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Related papers: Risk measures based on target risk profiles

200 papers

Optimization of conditional convex risk measure is a central theme in dynamic portfolio selection theory, which has not yet systematically studied in the previous literature perhaps since conditional convex risk measures are neither random…

Optimization and Control · Mathematics 2019-10-24 Tiexin Guo

We study issues of robustness in the context of Quantitative Risk Management and Optimization. We develop a general methodology for determining whether a given risk measurement related optimization problem is robust, which we call…

Risk Management · Quantitative Finance 2021-02-12 Paul Embrechts , Alexander Schied , Ruodu Wang

Following several episodes of financial market turmoil in recent decades, changes in systemic risk have drawn growing attention. Therefore, we propose surveillance schemes for systemic risk, which allow to detect misspecified systemic risk…

Econometrics · Economics 2026-01-14 Timo Dimitriadis , Yannick Hoga

We study the optimal trade-off between expectation and tail risk for regret distribution in the stochastic multi-armed bandit model. We fully characterize the interplay among three desired properties for policy design: worst-case…

Machine Learning · Statistics 2025-10-27 David Simchi-Levi , Zeyu Zheng , Feng Zhu

Credit capital requirements in Internal Rating Based approaches require the calibration of two key parameters: the probability of default and the loss-given-default. This letter considers the uncertainty about these two parameters and…

Statistical Finance · Quantitative Finance 2020-10-19 Roberto Baviera

In this paper, we investigate risk minimization problem of derivatives based on non-tradable underlyings by means of dynamic g-expectations which are slight different from conditional g-expectations. In this framework, inspired by [1] and…

Portfolio Management · Quantitative Finance 2012-08-13 Tianxiao Wang

We study capital requirements for bounded financial positions defined as the minimum amount of capital to invest in a chosen eligible asset targeting a pre-specified acceptability test. We allow for general acceptance sets and general…

Risk Management · Quantitative Finance 2014-01-16 Walter Farkas , Pablo Koch-Medina , Cosimo Munari

Risk measures are commonly used to capture the risk preferences of decision-makers (DMs). The decisions of DMs can be nudged or manipulated when their risk preferences are influenced by factors such as the availability of information about…

Optimization and Control · Mathematics 2023-11-29 Shutian Liu , Quanyan Zhu

Numerical evaluation of performance measures in heavy-tailed risk models is an important and challenging problem. In this paper, we construct very accurate approximations of such performance measures that provide small absolute and relative…

Probability · Mathematics 2014-04-28 Eleni Vatamidou , Ivo J. B. F. Adan , Maria Vlasiou , Bert Zwart

Resource adequacy studies typically use standard metrics such as Loss of Load Expectation and Expected Energy Unserved to quantify the risk of supply shortfalls. This paper critiques present approaches to adequacy assessment and capacity…

Applications · Statistics 2023-09-13 Chris J. Dent , Nestor Sanchez , Aditi Shevni , Jim Q. Smith , Amy L. Wilson , Xuewen Yu

Expected Shortfall (ES) is the average return on a risky asset conditional on the return being below some quantile of its distribution, namely its Value-at-Risk (VaR). The Basel III Accord, which will be implemented in the years leading up…

Economics · Quantitative Finance 2017-07-18 Andrew J. Patton , Johanna F. Ziegel , Rui Chen

To find a trade-off between profitability and prudence, financial practitioners need to choose appropriate risk measures. Two key points are: Firstly, investors' risk attitudes under uncertainty conditions should be an important reference…

Risk Management · Quantitative Finance 2019-07-30 Wentao Hu

Expected shortfall is defined as the average over the tail below (or above) a certain quantile of a probability distribution. Expected shortfall regression provides powerful tools for learning the relationship between a response variable…

Methodology · Statistics 2025-01-03 Shushu Zhang , Xuming He , Kean Ming Tan , Wen-Xin Zhou

Portfolio optimization has long been dominated by covariance-based strategies, such as the Markowitz Mean-Variance framework. However, these approaches often fail to ensure a balanced risk structure across assets, leading to concentration…

Portfolio Management · Quantitative Finance 2025-08-07 Biswarup Chakraborty

We propose a novel risk matrix to characterize the optimal portfolio choice of an investor with tail concerns. The diagonal of the matrix contains the Value-at-Risk of each asset in the portfolio and the off-diagonal the pairwise…

Portfolio Management · Quantitative Finance 2021-12-23 Christis Katsouris

The utility-based shortfall risk (SR) measure introduced by Folmer and Schied [15] has been recently extended by Mao and Cai [29] to cumulative prospect theory (CPT) based SR in order to better capture a decision maker's utility/risk…

Optimization and Control · Mathematics 2021-12-21 Sainan Zhang , Huifu Xu

This paper proposes an innovative threshold measurement equation to be employed in a Realized-GARCH framework. The proposed framework incorporates a nonlinear threshold regression specification to consider the leverage effect and model the…

Risk Management · Quantitative Finance 2022-11-01 Chao Wang , Richard Gerlach

We present a probabilistic formulation of risk aware optimal control problems for stochastic differential equations. Risk awareness is in our framework captured by objective functions in which the risk neutral expectation is replaced by a…

Optimization and Control · Mathematics 2019-10-22 Jukka Isohätälä , William B. Haskell

Marginal expected shortfall (MES) is an important measure when assessing and quantifying the contribution of the financial institution to a systemic crisis. In this paper, we propose time-lagged marginal expected shortfall (TMES) as a…

Methodology · Statistics 2025-05-08 Jiajun Liu , Xuannan Liu , Yuwei Zhao

This paper investigates robust versions of the general empirical risk minimization algorithm, one of the core techniques underlying modern statistical methods. Success of the empirical risk minimization is based on the fact that for a…

Machine Learning · Statistics 2019-10-17 Stanislav Minsker , Timothée Mathieu