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.
New method assesses financial and cyber risks under uncertainty.
Options are generally learned by using an inaccurate environment model (or simulator), which contains uncertain model parameters. While there are several methods to learn options that are robust against the uncertainty of model parameters, these methods only consider either the worst case or the average (ordinary) case…
Worst-case risk measures refer to the calculation of the largest value for risk measures when only partial information of the underlying distribution is available. For the popular risk measures such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR), it is now known that their worst-case counterparts can be ev…
Quantification of risk positions under model uncertainty is of crucial importance from both viewpoints of external regulation and internal management. The concept of model uncertainty, sometimes also referred to as model ambiguity. Although we know the family of models, we cannot precisely decide which one to use. Give…
Develops a new method for robust risk measurement by averaging nearby payoffs.
A new algorithm avoids worst-case outcomes in risky contexts.
Proposes a new framework for balancing average- and worst-case performance in machine learning.
The paper analyzes extreme risk measures with limited distributional information.
New algorithms optimize spectral risk measures, improving interpolation between average and worst-case performance.
A method for calculating multi-portfolio time consistent multivariate risk measures in discrete time is presented. Market models for assets with transaction costs or illiquidity and possible trading constraints are considered on a finite probability space. The set of capital requirements at each time and state is c…
We propose an approach to the aggregation of risks which is based on estimation of simple quantities (such as covariances) associated to a vector of dependent random variables, and which avoids the use of parametric families of copulae. Our main result demonstrates that the method leads to bounds on the worst case Valu…
The paper uses EVT to improve tail risk measures under ambiguity sets.
A framework identifies worst-case decision points in safety-critical scenarios, improving risk assessment by 10 hours.
We discuss the coherence properties of Expected Shortfall (ES) as a financial risk measure. This statistic arises in a natural way from the estimation of the "average of the 100p % worst losses" in a sample of returns to a portfolio. Here p is some fixed confidence level. We also compare several alternative representat…
Numerical challenges inherent in algorithms for computing worst Value-at-Risk in homogeneous portfolios are identified and solutions as well as words of warning concerning their implementation are provided. Furthermore, both conceptual and computational improvements to the Rearrangement Algorithm for approximating wors…
The problem of data uncertainty has motivated the incorporation of robust optimization in various arenas, beyond the Markowitz portfolio optimization. This work presents the extension of the robust optimization framework for the minimization of downside risk measures, such as Value-at-Risk (VaR) and Conditional Value-a…
This paper calculates worst-case target semi-variances for uncertain losses.
We augment adversarial training (AT) with worst case adversarial training (WCAT) which improves adversarial robustness by 11% over the current state-of-the-art result in the norm on CIFAR-10. We obtain verifiable average case and worst case robustness guarantees, based on the expected and maximum values of the…
New versions of the set-valued average value at risk for multivariate risks are introduced by generalizing the well-known certainty equivalent representation to the set-valued case. The first "regulator" version is independent from any market model whereas the second version, called the market extension, takes trading …
The paper studies the convergence of SAA for systemic risk measures.
Study risk aggregation with order constraint under unknown dependence.
Study optimal portfolio selection with Recovery Average Value at Risk, showing better control over liabilities.
Paper improves worst-case regret bounds for RLSVI in reinforcement learning.
Expected Shortfall (ES) in several variants has been proposed as remedy for the defi-ciencies of Value-at-Risk (VaR) which in general is not a coherent risk measure. In fact, most definitions of ES lead to the same results when applied to continuous loss distributions. Differences may appear when the underlying loss di…
VaR-CPO optimizes VaR-constrained RL problems with conservative policy updates.
New risk measure improves creditor protection in financial regulation.
Paper introduces Lambda EVaR, a new risk measure.
Sharp bounds for distortion risk metrics under uncertain distributions.
By treating the financial market as a thermodynamic system, we establish a one-to-one correspondence between thermodynamic variables and economic quantities. Measured by the expected loss under the worst-case scenario, financial risk caused by model uncertainty is regarded as a result of the interaction between financi…
Canary optimizes VaR-constrained RL problems with a conservative bound using Cantelli's inequality.
The paper optimizes reinsurance under uncertain dependence among insurers.
The paper assesses the risk of negative treatment effects using bounds and inference.
This paper analyzes risk-sensitive reinforcement learning with Conditional Value-at-Risk (CVaR) for robust Markov Decision Processes.
New approach for prudent risk evaluation using model aggregation.
Proposes a new probabilistic framework for domain generalization.
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…
The paper analyzes insurance contracts under distributional uncertainty using Bregman-Wasserstein divergence.
In this paper we propose a problem-driven scenario generation approach to the single-period portfolio selection problem which use tail risk measures such as conditional value-at-risk. Tail risk measures are useful for quantifying potential losses in worst cases. However, for scenario-based problems these are problemati…
Accounting for model uncertainty in risk management and option pricing leads to infinite dimensional optimization problems which are both analytically and numerically intractable. In this article we study when this hurdle can be overcome for the so-called optimized certainty equivalent risk measure (OCE) -- including t…
Online TERM improves robustness and fairness in streaming data.
Pairwise comparison data arises in many domains, including tournament rankings, web search, and preference elicitation. Given noisy comparisons of a fixed subset of pairs of items, we study the problem of estimating the underlying comparison probabilities under the assumption of strong stochastic transitivity (SST). We…
Dual representations for robust risk measures and uncertainty sets.
Study on proper learning under relaxed worst-case robust loss for VC classes.
New policy optimizes risk and optimality in stochastic bandits.
Paper investigates Lambda Value-at-Risk under ambiguity and risk sharing.
The equivalence between multiportfolio time consistency of a dynamic multivariate risk measure and a supermartingale property is proven. Furthermore, the dual variables under which this set-valued supermartingale is a martingale are characterized as the worst-case dual variables in the dual representation of the risk m…
Overparameterized neural networks can be highly accurate on average on an i.i.d. test set yet consistently fail on atypical groups of the data (e.g., by learning spurious correlations that hold on average but not in such groups). Distributionally robust optimization (DRO) allows us to learn models that instead minimize…
Several well-established benchmark predictors exist for Value-at-Risk (VaR), a major instrument for financial risk management. Hybrid methods combining AR-GARCH filtering with skewed- residuals and the extreme value theory-based approach are particularly recommended. This study introduces yet another VaR predictor, …