no code implementations • 29 Apr 2024 • Yuyu Chen, Taizhong Hu, Ruodu Wang, Zhenfeng Zou
We study stochastic dominance between portfolios of independent and identically distributed (iid) extremely heavy-tailed (i. e., infinite-mean) Pareto random variables.
no code implementations • 27 Apr 2024 • Fabio Bellini, Tiantian Mao, Ruodu Wang, Qinyu Wu
We introduce a novel axiom of co-loss aversion for a preference relation over the space of acts, represented by measurable functions on a suitable measurable space.
no code implementations • 23 Apr 2024 • Liyuan Lin, Ruodu Wang, Ruixun Zhang, Chaoyi Zhao
We study the problem of choosing the copula when the marginal distributions of a random vector are not all continuous.
no code implementations • 22 Apr 2024 • Tobias Fissler, Fangda Liu, Ruodu Wang, Linxiao Wei
They are induced by law-based risk measures, called their generators, evaluated on the tail distribution.
no code implementations • 4 Apr 2024 • Muqiao Huang, Ruodu Wang
We introduce a technical tool called the folding score of distortion risk measures.
no code implementations • 24 Mar 2024 • Yuyu Chen, Paul Embrechts, Ruodu Wang
The phenomenon that diversification is not beneficial in the presence of super-Pareto losses is further illustrated by an equilibrium analysis in a risk exchange market.
no code implementations • 19 Mar 2024 • Christopher Chambers, Alan Miller, Ruodu Wang, Qinyu Wu
Max-stability is the property that taking a maximum between two inputs results in a maximum between two outputs.
no code implementations • 20 Feb 2024 • Yuanying Guan, Muqiao Huang, Ruodu Wang
We provide a new characterization of second-order stochastic dominance, also known as increasing concave order.
no code implementations • 30 Jan 2024 • Yi Shen, Zachary Van Oosten, Ruodu Wang
A notion of strong partial law invariance is introduced, allowing for a representation formula akin to the classical one.
no code implementations • 6 Jan 2024 • Jean-Gabriel Lauzier, Liyuan Lin, Ruodu Wang
We analyze the problem of optimally sharing risk using allocations that exhibit counter-monotonicity, the most extreme form of negative dependence.
no code implementations • 2 Dec 2023 • Xia Han, Ruodu Wang, Qinyu Wu
The form is a combination of the deviation-related functional and the expectation, and such measures belong to the class of consistent risk measures.
no code implementations • 13 Oct 2023 • Fabio Maccheroni, Massimo Marinacci, Ruodu Wang, Qinyu Wu
We then extend the analysis to comparative risk aversion by showing that the notion of Yaari (1969) corresponds to comparative propension to full insurance, while the stronger notion of Ross (1981) corresponds to comparative propension to partial insurance.
no code implementations • 25 Aug 2023 • Christopher P. Chambers, Peng Liu, Ruodu Wang
In this paper, we establish a mathematical duality between utility transforms and probability distortions.
no code implementations • 17 Jul 2023 • Giulio Principi, Peter P. Wakker, Ruodu Wang
We, finally, present cases where antimonotonic restrictions do weaken axioms and lead to new models, primarily for ambiguity aversion in nonexpected utility.
no code implementations • 12 Mar 2023 • Job Boerma, Aleh Tsyvinski, Ruodu Wang, Zhenyuan Zhang
We completely characterize optimal sorting and additionally show it is more positive when mismatch costs are less concave.
no code implementations • 22 Feb 2023 • Jean-Gabriel Lauzier, Liyuan Lin, Ruodu Wang
We systematically study pairwise counter-monotonicity, an extremal notion of negative dependence.
no code implementations • 8 Feb 2023 • Jean-Gabriel Lauzier, Liyuan Lin, Ruodu Wang
We address the problem of sharing risk among agents with preferences modelled by a general class of comonotonic additive and law-based functionals that need not be either monotone or convex.
no code implementations • 9 Jan 2023 • Xia Han, Liyuan Lin, Ruodu Wang
The diversification quotient (DQ) is recently introduced for quantifying the degree of diversification of a stochastic portfolio model.
no code implementations • 7 Sep 2022 • Ruodu Wang, Qinyu Wu
Quasi-convexity in probabilistic mixtures is a common and useful property in decision analysis.
no code implementations • 27 Aug 2022 • Qiuqi Wang, Ruodu Wang, Johanna Ziegel
In the recent Basel Accords, the Expected Shortfall (ES) replaces the Value-at-Risk (VaR) as the standard risk measure for market risk in the banking sector, making it the most important risk measure in financial regulation.
no code implementations • 17 Aug 2022 • Xia Han, Ruodu Wang, Xun Yu Zhou
We propose \emph{Choquet regularizers} to measure and manage the level of exploration for reinforcement learning (RL), and reformulate the continuous-time entropy-regularized RL problem of Wang et al. (2020, JMLR, 21(198)) in which we replace the differential entropy used for regularization with a Choquet regularizer.
no code implementations • 17 Aug 2022 • Yuyu Chen, Paul Embrechts, Ruodu Wang
We find the perhaps surprising inequality that the weighted average of independent and identically distributed Pareto random variables with infinite mean is larger than one such random variable in the sense of first-order stochastic dominance.
no code implementations • 16 Aug 2022 • Zhanyi Jiao, Steven Kou, Yang Liu, Ruodu Wang
We study an axiomatic framework for anonymized risk sharing.
no code implementations • 28 Jun 2022 • Xia Han, Liyuan Lin, Ruodu Wang
We establish the first axiomatic theory for diversification indices using six intuitive axioms: non-negativity, location invariance, scale invariance, rationality, normalization, and continuity.
no code implementations • 19 Apr 2022 • Hirbod Assa, Liyuan Lin, Ruodu Wang
It is straightforward to compute the value of PELVE for a given distribution model.
no code implementations • 4 Mar 2022 • Yuanying Guan, Zhanyi Jiao, Ruodu Wang
The celebrated Expected Shortfall (ES) optimization formula implies that ES at a fixed probability level is the minimum of a linear real function plus a scaled mean excess function.
no code implementations • 17 Jan 2022 • Tiantian Mao, Ruodu Wang, Qinyu Wu
The MA risk evaluation can be computed through explicit formulas in the lattice theory of stochastic dominance, and under some standard assumptions, the MA robust optimization admits a convex-program reformulation.
no code implementations • 10 Jan 2022 • Ruodu Wang, Zhenyuan Zhang
We propose a general framework of mass transport between vector-valued measures, which will be called simultaneous optimal transport (SOT).
no code implementations • 23 Oct 2021 • Xia Han, Qiuqi Wang, Ruodu Wang, Jianming Xia
In the literature of risk measures, cash subadditivity was proposed to replace cash additivity, motivated by the presence of stochastic or ambiguous interest rates and defaultable contingent claims.
no code implementations • 20 Oct 2021 • Tolulope Fadina, Yang Liu, Ruodu Wang
A risk analyst assesses potential financial losses based on multiple sources of information.
no code implementations • 1 Sep 2021 • Qiuqi Wang, Ruodu Wang, Ricardas Zitikis
To fill this gap, we study characterization of risk measures induced by efficient insurance contracts, i. e., those that are Pareto optimal for the insured and the insurer.
no code implementations • 11 Aug 2021 • Xia Han, Bin Wang, Ruodu Wang, Qinyu Wu
Recently, Wang and Zitikis (2021) put forward four economic axioms for portfolio risk assessment and provide the first economic axiomatic foundation for the family of ES.
1 code implementation • NeurIPS 2021 • Ziyu Xu, Ruodu Wang, Aaditya Ramdas
In bandit multiple hypothesis testing, each arm corresponds to a different null hypothesis that we wish to test, and the goal is to design adaptive algorithms that correctly identify large set of interesting arms (true discoveries), while only mistakenly identifying a few uninteresting ones (false discoveries).
no code implementations • 6 Jul 2021 • Xiaoqing Liang, Ruodu Wang, Virginia Young
We prove necessary and sufficient conditions satisfied by the optimal solution and consider three ambiguity orders to further determine the optimal indemnity.
no code implementations • 15 Apr 2021 • Yuyu Chen, Liyuan Lin, Ruodu Wang
We study the aggregation of two risks when the marginal distributions are known and the dependence structure is unknown, under the additional constraint that one risk is smaller than or equal to the other.
no code implementations • 29 Mar 2021 • Erio Castagnoli, Giacomo Cattelan, Fabio Maccheroni, Claudio Tebaldi, Ruodu Wang
In this paper monetary risk measures that are positively superhomogeneous, called star-shaped risk measures, are characterized and their properties studied.
no code implementations • 9 Dec 2020 • Fabio Bellini, Tolulope Fadina, Ruodu Wang, Yunran Wei
We present a general framework for a comparative theory of variability measures, with a particular focus on the recently introduced one-parameter families of inter-Expected Shortfall differences and inter-expectile differences, that are explored in detail and compared with the widely known and applied inter-quantile differences.
no code implementations • 24 Jul 2020 • Yuyu Chen, Peng Liu, Yang Liu, Ruodu Wang
Aggregation sets, which represent model uncertainty due to unknown dependence, are an important object in the study of robust risk aggregation.
no code implementations • 18 Jul 2020 • Jose Blanchet, Henry Lam, Yang Liu, Ruodu Wang
We discuss relevant applications in risk management and economics.
no code implementations • 17 Jul 2020 • Matteo Burzoni, Cosimo Munari, Ruodu Wang
We introduce and study the main properties of a class of convex risk measures that refine Expected Shortfall by simultaneously controlling the expected losses associated with different portions of the tail distribution.
no code implementations • 22 Aug 2018 • Ruodu Wang, Johanna F. Ziegel
Risk measures such as Expected Shortfall (ES) and Value-at-Risk (VaR) have been prominent in banking regulation and financial risk management.
1 code implementation • 20 Dec 2012 • Vladimir Vovk, Ruodu Wang
An old result by R\"uschendorf and, independently, Meng implies that the p-values can be combined by scaling up their arithmetic mean by a factor of 2 (and no smaller factor is sufficient in general).
Statistics Theory Statistics Theory 62G10, 62F03