New model approximates sparse mean-CVaR portfolio optimization efficiently.
problem NP-hard ℓ0-constrained mean-CVaR optimization. method Proximal alternating linearized minimization algorithm with nested fixed-point proximity.
result The model offers a guaranteed approximation of the ℓ0-constrained mean-CVaR model. SAA method solves insurance portfolio optimization with CVaR constraints.
problem Optimal allocation under CVaR constraint in insurance.
method Sample Average Approximation (SAA) method applied to CVaR constrained portfolio optimization.
result Convergence of SAA method and solution uniqueness proved under mild assumptions.
We study a risk-constrained version of the stochastic shortest path (SSP) problem, where the risk measure considered is Conditional Value-at-Risk (CVaR). We propose two algorithms that obtain a locally risk-optimal policy by employing four tools: stochastic approximation, mini batches, policy gradients and importance s…
New method approximates CVaR with less data for heavy-tailed risks.
problem Lack of data for accurate CVaR approximation in heavy-tailed distributions.
method Importance sampling based extrapolation for heavy-tailed distributions.
result Statistically consistent approximations with reduced data requirements.
Paper proposes an algorithm to optimize CVaR using retrospective approximation and importance sampling.
problem Optimizing risk-averse problems with large sample requirements for CVaR.
method Retrospective approximation combined with importance sampling, tailored for CVaR optimization.
result The proposed algorithm reduces variance efficiently and is computationally efficient.
New RL algorithm maximizes CVaR in low-rank MDPs with provable efficiency.
problem Maximizing CVaR in large state spaces with function approximation.
method Upper Confidence Bound (UCB) bonus-driven algorithm for low-rank MDPs.
result Achieves sample complexity of O(H^7 A^2 d^4 / τ^2 ε^2) for ε-optimal CVaR.
Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) are two risk measures which are widely used in the practice of risk management. This paper deals with the problem of computing both VaR and CVaR using stochastic approximation (with decreasing steps): we propose a first Robbins-Monro procedure based on Rockaffela…
Improved portfolio optimization using VaR and CVaR with NMVM models.
problem Optimizing portfolios with VaR and CVaR under NMVM distributions.
method Transformed mean-CVaR-skewness problems into quadratic optimization with closed-form solutions for NMVM models.
result Approximate closed-form expressions for VaR and CVaR of NMVM portfolios.
Privacy affects how much data is needed for CVaR optimization.
problem Privacy constraints impact the effective sample size for CVaR optimization.
method Analyzes the privacy-relevant sample size and decomposes CVaR excess risk.
result The effective private tail sample size is εnτ, affecting CVaR learning rates.
New method corrects bias in CVaR estimation for extreme risks.
problem Limited data above VaR leads to poor CVaR estimation.
method Bias-corrected peaks-over-threshold (POT) estimation using GPD.
result Asymptotically unbiased CVaR estimator with lower threshold.
New approach uses SGLD to minimize CVaR for portfolio weights.
problem Minimizing CVaR for portfolio weights with complete theoretical guarantees.
method Stochastic Gradient Langevin Dynamics (SGLD) with discontinuous updating.
result Theoretical guarantees for convergence in Wasserstein distances for convex and non-convex functions.
Optimizes dynamic investment portfolios with correlated jumps.
problem Maximizing expected terminal wealth in a multivariate Merton model with dependent jumps.
method Approximating CVaR with comonotonic bounds and maximizing expected terminal wealth.
result Improved optimization of dynamic investment portfolios.
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.
Quantum algorithms for CVaR portfolio optimization face trade-offs between hardware coherence and expressibility.
problem Quantum algorithmic resilience for CVaR portfolio optimization
method WS-QAOA vs. HE-VQNN
result WS-QAOA provides exact theoretical mapping but suffers from hardware decoherence, while HE-VQNN preserves hardware coherence but lacks expressibility.
Proposes RM-CVaR for better portfolio optimization using multiple β-CVaR.
problem Optimizing portfolios with CVaR risk measure and selecting β.
method Regularized Multiple β-CVaR approach.
result Demonstrates superior performance in risk-adjusted returns and maximum drawdown.
New method for optimizing risk in financial models using Fourier transforms.
problem Optimizing risk in financial models with multi-period mean-CVaR.
method Strictly monotone 2D integration scheme via Fourier-trained transition kernels.
result Established robust and accurate optimization method for financial models.
An efficient adaptive direct numerical integration (DNI) algorithm is developed for computing high quantiles and conditional Value at Risk (CVaR) of compound distributions using characteristic functions. A key innovation of the numerical scheme is an effective tail integration approximation that reduces the truncation …
Conditional Value at Risk (CVaR) is a prominent risk measure that is being used extensively in various domains. We develop a new formula for the gradient of the CVaR in the form of a conditional expectation. Based on this formula, we propose a novel sampling-based estimator for the CVaR gradient, in the spirit of the l…
Study enhances robustness of In-CVaR based regression models under perturbation and contamination.
problem Enhancing robustness of nonlinear regression models under perturbation and contamination.
method Introduces interval conditional value-at-risk (In-CVaR) and rigorously analyzes its robustness properties under both perturbation and contamination.
result The In-CVaR based estimator is qualitatively robust in terms of the Prokhorov metric if and only if the largest portion of losses is trimmed.
We consider continuous-time stochastic optimal control problems featuring Conditional Value-at-Risk (CVaR) in the objective. The major difficulty in these problems arises from time-inconsistency, which prevents us from directly using dynamic programming. To resolve this challenge, we convert to an equivalent bilevel op…
Conditional Value-at-Risk (CVaR) is a widely used risk metric in applications such as finance. We derive concentration bounds for CVaR estimates, considering separately the cases of light-tailed and heavy-tailed distributions. In the light-tailed case, we use a classical CVaR estimator based on the empirical distributi…
New MKABSDEs help calculate initial margins in financial contracts.
problem Calculating initial margins in financial contracts with dependencies.
method Introduced MKABSDEs, provided existence and uniqueness, applied to CVaR, used deterministic and Monte-Carlo methods for numerical approximations.
result MKABSDEs provide a new way to solve for initial margins in financial contracts.
New learning algorithm for heavy-tailed data using CVaR.
problem Learning with potentially heavy-tailed losses.
method Estimator of CVaR for heavy-tailed data, robust learning algorithm.
result High-probability excess CVaR bounds and empirical tests.
Boosted CVaR Classification improves tail performance in classification tasks.
problem Maximizing tail performance in classification tasks.
method Proposed Boosted CVaR Classification framework using randomized classifiers and LPBoost algorithm.
result Minimizing CVaR loss over randomized classifiers leads to better tail performance.
We consider a statistical model for pairs of traded assets, based on a Cointegrated Vector Auto Regression (CVAR) Model. We extend standard CVAR models to incorporate estimation of model parameters in the presence of price series level shifts which are not accurately modeled in the standard Gaussian error correction mo…
Paper proposes CVaR-TS for risk-constrained MAB problems.
problem Risk in decision-making complicates reward maximization in MAB problems.
method Risk measure CVaR is used, and Thompson Sampling is adapted for CVaR.
result CVaR-TS outperforms other L/UCB-based algorithms in risk-constrained MAB settings.
Proposes a Doubly Robust mean-CVaR portfolio method to improve investment risk management.
problem Portfolio optimization challenges in unstable financial markets.
method Doubly Robust approach to mean-CVaR portfolio optimization.
result The proposed method outperforms traditional mean-variance optimization.
This paper provides a PAC-Bayesian bound for CVaR in machine learning.
problem Learning algorithms minimizing CVaR of empirical loss.
method Generalization bound of PAC-Bayesian type, reducing CVaR estimation to expectation estimation.
result The bound is small when empirical CVaR is small, providing concentration inequalities for CVaR.
This thesis presents the Conditional Value-at-Risk concept and combines an analysis that covers its application as a risk measure and as a vector norm. For both areas of application the theory is revised in detail and examples are given to show how to apply the concept in practice. In the first part, CVaR as a risk mea…
Integrates ESG data into Black-Litterman for portfolio optimization.
problem Optimizing portfolios with ESG considerations.
method Black-Litterman framework with Stein shrinkage for ESG bias, multivariate affine normal-inverse Gaussian model, CVaR risk measure, daily reallocation.
result Successful portfolio optimization with returns of 40-45% annually.
We introduce performance-based regularization (PBR), a new approach to addressing estimation risk in data-driven optimization, to mean-CVaR portfolio optimization. We assume the available log-return data is iid, and detail the approach for two cases: nonparametric and parametric (the log-return distribution belongs in …
We propose a risk-averse statistical learning framework wherein the performance of a learning algorithm is evaluated by the conditional value-at-risk (CVaR) of losses rather than the expected loss. We devise algorithms based on stochastic gradient descent for this framework. While existing studies of CVaR optimization …
Study a continuous portfolio optimization with a new CVaR-like constraint using martingale approach.
problem Optimizing a portfolio under a new CVaR-like constraint that is not compatible with traditional methods.
method Follows a martingale approach in a complete market setting, solving a convex constrained minimization problem.
result Obtains a tractable and interpretable characterization of the optimal strategy.
This work analyzes CVaR under heavy-tailed data, providing generalization and robustness bounds.
problem Understanding CVaR's behavior under heavy-tailed data and rare high-impact losses.
method Learning-theoretic analysis of CVaR-based empirical risk minimization.
result Sharp, high-probability generalization and excess risk bounds under minimal moment assumptions.
This paper develops a matrix-variate adaptive Markov chain Monte Carlo (MCMC) methodology for Bayesian Cointegrated Vector Auto Regressions (CVAR). We replace the popular approach to sampling Bayesian CVAR models, involving griddy Gibbs, with an automated efficient alternative, based on the Adaptive Metropolis algorith…
RL-CVaR model improves insurance reserving under economic stress.
problem Managing insurance reserve setting under claim development uncertainty and macroeconomic stress.
method Reinforcement Learning (PPO) with CVaR constraints, trained under regime-aware curriculum.
result RL-CVaR policy reduces solvency violations and tail-risk compared to classical methods.
Quantum method improves CVaR evaluation under correlated fields.
problem Accurately evaluating CVaR in high-dimensional, correlated material uncertainty.
method Quantum-enhanced inference framework using stabilized IQAE.
result Quantum method achieves lower oracle complexity than classical methods.
Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) are popular risk measures from academic, industrial and regulatory perspectives. The problem of minimizing CVaR is theoretically known to be of Neyman-Pearson type binary solution. We add a constraint on expected return to investigate the Mean-CVaR portfolio sele…
Bayesian approach improves portfolio optimization using VaR and CVaR.
problem Optimizing portfolio weights using VaR and CVaR for risk management.
method Bayesian perspective, posterior predictive distribution, observed data.
result Bayesian approach yields more accurate optimal portfolio weights.
We solve robust optimization problems using Wasserstein balls and apply it to mean-CVaR optimization.
problem Distributionally robust optimization with Wasserstein ambiguity sets.
method Transformed robust optimization into non-robust with penalty term, selecting ambiguity set size.
result Impressive results in robust mean-CVaR optimization compared to other strategies.
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…
Improved deep hedging with ensemble uncertainty quantification.
problem Uncertainty in deep hedging models hinders their deployment.
method Trained an ensemble of LSTM networks to quantify uncertainty in deep hedging under Heston volatility and proportional transaction costs.
result The ensemble's disagreement provides a strong predictive confidence measure for hedge performance.
ACFS optimizes spectral risk under decision-dependent uncertainty using adaptive forest sampling.
problem Minimizing spectral risk with decision-dependent uncertainty.
method ACFS integrates Generalised Random Forests, CEM-guided exploration, rank-weighted augmentation, and multi-start refinement.
result ACFS achieves lowest median oracle spectral risk on both benchmarks.
Improved portfolio optimization using GAM factor models.
problem Enhancing CVaR portfolio optimization performance.
method Combines autoregressive filters with factor regressions to predict stock returns.
result Substantial improvement in portfolio performances with GAM models.
A new CVaR test reduces group performance disparity detection complexity.
problem Detecting performance disparities across multiple sensitive groups in ML models.
method Conditional Value-at-Risk (CVaR) testing to reduce sample complexity.
result Sample complexity reduced exponentially to be at most the square root of the number of groups.
Tail-Safe hedging uses reinforcement learning with a safety layer to manage financial risks.
problem Managing financial risks in derivatives trading with robustness and explainability.
method Combines distributional reinforcement learning with a CBF-QP safety layer to enforce financial constraints.
result Improves risk management without degrading central performance and avoids hard constraint violations.
This paper develops a CVaR framework for managing tail risks using puts and trend-following strategies.
problem Managing tail risks, especially crashes and drawdowns, requires different forms of protection.
method Develops a continuous-time CVaR framework that integrates long out-of-the-money put options and systematic trend-following overlays.
result Shows how convex crash protection and drawdown protection can be optimally combined in a mandate.
Instead of controlling "symmetric" risks measured by central moments of investment return or terminal wealth, more and more portfolio models have shifted their focus to manage "asymmetric" downside risks that the investment return is below certain threshold. Among the existing downside risk measures, the lower-partial …