Paper develops risk statistics for portfolios considering regulator-based risk.
problem Traditional risk statistics fail to describe regulator-based risk.
method Develop dual representation for regulator-based risk statistics.
result Derived dual representation for regulator-based risk statistics.
New risk statistics for loss-based regulation.
problem Regulatory focus on losses over gains.
method Developed new risk statistics using scenario analysis.
result New risk statistics extend existing measures.
The paper introduces a new risk statistic considering the time value of money.
problem Traditional risk statistics do not fully account for the time value of money.
method Introducing set-valued risk statistics with the time value of money.
result The new risk statistic provides a more accurate quantification of portfolio risk.
Paper tackles complex risk in deep neural networks.
problem Complex risk in deep neural networks.
method Developed new approach for complex risk statistics.
result Derived dual representation for complex risk.
Develops a statistical framework for coherent risk estimation.
problem Constructing coherent risk estimators with sound financial and statistical properties.
method Inspired by axiomatic risk measure theory, defines coherent risk estimators through robust representations linked to L-estimators. result Demonstrates that coherence of a risk measure does not necessarily carry over to its estimators and shows alternative weight structures can lead to different outcomes.
Study excess risk in statistical inference with transformations.
problem Excess risk in estimating random variables from feature vectors and transformations.
method Characterize lossless transformations, develop test statistics, and information-theoretic bounds.
result Strongly consistent partitioning test statistic for lossless transformations.
L-ARC improves model fairness by localizing risk guarantees.
problem Improving model fairness in tasks like image segmentation and wireless networks.
method Localized Adaptive Risk Control (L-ARC) updates a threshold function in RKHS to target localized statistical risk guarantees.
result L-ARC produces prediction sets with improved fairness across different data subpopulations.
New statistical factors improve portfolio risk estimation.
problem Improving estimation of portfolio risk using new statistical factors.
method Matrix factor models and statistical methods (partial F test, double selection LASSO).
result New statistical factors add explanatory power in asset pricing.
The book chapter discusses tail risk analysis for financial data using extreme value statistics.
problem Serial dependence in financial time series complicates tail risk assessment.
method The approach involves unconditional and conditional quantile forecasting.
result Serial dependence impacts multivariate tail dependence.
Prove non-asymptotic bounds for minimal risk in statistical learning
problem Estimating minimal risk in statistical learning
method Using concentration inequalities
result Non-asymptotic bounds for minimal risk
We develop a statistical framework to benchmark and select large language models based on their risks.
problem Benchmarking and selecting large language models based on their associated risks.
method A distributional framework using first and second order stochastic dominance, linked to mean-risk models in finance.
result Formalizes a risk-aware approach for model selection, balancing risk and utility.
The paper examines expectile quadrangle properties in risk management.
problem Exploring the properties of expectile quadrangles in risk management.
method Rigorously examines the properties of expectile quadrangles.
result Rigorously examines the properties of expectile quadrangles.
Paper improves clustering risk bounds for kernel k-means.
problem Improving clustering risk bounds for kernel k-means.
method Analyzes kernel k-means and Nyström approximation.
result Achieves nearly optimal excess clustering risk bound.
The paper analyzes the risk of investing in a basket of 27 cryptocurrencies using statistical distributions.
problem Risk assessment of capital allocation in a basket of cryptocurrencies.
method Used statistical tests to determine the most appropriate distribution (SDI) for modeling returns, and adapted the generalized Pareto distribution for tail risk assessment.
result Found that a combination of stable and generalized Pareto distributions provides a more accurate risk assessment for the basket of cryptocurrencies.
Statistical learning theory explains SVMs for data-driven decision making.
problem Decision making and model construction from data.
method Statistical learning theory, focusing on empirical and structural risk minimization.
result Support Vector Machines (SVMs) are a prominent implementation of structural risk minimization.
Starting from the requirement that risk measures of financial portfolios should be based on their losses, not their gains, we define the notion of loss-based risk measure and study the properties of this class of risk measures. We characterize loss-based risk measures by a representation theorem and give examples of su…
In this study, we analyze the aerospace stocks prices in order to characterize the sector behavior. The data analyzed cover the period from January 1987 to April 1999. We present a new index for the aerospace sector and we investigate the statistical characteristics of this index. Our results show that this index is we…
New algorithms avoid non-monotonic risk curves in statistical learning.
problem Non-monotonic behavior of risk curves in statistical learning.
method Derive risk-monotonic algorithms under weak assumptions.
result Risk monotonicity does not necessarily lead to worse excess risk rates.
This paper derives -- considering a Gaussian setting -- closed form solutions of the statistics that Adrian and Brunnermeier and Acharya et al. have suggested as measures of systemic risk to be attached to individual banks. The statistics equal the product of statistic specific Beta-coefficients with the mean corrected…
A new method for backtesting ES forecasts in banking.
problem Designing a model-free backtesting procedure for Expected Shortfall forecasts.
method Use e-values and e-processes to introduce backtest e-statistics for VaR and ES.
result The proposed method can be applied to various risk measures and statistical quantities.
The paper validates a centrality measure for financial networks during financial distress.
problem Systemic risk and shock propagation in financial networks.
method Statistical validation method for network centrality measures.
result The proposed centrality measure increases significantly during financial distress.
Paper introduces a new volatility estimator for jump-diffusion models.
problem Disentangling integrated variance from total process quadratic variation.
method Order statistics approach to estimate time-varying volatility and jumps.
result Empirical tests show improved Value at Risk forecasting.
Robust learning method minimizes risk with corrupted data.
problem Statistical learning with unknown corrupted data fraction.
method Develops a robust learning method with specified corrupted data fraction upper bound.
result Optimal weights provide robustness against corrupted data.
Study tests if equity factors explain Bitcoin's risk and returns.
problem Explaining Bitcoin's risk and return with equity factors.
method Applied statistical methods to test Fama-French factors on Bitcoin's excess returns.
result Fama-French factors have explanatory power on Bitcoin's risk and returns.
Extends conformal prediction for controlling expected risk of monotone loss functions.
problem Controlling expected risk of monotone loss functions.
method Generalizes split conformal prediction with coverage guarantee, extending to distribution shift, quantile risk, multiple, adversarial, and expectations of U-statistics.
result Tight up to an O(1/n) factor, with worked examples in computer vision and natural language processing. Research evaluates three risk models for portfolio construction during market downturns.
problem Challenges in constructing quantitative portfolios using statistical risk models.
method Three statistical risk models tested on 1,000 stocks across four periods.
result Models consistently outperform market returns in various crises.
This paper reviews statistical and machine learning methods for anti-money laundering.
problem Lack of scientific literature on statistical and machine learning methods for anti-money laundering.
method Client risk profiling and suspicious behavior flagging.
result Client risk profiling involves diagnostics, while suspicious behavior flagging involves non-disclosed features and hand-crafted risk indices.
Paper compares neural networks and classical statistics for dementia prediction, highlighting interpretability of classical methods.
problem Tackles the challenge of interpreting risk factors for dementia prediction.
method Compares neural networks and classical statistics for dementia prediction.
result Classical statistics provide clearer interpretation of risk factors compared to neural networks.
Develops a new robust risk measure for neural networks.
problem Adversarial risk metrics fail to capture probabilistic perturbations and poor train-test generalization.
method Statistically robust risk (SRR) framework considering pointwise corruption distributions.
result SRR provides superior generalization performance compared to adversarial risks.
Improved risk bounds for statistical inference problems.
problem Statistical inference and risk bounds for estimators.
method Adapted binary hypothesis testing approach to Fano's inequality for tighter lower bounds.
result Asymptotically sharp risk lower bounds for density estimation, active learning, and compressed sensing.
The paper tackles extreme value statistics for censored data with heavy tails under competing risks.
problem Estimating extreme value index and quantiles of sub-distribution function in heavy-tailed data with censoring and competing risks.
method Asymptotic normality of a novel Aalen-Johansen integral estimator is established for the extreme value index. Estimation of extreme quantiles of cumulative incidence function is also addressed.
result Asymptotic normality of the proposed estimator for extreme value index is established.
Proposes risk-averse learning framework using CVaR for better performance evaluation.
problem Risk-averse evaluation of machine learning algorithms.
method Develops algorithms based on stochastic gradient descent for CVaR optimization with weaker distributional assumptions.
result Shows convergence and generalization bounds for the proposed algorithms.
The paper tackles nonparametric regression with distributed data under communication constraints.
problem Nonparametric estimation of a smooth function with data distributed across multiple machines and limited communication.
method The approach involves constructing an estimator of the true function at a central machine with limited bits for transmission, considering various settings of machine number, data size, and communication budget.
result The paper identifies three regimes based on the relationship among machines, data size, and communication budget, providing both lower and upper bounds on statistical risk.
New measures generalize existing ones, linking information and risk.
problem Linking information measures and risk in statistical decision problems.
method Introducing new families of divergence measures and deriving an information processing equality.
result Extension of variational φ-divergence representation to multiple distributions. Improves statistical learning bounds with self-concordant losses.
problem Statistical prediction with nuisance components.
method Orthogonal statistical learning with self-concordant loss.
result Non-asymptotic bounds on excess risk improved by a dimension factor.
This study measures price risk aversion using indirect utility functions in a lab experiment.
problem Measuring risk aversion with uncertain prices in experimental economics.
method Using indirect utility functions and a multiple price list method in a lab experiment.
result Price risk aversion is statistically greater than payoff risk aversion.
Score attack method provides a lower bound on privacy-constrained minimax risk.
problem Characterizing the optimality of privacy-constrained statistical models.
method Score attack based on tracing attack concept.
result Optimally lower bounds the minimax risk of estimating unknown model parameters.
New framework calibrates models to control risk under performativity.
problem Calibrating models to ensure reliable decision-making under performativity.
method Iteratively refined calibration process for different risk measures and tail bounds.
result Statistically rigorous risk control under performativity demonstrated.
SVR analyzed within RQ framework for risk management.
problem Risk management in stochastic optimization.
method Risk Quadrangle (RQ) theory applied to SVR.
result SVR formulations as minimization of Vapnik error and CVaR norm.
New robust estimators for risk minimization in statistical models.
problem Robust estimation in statistical models, especially in heavy-tailed settings.
method Novel robust gradient descent approach for convex risk minimization.
result Proves robustness of estimators in general statistical models and specific cases.
Machine learning helps estimate risk premiums of stocks without knowing their factors.
problem Estimate risk premiums of stocks without knowing their underlying factors.
method Used elastic-net machine learning to project stock returns onto peers and construct replicate portfolios.
result Unique stocks have higher SARP and excess returns than ubiquitous stocks.
This paper extends the Risk Quadrangle framework for risk management and optimization.
problem Integrating risk management, optimization, and statistical estimation.
method Review and extension of the Risk Quadrangle framework with new quadrangles.
result New quadrangles offer novel approaches to risk-sensitive decision-making.
Study on statistical estimation over Gaussian MAC, comparing analog and digital schemes.
problem Distributed minimax statistical estimation over a Gaussian MAC.
method Developed analog joint estimation-communication schemes and derived information-theoretic lower bounds.
result Achieved risk within a logarithmic factor of information-theoretic lower bounds.
This research proposes methods to model and assess liability liquidity risk in asset management.
problem Lack of standardized models for liability liquidity risk in asset management.
method Statistical models, zero-inflated models, aggregate and individual-based approaches, and factor models.
result Developed mathematical and statistical approaches to estimate and assess redemption shocks.
In statistical learning theory, convex surrogates of the 0-1 loss are highly preferred because of the computational and theoretical virtues that convexity brings in. This is of more importance if we consider smooth surrogates as witnessed by the fact that the smoothness is further beneficial both computationally- by at…
The recent explosion in the amount and dimensionality of data has exacerbated the need of trading off computational and statistical efficiency carefully, so that inference is both tractable and meaningful. We propose a framework that provides an explicit opportunity for practitioners to specify how much statistical ris…
This paper improves the robustness of risk estimation for financial positions.
problem Ensuring robustness of risk measures in the presence of data noise.
method Proposes a quantitative approach using the Fortet-Mourier metric to quantify the variation of true probability measures.
result Derives explicit error bounds for discrepancies between laws of estimators based on true and perturbed data.
Study tests if deep hedging differs from delta hedging in a GARCH market model.
problem Whether deep hedging includes speculative components in a GARCH market.
method Tested in a GARCH-based market model, comparing deep hedging and delta hedging.
result The difference between deep hedging and delta hedging is speculative if risk measure does not prioritize adverse outcomes.