estimate the extent to which the expected utility of optimal portfolios is affected by small changes in the risk tolerance. For this…

Mathematical Finance · Quantitative Finance 2021-04-05 Hyungbin Park

The contour maps of the error of historical resp. parametric estimates for large random portfolios optimized under the risk measure Expected Shortfall (ES) are constructed. Similar maps for the sensitivity of the portfolio weights to small…

Risk Management · Quantitative Finance 2015-10-19 Fabio Caccioli , Imre Kondor , Gábor Papp

The geology of oil reservoirs is largely unknown. Consequently, the reservoir models used for production optimization are subject to significant uncertainty. To minimize the associated risk, the oil literature has mainly used ensemble-based…

Optimization and Control · Mathematics 2018-01-03 Andrea Capolei , Lasse Hjuler Christiansen , John Bagterp Jørgensen

This paper applies the Extreme-Value (EV) Generalised Pareto distribution to the extreme tails of the return distributions for the S&P500, FT100, DAX, Hang Seng, and Nikkei225 futures contracts. It then uses tail estimators from these…

Risk Management · Quantitative Finance 2011-03-30 John Cotter , Kevin Dowd

We introduce the Value-at-Risk Constrained Policy Optimization algorithm (VaR-CPO), a sample efficient and conservative method designed to optimize Value-at-Risk (VaR) constrained reinforcement learning (RL) problems. Empirically, we…

Machine Learning · Computer Science 2026-05-01 Rohan Tangri , Jan-Peter Calliess

A method for quantile-based, semi-parametric historical simulation estimation of multiple step ahead Value-at-Risk (VaR) and Expected Shortfall (ES) models is developed. It uses the quantile loss function, analogous to how the…

Statistical Finance · Quantitative Finance 2025-03-06 Richard Gerlach , Antonio Naimoli , Giuseppe Storti

Conditional value-at-risk (CVaR) and value-at-risk (VaR) are popular tail-risk measures in finance and insurance industries as well as in highly reliable, safety-critical uncertain environments where often the underlying probability…

Machine Learning · Computer Science 2021-06-23 Shubhada Agrawal , Wouter M. Koolen , Sandeep Juneja

This article studies the sensitivity of the power utility maximization problem with respect to the investor's relative risk aversion, the statistical probability measure, the investment constraints and the market price of risk. We extend…

Optimization and Control · Mathematics 2011-07-04 Markus Mocha , Nicholas Westray

A model for decision making that generalizes Expected Utility Maximization is presented. This model, Expected Qualitative Utility Maximization, encompasses the Maximin criterion. It relaxes both the Independence and the Continuity…

Computer Science and Game Theory · Computer Science 2007-05-23 Daniel Lehmann

Value-at-risk (VaR) is an established measure to assess risks in critical real-world applications with random environmental factors. This paper presents a novel VaR upper confidence bound (V-UCB) algorithm for maximizing the VaR of a…

Machine Learning · Computer Science 2021-05-14 Quoc Phong Nguyen , Zhongxiang Dai , Bryan Kian Hsiang Low , Patrick Jaillet

We propose a new risk-constrained reformulation of the standard Linear Quadratic Regulator (LQR) problem. Our framework is motivated by the fact that the classical (risk-neutral) LQR controller, although optimal in expectation, might be…

Systems and Control · Electrical Eng. & Systems 2020-10-30 Anastasios Tsiamis , Dionysios S. Kalogerias , Luiz F. O. Chamon , Alejandro Ribeiro , George J. Pappas
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