Modeling business cycles via collective risk fluctuations in economic agents' risk space.
problem Understanding and predicting business cycles through economic agents' risk dynamics.
method Continuous numerical risk grades for economic agents, modeling collective economic variables and flows as functions of risk coordinates, deriving equations for their evolution.
result Business and credit cycles are explained as fluctuations of collective economic variables and their mean risks in the risk space of economic agents.
Study shows cooperation can reduce investment risk and price gaps.
problem Investment risk and price gaps in cooperative markets.
method Introduced Collective Arbitrage and Collective Super-replication, established asset pricing theorems.
result Reduction of price intervals through collective super-replication.
Framework for systemic risk modeling using jointly exchangeable arrays.
problem Systemic risk in insurance portfolios with interactions.
method Jointly exchangeable arrays, central limit theorems, simulation-based validation.
result Asymptotic approximations for total portfolio losses in large portfolios over long time horizons.
Optimizes investment strategies for retirees with longevity risk.
problem Maximizing retirement savings under longevity risk for a group of investors.
method Analytic and numerical solutions for investment strategies in both discrete and continuous time models.
result Analytic formulae for optimal investment strategies in both discrete and continuous time models.
Study collective pricing and hedging with admissible risk exchanges forming a finitely generated convex cone.
problem Collective pricing and hedging with exchanges forming a finitely generated convex cone.
method Extend collective First Fundamental Theorem of Asset Pricing and pricing-hedging duality.
result No collective arbitrage implies the closedness of the aggregate feasibility cone.
New guarantees for ERM with adaptively collected data.
problem Failure of ERM guarantees with adaptively collected data.
method Importance sampling weighted ERM algorithm with maximal inequality.
result First generalization guarantees and fast convergence rates for adaptively collected data.
Study proposes a tax-based system to share disaster risk among regions.
problem Systemic risk in catastrophic events and insurer insolvency.
method Public-private partnership with government intervention through taxation.
result Taxation system effectively shares residual claims in case of insurer insolvency.
Study optimizes data collection from biased, costly sources to minimize risk.
problem Estimating population means and group-conditional means from multiple sources with varying costs and biases.
method Develops a sampling plan that maximizes effective sample size, paired with a post-stratification estimator.
result Achieves budgeted minimax optimal risk for estimating population means and group-conditional means.
The paper extends collective arbitrage concepts to multi-agent markets with cooperation.
problem Understanding collective market completeness and pricing in multi-agent systems.
method Develops new techniques and theorems to establish collective pricing-hedging duality and collective replication.
result Established a Second Fundamental Theorem of Asset Pricing in cooperative multi-agent settings.
Unified Bayesian framework for CAT bond pricing.
problem Uncertainty in catastrophe occurrences and interest rates in CAT bond markets.
method Bayesian framework based on uncertainty quantification of catastrophes and interest rates.
result Unified asset pricing approach with informative expected risk premia.
Set-valued risk measures on Ldp with 0≤p≤∞ for conical market models are defined, primal and dual representation results are given. The collection of initial endowments which allow to super-hedge a multivariate claim are shown to form the values of a set-valued sublinear (coherent) risk measure. Sc…
Research shows collective learning across diverse environments is hard due to privacy and security concerns.
problem Privacy, security, and equity concerns restrict information sharing in diverse AI environments.
method Characterized learning algorithms as choice correspondences, provided minimum requirements for rational learning algorithms.
result The only rational learning algorithm in heterogeneous environments is unilaterally learning from a single environment without information sharing.
Study analyzes gambling behavior and risk attitudes using blockchain data.
problem Lack of real-life gambling data for validating predictions and experimental findings.
method Collects and analyzes betting data from a decentralized application on the Ethereum Blockchain.
result Empirical examples of gambling systems and insights into risk preferences.
Companies try to maximize their profits by recovering returned products of highly uncertain quality and quantity. In this paper, a reverse logistics network for an Original Equipment Manufacturer (OEM) is presented. Returned products are selected for remanufacturing or scrapping, based on their quality and proportional…
Collectives can manipulate learning platforms by coordinated data submission, requiring strategic assessments and algorithms.
problem Collectives can influence learning platforms by altering data, posing risks and requiring strategic planning.
method Developed a theoretical and algorithmic framework to understand and mitigate collective manipulation of learning platforms.
result Demonstrated the need for strategic assessments and implementable coordination algorithms to prevent collective manipulation.
Two pension funds mutually insure against longevity risk.
problem Mutual insurance against systematic longevity risk for pension funds.
method Mathematical demonstration and market clearing condition.
result Insurance provides little benefit when fund preferences are similar, but can be beneficial when preferences vary significantly.
We propose a robust risk measurement approach that minimizes the expectation of overestimation plus underestimation costs. We consider uncertainty by taking the supremum over a collection of probability measures, relating our approach to dual sets in the representation of coherent risk measures. We provide results that…
We present a simple model of firm rating evolution. We consider two sources of defaults: individual dynamics of economic development and Potts-like interactions between firms. We show that such a defined model leads to phase transition, which results in collective defaults. The existence of the collective phase depends…
New risk-sharing rules induced by capital allocation principles.
problem Risk sharing in corporate structures.
method Randomizing existing capital allocation principles.
result Derives new risk-sharing rules complementing existing literature.
In this paper we introduce a new coherent cumulative risk measure on RLp, 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…
The study reveals traders' risk aversion and a new risk premium from market volumes.
problem Understanding traders' rationality and risk aversion from market volumes.
method Optimal Merton dynamics model to estimate average risk aversion and price of risk.
result Validation of the proposed trading strategy model on real data.
This paper focuses on stochastic orders and its applications : policy limits and deductibles. Further, many applications and some examples are given : comparison of two families of copulas, individual and collective risk model, reinsurance contracts and dependent portfolios increase risk. More precisely, we propose a n…
For controlled discrete-time stochastic processes we introduce a new class of dynamic risk measures, which we call process-based. Their main features are that they measure risk of processes that are functions of the history of a base process. We introduce a new concept of conditional stochastic time consistency and we …
Inherent risk scoring is an important function in anti-money laundering, used for determining the riskiness of an individual during onboarding before fraudulent transactions occur. It is, however, often fraught with two challenges: (1) inconsistent notions of what constitutes as high or low risk by experts a…
Workplace injuries result in substantial human and financial losses. As reported by the International Labour Organization (ILO), there are more than 374 million work-related injuries reported every year. In this study, we investigate the problem of injury risk prediction and prevention in a work environment. While inju…
Extends risk control to adaptive data collection, anytime-valid guarantees.
problem Ensuring safety of machine learning models with critical risk measures.
method Sequential risk controlling prediction sets (RCPS) for adaptive data collection and active labeling.
result Anytime-valid guarantees for risk control in sequential data collection.
Paper connects two portfolio methods, HRP and Minimum Variance, revealing their underlying similarity.
problem Inability to universally adopt optimization-based portfolio construction methods.
method Unifies Hierarchical Risk Parity and Minimum Variance approaches.
result Schur complementary allocation reveals the connection between HRP and Minimum Variance.
Predict real-time crash risks during hurricane evacuations using connected vehicle data.
problem Mitigate crash risks during hurricane evacuations by predicting high-risk locations.
method Used connected vehicle data to predict crash risks in real-time, considering weather and traffic features.
result Gaussian Process Boosting and Extreme Gradient Boosting models performed best, with recall of 0.91.
We show that the emergence of systemic risk in complex systems can be understood from the evolution of functional networks representing interactions inferred from fluctuation correlations between macroscopic observables. Specifically, we analyze the long-term collective dynamics of the New York Stock Exchange between 1…
Study shows small groups can influence machine learning algorithms.
problem How small groups can influence machine learning algorithms deployed on digital platforms.
method Proposed a theoretical model and conducted experiments on a large-scale language model.
result Small groups can exert significant control over machine learning algorithms.
RiskMiner discovers formulaic alphas using MCTS for better performance.
problem Mining formulaic alphas without considering structural information and alpha correlations.
method Formulates alpha mining as an MDP and solves it with a risk-seeking MCTS.
result Our method outperforms state-of-the-art benchmarks and achieves the most profitable results.
Model evaluates insurance risk using thermodynamic principles.
problem Risk of lapses due to adverse selection in insurance.
method Collective model with diffusion process influenced by statistical mechanics.
result Derives level premium to evaluate insurance risk.
In this paper we study the effect of network structure between agents and objects on measures for systemic risk. We model the influence of sharing large exogeneous losses to the financial or (re)insuance market by a bipartite graph. Using Pareto-tailed losses and multivariate regular variation we obtain asymptotic resu…
Federated learning for data streams tackles real-time learning from IoT devices.
problem Efficiently learn from data streams generated by IoT devices and smartphones.
method Proposes a general federated learning algorithm for data streams using weighted empirical risk minimization.
result Demonstrates improved performance on various machine learning tasks compared to static dataset approaches.
Develops a new risk measure for Markov chains' asymptotic behavior.
problem Lack of risk measures for asymptotic regimes of Markov chains.
method Simulation-based approach using large deviations theory, density estimation, and stochastic approximation.
result Developed Asymptotic CVaR (ACVaR) for Markov chains.
Risk Advisor predicts and mitigates ML deployment failures.
problem Predicting and mitigating test-time failure risks of ML systems.
method Post-hoc meta-learner for estimating failure risks and uncertainties.
result Reliably predicts deployment-time failure risks across various ML models.
Current economic theories miss most of economic dynamics.
problem Accuracy of economic theories and policies depend on economic variables and processes.
method Identify and analyze overlooked economic variables and processes.
result Many economic variables and processes not accounted for in current theories.
Optimizes risk-neutral probabilities for derivative pricing.
problem Deriving bounds on derivative values under multiple risk-neutral scenarios.
method Convex optimization over the set of risk-neutral probability distributions.
result Tractable finite-dimensional optimization problems for pricing.
Paper introduces risk assessment for contextual bandits without experiments.
problem Evaluate policies using logged data in context bandits.
method Lipschitz risk functionals and Off-Policy Risk Assessment (OPRA) framework.
result OPRA provides finite sample guarantees for various risk estimates.
The collection and analysis of user data drives improvements in the app and web ecosystems, but comes with risks to privacy. This paper examines discrete distribution estimation under local privacy, a setting wherein service providers can learn the distribution of a categorical statistic of interest without collecting …
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection an…
We propose a general approach for supervised learning with structured output spaces, such as combinatorial and polyhedral sets, that is based on minimizing estimated conditional risk functions. Given a loss function defined over pairs of output labels, we first estimate the conditional risk function by solving a (possi…
Dictionary learning is the problem of estimating the collection of atomic elements that provide a sparse representation of measured/collected signals or data. This paper finds fundamental limits on the sample complexity of estimating dictionaries for tensor data by proving a lower bound on the minimax risk. This lower …
MaxRM uses random forests to minimize maximum risk across different environments.
problem Designing methods that generalize better to test environments with different distributions.
method Introducing variants of random forests based on the principle of MaxRM (Maximum Risk Minimization).
result Proved statistical consistency for the proposed method and provided an out-of-sample guarantee for MaxRM with regret.
Study combines intra-risk and contagion risk for SME bankruptcy prediction.
problem Predicting bankruptcy risk of SMEs considering both intra-risk and contagion risk.
method Proposes a novel model using Graph Neural Networks to combine intra-risk and contagion risk.
result Model outperforms state-of-the-art methods in bankruptcy prediction.
Every time drivers take to the road, and with each mile that they drive, exposes themselves and others to the risk of an accident. Insurance premiums are only weakly linked to mileage, however, and have lump-sum characteristics largely. The result is too much driving, and too many accidents. In this paper, we introduce…
The emph{securities market} is the fundamental theoretical framework in economics and finance for resource allocation under uncertainty. Securities serve both to reallocate risk and to disseminate probabilistic information. emph{Complete} securities markets - which contain one security for every possible state of natur…
News novelty predicts negative stock market returns.
problem Negative stock market returns due to increased news novelty.
method Quantified news novelty using entropy measure from recurrent neural network applied to a large news corpus.
result Entropy exposure carries a negative risk premium, indicating that assets positively correlated with entropy hedge aggregate news risk.