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In this paper, we study general monetary risk measures (without any convexity or weak convexity). A monetary (respectively, positively homogeneous) risk measure can be characterized as the lower envelope of a family of convex (respectively,…

Mathematical Finance · Quantitative Finance 2020-12-15 Guangyan Jia , Jianming Xia , Rongjie Zhao

For an integer $n$ and the parameter $\gamma\in(0,n)$, the Riesz potential $I_\gamma$ is known to take boundedly $L^1(\mathbb{R}^n)$ into $L^{\frac{n}{n-\gamma},\infty}(\mathbb{R}^n)$, and also that the target space is the smallest possible…

Functional Analysis · Mathematics 2025-10-02 Zdeněk Mihula , Luboš Pick , Armin Schikorra

Law-invariant functionals are central to risk management and assign identical values to random prospects sharing the same distribution under an atomless reference probability measure. This measure is typically assumed fixed. Here, we adopt…

Risk Management · Quantitative Finance 2026-02-10 Felix-Benedikt Liebrich , Ruodu Wang

Monitoring means to observe a system for any changes which may occur over time, using a monitor or measuring device of some sort. In this paper we formulate a problem of monitoring dates of maximal risk of a financial position. Thus, the…

Risk Management · Quantitative Finance 2009-02-17 Erick Trevino Aguilar

Consider the problem of learning a large number of response functions simultaneously based on the same input variables. The training data consist of a single independent random sample of the input variables drawn from a common distribution…

Machine Learning · Statistics 2021-11-30 Vincent Plassier , François Portier , Johan Segers

We establish a weighted $L^p$ norm estimate for the Bergman projection for a class of pseudoconvex domains. We obtain an upper bound for the weighted $L^p$ norm when the domain is, for example, a bounded smooth strictly pseudoconvex domain,…

Complex Variables · Mathematics 2023-10-18 Zhenghui Huo , Nathan A. Wagner , Brett D. Wick

Spectral risk measures (SRMs) are risk measures that take account of user riskaversion, but to date there has been little guidance on the choice of utility function underlying them. This paper addresses this issue by examining alternative…

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

Starting from the requirement that risk measures of financial portfolios should be based on their losses, not their gains, we define the notion of loss-based risk measure and study the properties of this class of risk measures. We…

Risk Management · Quantitative Finance 2014-03-26 Rama Cont , Romain Deguest , Xuedong He

The paper concerns the investigation of nonconvex and nondifferentiable integral functionals on general Banach spaces, which may not be reflexive and/or separable. Considering two major subdifferentials of variational analysis, we derive…

Optimization and Control · Mathematics 2016-03-28 Boris S. Mordukhovich , Nobusumi Sagara

We identify a large class of Orlicz spaces $X$ for which the topology $\sigma(X,X_n^\sim)$ fails the C-property introduced in [7]. We also establish a variant of the C-property and use it to prove a $w^*$-representation theorem for proper…

Mathematical Finance · Quantitative Finance 2016-09-22 Niushan Gao , Foivos Xanthos

We propose a distributional framework for benchmarking socio-technical risks of foundation models with quantified statistical significance. Our approach hinges on a new statistical relative testing based on first and second order stochastic…

We propose a risk measurement approach for a risk-averse stochastic problem. We provide results that guarantee that our problem has a solution. We characterize and explore the properties of the argmin as a risk measure and the minimum as a…

Risk Management · Quantitative Finance 2023-05-09 Marcelo Brutti Righi , Fernanda Maria Müller , Marlon Ruoso Moresco

We introduce a universal framework for mean-covariance robust risk measurement and portfolio optimization. We model uncertainty in terms of the Gelbrich distance on the mean-covariance space, along with prior structural information about…

Portfolio Management · Quantitative Finance 2025-10-02 Viet Anh Nguyen , Soroosh Shafiee , Damir Filipović , Daniel Kuhn

Let $\msp$ be a measure space and let $1 < p < \infty$. The {\em weak $L^p$}\/ space $\wlp$ consists of all measurable functions $f$ such that \[ \|f\| = \sup_{t>0}t^{\frac{1}{p}}f^*(t) < \infty,\] where $f^*$ is the decreasing…

Functional Analysis · Mathematics 2009-09-25 Denny H. Leung

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

The new class of Banach spaces, so-called asymptotic $l_p$ spaces, is introduced and it is shown that every Banach space with bounded distortions contains a subspace from this class. The proof is based on an investigation of certain…

Functional Analysis · Mathematics 2009-09-25 Vitali D. Milman , Nicole Tomczak-Jaegermann

Let $\Omega\subset \mathbb{C}$ be an arbitrary domain in the one-dimensional complex plane equipped with a positive Radon measure $\mu$. For any $1\le p< \infty$, it is shown that the weighted Bergman space $A^p(\Omega, \mu)$ of holomorphic…

Functional Analysis · Mathematics 2021-11-16 Yong Han , Yanqi Qiu , Zipeng Wang

Understanding the complemented subspaces of $L_p$ has been an interesting topic of research in Banach space theory since 1960. 1999, Alspach proposed a systematic approach to classifying the subspaces of $L_p$ by introducing a norm given by…

Functional Analysis · Mathematics 2015-03-17 Isaac DeFrain , Mitch Phillipson , Simei Tong

Uncertainty is prevalent in engineering design, data-driven problems, and decision making broadly. Due to inherent risk-averseness and ambiguity about assumptions, it is common to address uncertainty by formulating and solving conservative…

Optimization and Control · Mathematics 2024-04-05 Johannes O. Royset