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.
Develops a method to predict stock returns with time-varying risk premia.
Corrects GCV for inconsistent risk estimation in finite ensembles of penalized estimators.
Sparse-penalized deep neural networks improve performance in weakly dependent processes.
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.
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.
PyDTS analyzes survival data with discrete intervals and competing risks.
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.
We extend the analysis of investment strategies derived from penalized quantile regression models, introducing alternative approaches to improve state\textendash of\textendash art asset allocation rules. First, we use a post\textendash penalization procedure to deal with overshrinking and concentration issues. Second, …
The paper develops a deep neural network estimator for weakly dependent processes with various loss functions.
AI measures financial risk using linear quantile lasso regression.
Proposes a new model to analyze CT scans for lung cancer patients.
Proposes RVP to address theoretical concerns of V-REx for OOD generalization.
Paper proposes a new method to optimize deep neural networks with sparse regularization.
Let $\cF$ be a set of classification procedures with values in . 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.
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…
We consider first order expansions of convex penalized estimators in high-dimensional regression problems with random designs. Our setting includes linear regression and logistic regression as special cases. For a given penalty function and the corresponding penalized estimator , we construct a quantity ,…
This paper optimizes portfolio selection by penalizing tracking error, improving Sharpe ratio.
We consider a high dimensional binary classification problem and construct a classification procedure by minimizing the empirical misclassification risk with a penalty on the number of selected features. We derive non-asymptotic probability bounds on the estimated sparsity as well as on the excess misclassification ris…
We consider a group of mean-variance investors with mimicking desire such that each investor is willing to penalize deviations of his portfolio composition from compositions of other group members. Penalizing norm constraints are already applied for statistical improvement of Markowitz portfolio procedure in order to c…
This paper extends the standard chaining technique to prove excess risk upper bounds for empirical risk minimization with random design settings even if the magnitude of the noise and the estimates is unbounded. The bound applies to many loss functions besides the squared loss, and scales only with the sub-Gaussian or …
The MDL two-part coding provides a finite-sample upper bound on the statistical risk of penalized likelihood estimators over countable models. However, the bound does not apply to unpenalized maximum likelihood estimation or procedures with exceedingly small penalties. In this paper,…
Sparse multinomial logistic regression for multiclass classification with feature selection.
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…
Model predicts road traffic using high-dimensional time-series with L1-penalization.
Portfolio selection is the central task for assets management, but it turns out to be very challenging. Methods based on pattern matching, particularly the CORN-K algorithm, have achieved promising performance on several stock markets. A key shortage of the existing pattern matching methods, however, is that the risk i…
Develops a deep learning framework for various data types.
Study investigates asymptotic risk of overparameterized models, including deep neural networks.
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…
Develops methods for estimating constrained function-valued parameters in infinite-dimensional models.
A robust machine learning approach forecasts U.S. Treasury yields, reducing risk for investors.
Paper robustifies reinforcement learning with risk-averse methods.
Deep neural networks enforce non-crossing quantile regression curves.
Proposes a new framework for risk-sensitive RL using deep nets.
Bayesian method detects change points and clusters in piece-wise constant signals.
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.
Study accelerates gradient methods in machine learning, revealing risk and stability connections.
This paper investigates how to measure common market risk factors using newly proposed Panel Quantile Regression Model for Returns. By exploring the fact that volatility crosses all quantiles of the return distribution and using penalized fixed effects estimator we are able to control for otherwise unobserved heterogen…
Paper proposes deep neural networks for nonparametric regression from dependent data.
A new option pricing model handles non-constant risk aversion and transaction costs.
Neural networks are usually not the tool of choice for nonparametric high-dimensional problems where the number of input features is much larger than the number of observations. Though neural networks can approximate complex multivariate functions, they generally require a large number of training observations to obtai…
This paper tackles model selection for MoE models in high-dimensional data.
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…