arXiv research

Study optimal consumption and investment strategies with leverage constraints using Epstein-Zin utility.

Study finds financial constraints explain zero-leverage firms.

This paper characterizes the equilibrium in a continuous time financial market populated by heterogeneous agents who differ in their rate of relative risk aversion and face convex portfolio constraints. The model is studied in an application to margin constraints and found to match real world observations about financi…

Study proposes a neural network approach for high inflation investment portfolios with leverage constraints.

The majority of stylized facts of financial time series and several Value-at-Risk measures are modeled via univariate or multivariate GARCH processes. It is not rare that advanced GARCH models fail to converge for computational reasons, and a usual parsimonious approach is the GJR-GARCH model. There is a disagreement i…

In this work we consider adversarial contextual bandits with risk constraints. At each round, nature prepares a context, a cost for each arm, and additionally a risk for each arm. The learner leverages the context to pull an arm and then receives the corresponding cost and risk associated with the pulled arm. In additi…

We present a simple agent-based model of a financial system composed of leveraged investors such as banks that invest in stocks and manage their risk using a Value-at-Risk constraint, based on historical observations of asset prices. The Value-at-Risk constraint implies that when perceived risk is low, leverage is high…

Unified approach adjusts classifiers to meet system-level constraints.

Optimizes bank capital structure under Basel III constraints, simplifying complex dynamics.

ADMM solves constrained CASH problems by breaking them into smaller, manageable pieces.

The paper analyzes CMDPs, balancing exploration and exploitation to avoid constraint violations.

The study connects fairness constraints with optimal transport to derive new insights in classification.

Bayesian algorithms improve crowdsourcing with label and instance constraints.

VRSGT algorithm reduces orthogonality constraints in decentralized optimization.

Paper optimizes portfolios for absolute return funds with constraints.

There are some statistical anomalies in the Chinese stock market, i.e., positive return skewness, anti-leverage effect (positive returns induce higher volatility than negative returns); and reverse volatility asymmetry (contemporaneous return-volatility correlation is positive). In this paper, we first confirm the exis…

Framework for controlling multiple risks in AI models.

Paper proposes a new algorithm for graph learning with covariance constraints.

This work proposes an online learning approach to tighten constraints in stochastic control problems.

Algorithm minimizes regret while adhering to unknown safety constraints.

This work restricts hidden cardinality in causal models to infer causal relations.

CANs improve GANs by enforcing structured constraints during training.

Peters (2011a) defined an optimal leverage which maximizes the time-average growth rate of an investment held at constant leverage. It was hypothesized that this optimal leverage is attracted to 1, such that, e.g., leveraging an investment in the market portfolio cannot yield long-term outperformance. This places a str…

New methods for efficient exploration under unknown linear constraints in bandits.

This paper presents a distributionally robust Q-Learning algorithm (DrQ) which leverages Wasserstein ambiguity sets to provide idealistic probabilistic out-of-sample safety guarantees during online learning. First, we follow past work by separating the constraint functions from the principal objective to create a hiera…

Topological constraints improve neural network generalization.

Study integrates reliability constraints into generation planning models.

New method for private learning with fairness constraints.

New method optimizes processes under constraints using bivariate Gaussian models.

A new algorithm for identifying the best arm in linear feedback with safety constraints.

We study the constrained linear quadratic regulator with unknown dynamics, addressing the tension between safety and exploration in data-driven control techniques. We present a framework which allows for system identification through persistent excitation, while maintaining safety by guaranteeing the satisfaction of st…

New algorithm for contextual bandits with linear constraints using regression.

New DAG constraints improve differentiable DAG learning.

Unified framework for hard affine SDP constraints in vRKHSs.

Solves optimal stopping problem with Poisson constraints using jumps.

Paper studies optimal investing for retirees with risk constraints.

Recent work in learning ontologies (hierarchical and partially-ordered structures) has leveraged the intrinsic geometry of spaces of learned representations to make predictions that automatically obey complex structural constraints. We explore two extensions of one such model, the order-embedding model for hierarchical…

We propose a method to model multi-agent behaviors with limited observation and mechanical constraints.

Proposes SPCA to incorporate structural constraints in model identification.

Graph learning improves FXRP and FXSA with significant statistical arbitrage gains.

Model explains money creation under regulatory constraints.

VRPG algorithm optimizes convex constraints with non-asymptotic guarantees.

Bayesian optimization improves with transfer learning for aircraft design.

New framework improves reliability of learned representations by modeling uncertainty and structural constraints.

We propose an iterative gradient-based algorithm to efficiently solve the portfolio selection problem with multiple spectral risk constraints. Since the conditional value at risk (CVaR) is a special case of the spectral risk measure, our algorithm solves portfolio selection problems with multiple CVaR constraints. In e…

C-HMCNN(h) improves HMC classification by leveraging class hierarchy.

Develops a new solver for optimizing with stochastic dominance constraints.

Enhances flexibility in data reweighting with optimal transport and maximum entropy principles.