A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Develops a statistical framework for coherent risk estimation.
Study asymptotic properties of generalized shortfall risk measures for heavy-tailed risks.
New concept of partial law invariance connects decision theory and financial risk management.
The purpose of this paper is to give a selective survey on recent progress in random metric theory and its applications to conditional risk measures. This paper includes eight sections. Section 1 is a longer introduction, which gives a brief introduction to random metric theory, risk measures and conditional risk measu…
The paper mentioned in the title introduces the entropic value at risk. I give some extra comments and using the general theory make a relation with some commonotone risk measures.
Paper introduces a new method for allocating capital based on risk measures from ruin theory.
The aim of this paper is to provide several examples of convex risk measures necessary for the application of the general framework for portfolio theory of Maier-Paape and Zhu, presented in Part I of this series (arXiv:1710.04579 [q-fin.PM]). As alternative to classical portfolio risk measures such as the standard devi…
This paper gives an overview of the theory of dynamic convex risk measures for random variables in discrete time setting. We summarize robust representation results of conditional convex risk measures, and we characterize various time consistency properties of dynamic risk measures in terms of acceptance sets, penalty …
Paper examines risk measure expansions under FGM dependence, improving accuracy at extreme levels.
New risk measures for financial and ESG risks using utility functions.
Paper studies convex risk measures linked to optimization.
This paper was presented and written for two seminars: a national UK University Risk Conference and a Risk Management industry workshop. The target audience is therefore a cross section of Academics and industry professionals. The current ongoing global credit crunch has highlighted the importance of risk measurement i…
The paper examines expectile quadrangle properties in risk management.
Different approaches to defining dynamic market risk measures are available in the literature. Most are focused or derived from probability theory, economic behavior or dynamic programming. Here, we propose an approach to define and implement dynamic market risk measures based on recursion and state economy representat…
Surveying risk measures for handling uncertainty in various fields.
Network theory assesses systemic risk in the insurance sector.
A Nash game theory approach allocates capital requirements among financial institutions.
Risk is an inherent feature of agricultural production and marketing and accurate measurement of it helps inform more efficient use of resources. This paper examines three tail quantile-based risk measures applied to the estimation of extreme agricultural financial risk for corn and soybean production in the US: Value …
Overview of risk-sensitive Markov decision processes with Optimized Certainty Equivalent.
New PAC-Bayes bounds derived using Legendre transform and f-divergences.
The paper develops a new approach to conditional risk measures using modular convex analysis.
Extends inf-convolution to countable risk measures for risk sharing.
The risk of a financial position is usually summarized by a risk measure. As this risk measure has to be estimated from historical data, it is important to be able to verify and compare competing estimation procedures. In statistical decision theory, risk measures for which such verification and comparison is possible,…
Utility and risk are two often competing measurements on the investment success. We show that efficient trade-off between these two measurements for investment portfolios happens, in general, on a convex curve in the two dimensional space of utility and risk. This is a rather general pattern. The modern portfolio theor…
We study combinations of risk measures under no restrictive assumption on the set of alternatives. We develop and discuss results regarding the preservation of properties and acceptance sets for the combinations of risk measures. One of the main results is the representation of resulting risk measures from the properti…
Value-at-Risk is a flawed substitute for non-ruin capital, leading to misleading financial standards.
A new framework for robust risk measurement and portfolio optimization.
Paper characterizes monotonic mean-deviation risk measures.
New risk measures control subgroup imbalances, improving PAC-Bayesian bounds.
Paper improves risk estimation for extreme events.
In this paper we introduce a new coherent cumulative risk measure on , the space of càdlàg processes having Laplace transform. This new coherent risk measure turns out to be tractable enough within a class of models where the aggregate claims is driven by a spectrally positive Lévy process. Moreover, w…
Using elements from the theory of ergodic backward stochastic differential equations (BSDE), we study the behavior of forward entropic risk measures. We provide their general representation results (via both BSDE and convex duality) and examine their behavior for risk positions of long maturities. We show that forward …
Generalizes risk sharing models to a continuum of agents.
The objective in a traditional reinforcement learning (RL) problem is to find a policy that optimizes the expected value of a performance metric such as the infinite-horizon cumulative discounted or long-run average cost/reward. In practice, optimizing the expected value alone may not be satisfactory, in that it may be…
Develops a new risk measure for Markov chains' asymptotic behavior.
Paper proposes a new method to evaluate joint risk under uncertainty.
Risk measures such as Expected Shortfall (ES) and Value-at-Risk (VaR) have been prominent in banking regulation and financial risk management. Motivated by practical considerations in the assessment and management of risks, including tractability, scenario relevance and robustness, we consider theoretical properties of…
Motivated by liquidity risk in mathematical finance, D. Lacker introduced concentration inequalities for risk measures, i.e. upper bounds on the \emph{liquidity risk profile} of a financial loss. We derive these inequalities in the case of time-consistent dynamic risk measures when the filtration is assumed to carry a …
In this paper we study time-consistent risk measures for returns that are given by a GARCH(1,1) model. We present a construction of risk measures based on their static counterparts that overcomes the lack of time-consistency. We then study in detail our construction for the risk measures Value-at-Risk (VaR) and Average…
Paper uses stochastic algorithms to estimate systemic risk measures.
The paper introduces risk consistency properties for credit ratings.
Establishes a link between risk measures and uniform integrability in finance.
The paper shows vector-valued risk measures ignore dependence structures.
Enhances financial risk quantification in classical models.
Develops risk measures for markets with constraints and costs.
Distortion risk measures are extensively used in finance and insurance applications because of their appealing properties. We present three methods to construct new class of distortion functions and measures. The approach involves the composting methods, the mixing methods and the approach that based on the theory of c…
Understanding and measuring model risk is important to financial practitioners. However, there lacks a non-parametric approach to model risk quantification in a dynamic setting and with path-dependent losses. We propose a complete theory generalizing the relative-entropic approach by Glasserman and Xu to the dynamic ca…
Risk assessment under different possible scenarios is a source of uncertainty that may lead to concerning financial losses. We address this issue, first, by adapting a robust framework to the class of spectral risk measures. Second, we propose a Deviation-based approach to quantify uncertainty. Furthermore, the theory …