Develops a method to predict stock returns with time-varying risk premia.
problem Predicting stock returns with time-varying risk premia while maintaining no-arbitrage restrictions.
method Penalized two-pass regression with time-varying factor loadings, incorporating penalization in the first pass and grouping in the second pass.
result The proposed method reduces prediction errors compared to other approaches.
Corrects GCV for inconsistent risk estimation in finite ensembles of penalized estimators.
problem Inconsistent risk estimation of GCV for finite ensembles of penalized estimators.
method Identifies a correction involving an additional scalar correction based on degrees of freedom adjusted training errors from each ensemble component.
result CGCV maintains computational advantages of GCV and is model-free uniformly consistent for ridge regression.
Sparse-penalized deep neural networks improve performance in weakly dependent processes.
problem Nonparametric regression and classification under weak dependence.
method Sparse-penalized deep neural networks with oracle inequalities and convergence rates established.
result The proposed estimators outperform non-penalized ones in simulations.
We give improved constants for data dependent and variance sensitive confidence bounds, called empirical Bernstein bounds, and extend these inequalities to hold uniformly over classes of functionswhose growth function is polynomial in the sample size n. The bounds lead us to consider sample variance penalization, a nov…
New risk class penalizes loss deviations from mean on both sides.
problem Current risks are sensitive to loss tails on the upside and ignore the downside.
method Introduces a bi-directional risk class with flexible tail sensitivity.
result Derives high-probability learning guarantees without gradient clipping.
We study the risk assessment of uncertain cash flows in terms of dynamic convex risk measures for processes as introduced in Cheridito, Delbaen, and Kupper (2006). These risk measures take into account not only the amounts but also the timing of a cash flow. We discuss their robust representation in terms of suitably p…
The paper develops robust risk measures for uncertain loss positions.
problem Risk assessment for loss positions with uncertain distributions.
method Robust optimized certainty equivalents and generalized quantiles are proposed and analyzed.
result Robust expectiles with specific penalization functions are coherent risk measures.
Study pairs trading strategy with uncertain drift and penalized risk.
problem Optimizing pairs trading strategy with uncertain drift and risk penalty.
method Model pairs trading as a Gaussian mean-reverting process with a Markov chain, use stochastic filtering theory, and solve for logarithmic utility function.
result Characterize optimal strategies and value functions under full and partial information, showing certainty equivalence principle.
Improved asset allocation strategies using penalized quantile regression.
problem Improving investment strategies in asset allocation.
method Post-penalization, nonconvex penalties, and optimal tuning parameter selection.
result Alternative methods outperform simple LASSO, especially for extreme risk.
The paper develops a classification method using penalties on feature selection for high-dimensional data.
problem High-dimensional binary classification with many irrelevant features.
method Empirical risk minimization with l0-penalization for feature selection.
result The method achieves a sparse solution close to true sparsity with high probability and converges to low misclassification risk.
PyDTS analyzes survival data with discrete intervals and competing risks.
problem Discrete-time survival analysis with competing risks and optional penalization.
method Regularized estimation methods, model evaluation metrics, variable screening tools, and simulation module.
result Supports research and development in discrete-time survival analysis.
RACORN-K improves portfolio selection by penalizing risk in pattern matching.
problem Challenges in portfolio selection, especially risk-aversion in pattern matching methods.
method Risk-aversion CORN-K algorithm (RACORN-K) that penalizes risk in pattern matching.
result Significant improvements in return, Sharp ratio, and maximum drawdown on volatile markets.
The matrix completion problem consists in reconstructing a matrix from a sample of entries, possibly observed with noise. A popular class of estimator, known as nuclear norm penalized estimators, are based on minimizing the sum of a data fitting term and a nuclear norm penalization. Here, we investigate the case where …
Study optimizes portfolio to minimize relative drawdown duration, penalizing unfavorable performance states.
problem Minimizing relative drawdown duration in portfolio optimization relative to a benchmark.
method Introduces a benchmark-relative drawdown-duration criterion penalizing unfavorable performance states. Uses a one-dimensional Markovian representation and Hamilton-Jacobi-Bellman equation.
result Derives explicit projection-based characterization of the optimal feedback control and identifies geometric settings for unique strong solutions.
Paper proposes a new model to measure common risk factors using quantile regression.
problem Measuring common market risk factors among financial assets.
method Panel Quantile Regression Model for Returns with penalized fixed effects estimator.
result The proposed model outperforms other models in Value-at-Risk forecasting, especially in the 5% and 10% quantiles.
The paper develops a deep neural network estimator for weakly dependent processes with various loss functions.
problem Learning weakly dependent processes with a broad class of loss functions.
method Sparse-penalized deep neural networks with ψ-weak dependence structure and θ∞-coefficients. result Oracle inequalities for the excess risk of the sparse-penalized deep neural networks estimators.
AI measures financial risk using linear quantile lasso regression.
problem Measuring systemic financial risk accurately and quantitatively.
method Linear quantile lasso regression with penalization parameter lambda.
result The Financial Risk Meter (FRM) is a valid measure of systemic risk.
Investors mimic others' portfolios to reduce risk, leading to mutual funds that optimize this behavior.
problem Reduction of estimation risk in portfolio choice for mean-variance investors.
method Introduces penalties for deviations from group compositions, derived optimal portfolio weights.
result Explicit analytical solution for optimal portfolio weights in mutual funds.
Proposes a new model to analyze CT scans for lung cancer patients.
problem Analyzing survival risks of lung cancer patients using CT scans.
method Penalized Deep Partially Linear Cox Model (Penalized DPLC) incorporating SCAD penalty and deep neural network.
result The model effectively selects important texture features and estimates nonparametric components.
We develop a first order expansion for convex penalized estimators in high-dimensional regression.
problem High-dimensional regression problems with random designs.
method Construct a first order expansion η of the penalized estimator β^. result The risk of β^ is asymptotically the same as the risk of η. Proposes RVP to address theoretical concerns of V-REx for OOD generalization.
problem Theoretical concerns about V-REx's motivation and utility.
method Risk Variance Penalization (RVP) modifies V-REx's regularization.
result RVP discovers a robust predictor and finds invariant predictors under certain conditions.
Paper proposes a new method to optimize deep neural networks with sparse regularization.
problem Difficulty in achieving optimal convergence rates for deep neural networks due to sparsity constraints.
method Introduces a novel penalized estimation method for sparse DNNs, resolving computational and theoretical issues.
result Establishes an oracle inequality for the excess risk of the proposed sparse-penalized DNN estimator and derives convergence rates.
Let $\cF$ be a set of M classification procedures with values in [−1,1]. Given a loss function, we want to construct a procedure which mimics at the best possible rate the best procedure in $\cF$. This fastest rate is called optimal rate of aggregation. Considering a continuous scale of loss functions with various …
Study dynamic risk measures with distributional uncertainty using optimal transport.
problem Risk robustification under distributional uncertainty in Markovian models.
method Characterize risk measures via convex monotone semigroups and optimal transport costs.
result Identify generator and correction terms for dynamic risk measures under different scaling regimes.
In this paper the robust utility maximization problem for a market model based on Lévy processes is analyzed. The interplay between the form of the utility function and the penalization function required to have a well posed problem is studied, and for a large class of utility functions it is proved that the dual probl…
In this paper we present nonparametric estimators for coefficients in stochastic differential equation if the data are described by independent, identically distributed random variables. The problem is formulated as a nonlinear ill-posed operator equation with a deterministic forward operator described by the Fokker-Pl…
This paper optimizes portfolio selection by penalizing tracking error, improving Sharpe ratio.
problem Optimizing portfolio allocation with a penalty for deviation from a reference portfolio.
method Formulated as a McKean-Vlasov control problem, provides explicit solutions and asymptotic expansions.
result The penalized portfolio strategy outperforms standard mean-variance and reference portfolios in most cases.
Paper extends chaining technique for empirical risk minimization bounds.
problem Empirical risk minimization with unbounded noise and estimates.
method Chaining technique applied to random design settings, proving excess risk bounds.
result Proves upper bounds for empirical risk minimization with sub-Gaussian or subexponential noise.
Method forecasts market states using sparse precision matrix and penalized Mahalanobis distance.
problem Forecasting market states and distinguishing bull and bear markets.
method Identifies market states via sparse precision matrix and expectation values. Uses penalized Mahalanobis distance for clustering and forecasting.
result Successfully clusters market states and forecasts future market conditions with significant accuracy.
The paper improves risk bounds for maximum likelihood estimation with arbitrary penalties.
problem Improving risk bounds for maximum likelihood estimation with arbitrary penalties.
method Developed a more general inequality for arbitrary penalties, leading to exact risk bounds of order 1/n.
result Derived exact risk bounds of order 1/n for iid parametric models, improving on previous bounds.
Sparse multinomial logistic regression for multiclass classification with feature selection.
problem High-dimensional multiclass classification with a focus on sparse models.
method Penalized maximum likelihood with complexity penalty, feature selection using group Lasso and Slope classifiers.
result Achievement of minimax order in both small and large number of classes regimes.
Model predicts road traffic using high-dimensional time-series with L1-penalization.
problem Predicting high-dimensional road traffic data with limited observations.
method Vector autoregressive model with L1-penalization for high-dimensional regression.
result The approach identifies the most important road sections and is competitive in prediction.
It is well known that quantile regression model minimizes the portfolio extreme risk, whenever the attention is placed on the estimation of the response variable left quantiles. We show that, by considering the entire conditional distribution of the dependent variable, it is possible to optimize different risk and perf…
Develops a deep learning framework for various data types.
problem Handling nonparametric regression and classification across different data types.
method Introduces a general framework with two estimators: NPDNN and SPDNN, based on data satisfying generalized Bernstein-type inequalities.
result Both NPDNN and SPDNN estimators are minimax optimal in many classical settings.
Study investigates asymptotic risk of overparameterized models, including deep neural networks.
problem Understanding the risk of overparameterized models, especially deep neural networks.
method Analyzes the upper bound of an asymptotic risk of an estimator with penalization, combining Fisher information matrix properties and extended Marchenko-Pastur law.
result Generalized results valid for models without linear-in-feature constraints, indicating small asymptotic risk for specific structures like divisibility.
Develops methods for estimating constrained function-valued parameters in infinite-dimensional models.
problem Estimating function-valued parameters with structural constraints in complex models.
method Characterizes constrained solutions as minimizers of penalized population risk, using a Lagrange-type formulation and path through unconstrained space.
result Proposes estimators that achieve optimal risk and constraint satisfaction, applicable across various statistical learning approaches.
ConvSCCS model detects rare adverse drug reactions from EHRs.
problem Underreporting of adverse drug reactions due to physician reports.
method Conditional Poisson model with convolution and penalized step functions.
result Improves estimation of relative risks in diabetic patients.
We present the Shortfall Deviation Risk (SDR), a risk measure that represents the expected loss that occurs with certain probability penalized by the dispersion of results that are worse than such an expectation. SDR combines Expected Shortfall (ES) and Shortfall Deviation (SD), which we also introduce, contemplating t…
Deep neural networks enforce non-crossing quantile regression curves.
problem Estimating quantile regression curves without crossing.
method Penalized deep ReQU neural networks with a non-crossing penalty.
result Established non-asymptotic risk and error bounds for the estimated QRP.
A robust machine learning approach forecasts U.S. Treasury yields, reducing risk for investors.
problem Noisy and uncertain U.S. Treasury yields pose risk to forecast users.
method Formulates yield curve forecasting as a distributionally robust problem, combining factor models and machine learning.
result Robust forecast combinations improve out-of-sample performance across different maturity periods.
Paper robustifies reinforcement learning with risk-averse methods.
problem Making predictions robust to changes in system dynamics or rewards.
method Approximates Robust Reinforcement Learning using Φ-divergence and Risk-Averse formulation. result Classical Reinforcement Learning can be robustified using standard deviation penalization.
Bayesian method detects change points and clusters in piece-wise constant signals.
problem Detecting change points and clustering in piece-wise constant signals.
method Nonparametric penalized least square model selection on partitions of design points, with an efficient algorithm.
result Oracle inequality and adaptive upper bound on expected square risk of the estimator.
Proposes a new framework for risk-sensitive RL using deep nets.
problem Risk-sensitive reinforcement learning problems.
method Conditional elicitability, scoring functions, deep neural networks.
result Dynamic spectral risk measures can be approximated by deep nets.
Risk bounds for Classification and Regression Trees (CART, Breiman et. al. 1984) classifiers are obtained under a margin condition in the binary supervised classification framework. These risk bounds are obtained conditionally on the construction of the maximal deep binary tree and permit to prove that the linear penal…
Data-driven optimization improves mean-variance portfolios by penalizing norms.
problem Estimation error in mean-variance optimization.
method Augment MVO with norm penalties, use neural networks for optimization, and compute derivatives implicitly.
result Data-driven optimization reduces portfolio risk compared to standard MVO.
New method reduces unfairness in binary classification.
problem Achieving similar false positive and negative rates across two populations.
method Penalizes unfairness to achieve balanced false positive and negative rates.
result Empirically validated approach improves fairness and accuracy.
Study accelerates gradient methods in machine learning, revealing risk and stability connections.
problem Understanding the statistical risk of accelerated gradient methods in machine learning.
method Continuous-time analysis of Nesterov's accelerated gradient method and Polyak's heavy ball method for least squares regression.
result Connections between early stopping, stability, and curvature of loss function are revealed.
Paper proposes deep neural networks for nonparametric regression from dependent data.
problem Nonparametric regression from strongly mixing observations.
method Minimum error entropy principle applied to deep neural networks.
result Deep neural networks achieve minimax optimal convergence rates for Gaussian errors.