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.
Proposes resilience metrics for large blackout costs with logarithmic resilience.
Logarithmic regret strategies for safe multi-armed bandits with safety risk constraints.
The paper addresses portfolio allocation with uncertain covariance matrices, finding a logarithmic risk dependence.
We investigate optimal consumption problems for a Black-Scholes market under uniform restrictions on Value-at-Risk and Expected Shortfall for logarithmic utility functions. We find the solutions in terms of a dynamic strategy in explicit form, which can be compared and interpreted. This paper continues our previous wor…
The Kelly Criterion is applied to prediction markets to analyze risk and return.
Study identifies key ESG variables for assessing financial risk.
The paper analyzes competition among fund managers using excess logarithmic returns and constructs games to find optimal allocations.
New estimator achieves minimax optimal risk in transfer learning.
New approach avoids restrictive assumptions for optimal portfolio in default risk scenarios.
Paper analyzes risk bounds for in-context learning in multiclass classification.
Study risk-constrained Kelly optimization for mutually exclusive outcomes, proving support invariance and developing a structured algorithm.
Kelly investing improved with options to reduce estimation risk.
A new method for estimating probabilities and risks using Markov processes.
We show that our generalization of the Black-Scholes partial differential equation (pde) for nontrivial diffusion coefficients is equivalent to a Martingale in the risk neutral discounted stock price. Previously, this was proven for the case of the Gaussian logarithmic returns model by Harrison and Kreps, but we prove …
Most high-dimensional estimation and prediction methods propose to minimize a cost function (empirical risk) that is written as a sum of losses associated to each data point. In this paper we focus on the case of non-convex losses, which is practically important but still poorly understood. Classical empirical process …
We develop a new theoretical framework, the \emph{envelope complexity}, to analyze the minimax regret with logarithmic loss functions and derive a Bayesian predictor that adaptively achieves the minimax regret over high-dimensional -balls within a factor of two. The prior is newly derived for achieving the mini…
Study shows Skorokhod insider outperforms forward insider in logarithmic utility maximization.
Robo-advisors estimate clients' risk aversion using interactive questionnaires.
Study gap-dependent regret bounds for risk-sensitive RL.
New algorithm reduces online logistic regression regret without exponential constant.
We maximize the expected utility from terminal wealth for an HARA investor when the market price of risk is an unobservable random variable. We compute the optimal portfolio explicitly and explore the effects of learning by comparing it with the corresponding myopic policy. In particular, we show that, for a market pri…
Geometrically convex return risk measures on AM-algebras
We consider the economic problem of optimal consumption and investment with power utility. We study the optimal strategy as the relative risk aversion tends to infinity or to one. The convergence of the optimal consumption is obtained for general semimartingale models while the convergence of the optimal trading strate…
We derive PAC-Bayesian learning guarantees for heavy-tailed losses, and obtain a novel optimal Gibbs posterior which enjoys finite-sample excess risk bounds at logarithmic confidence. Our core technique itself makes use of PAC-Bayesian inequalities in order to derive a robust risk estimator, which by design is easy to …
Develops a new option pricing model under G-expectation framework.
This work improves policy evaluation and selection using logarithmic smoothing for pessimistic off-policy estimation.
We derive new results related to the portfolio choice problem for power and logarithmic utilities. Assuming that the portfolio returns follow an approximate log-normal distribution, the closed-form expressions of the optimal portfolio weights are obtained for both utility functions. Moreover, we prove that both optimal…
Paper presents efficient IS for tail risk estimation with machine learning features.
We study a portfolio selection problem in a continuous-time Itô-Markov additive market with prices of financial assets described by Markov additive processes which combine Lévy processes and regime switching models. Thus the model takes into account two sources of risk: the jump diffusion risk and the regime switching …
The study tightens risk bounds for mixtures of experts using local differential privacy.
The paper analyzes prediction error in nonstationary settings using weighted risk minimization.
Paper proposes an algorithm to optimize CVaR using retrospective approximation and importance sampling.
Online learning has traditionally focused on the expected rewards. In this paper, a risk-averse online learning problem under the performance measure of the mean-variance of the rewards is studied. Both the bandit and full information settings are considered. The performance of several existing policies is analyzed, an…
The aim of this paper is to determine the Value at Risk (VaR) of the portfolio consisting of long positions in foreign currencies on an emerging market. Basing on empirical data we restrict ourselves to the case when the tail parts of distributions of logarithmic returns of these assets follow the power laws and the lo…
We estimate risk measures in Markov cost processes with lower and upper bounds.
This note will extend the research presented in Brown & Rogers (2009) to the case of CRRA agents. We consider the model outlined in that paper in which agents had diverse beliefs about the dividends produced by a risky asset. We now assume that the agents all have CRRA utility, with some integer coefficient of relative…
The overarching goal of this paper is to derive excess risk bounds for learning from exp-concave loss functions in passive and sequential learning settings. Exp-concave loss functions encompass several fundamental problems in machine learning such as squared loss in linear regression, logistic loss in classification, a…
A statistical decision problem is hidden in the core of option pricing. A simple form for the price C of a European call option is obtained via the minimum Bayes risk, R_B, of a 2-parameter estimation problem, thus justifying calling C Bayes (B-)price. The result provides new insight in option pricing, among others obt…
We study the market selection hypothesis in complete financial markets, populated by heterogeneous agents. We allow for a rich structure of heterogeneity: individuals may differ in their beliefs concerning the economy, information and learning mechanism, risk aversion, impatience and 'catching up with Joneses' preferen…
Lower bounds on Bayes risk for realizable models derived using information theory.
We consider the problem of nonparametric regression when the covariate is -dimensional, where . In this paper we introduce and study two nonparametric least squares estimators (LSEs) in this setting---the entirely monotonic LSE and the constrained Hardy-Krause variation LSE. We show that these two LSEs are…
We present a method to obtain the average and the typical value of the number of critical points of the empirical risk landscape for generalized linear estimation problems and variants. This represents a substantial extension of previous applications of the Kac-Rice method since it allows to analyze the critical points…
New method for unbiased regression reduces excess risk.
Optimizes privacy-preserving optimization for heavy-tailed data.
Investor optimizes worst-case portfolio in uncertain markets.
We extend the lifecycle model (LCM) of consumption over a random horizon (a.k.a. the Yaari model) to a world in which (i.) the force of mortality obeys a diffusion process as opposed to being deterministic, and (ii.) a consumer can adapt their consumption strategy to new information about their mortality rate (a.k.a. h…
Study on estimating invertible functions with minimax analysis.
New algorithms achieve uniform stability for empirical risk minimization.