arXiv research

The paper calculates bounds for risk metrics and entropies under partial information constraints.

Exponential dispersion model is a useful framework in machine learning and statistics. Primarily, thanks to the additive structure of the model, it can be achieved without difficulty to estimate parameters including mean. However, tight conditions on cumulant function, such as analyticity, strict convexity, and steepne…

Kernelized cumulants improve statistical analysis in high-dimensional spaces.

Study quantifies how LLMs capture higher-order statistical structure using cumulant expansion.

The paper prices energy spread options using a complex stochastic model.

We introduce the class of affine forward variance (AFV) models of which both the conventional Heston model and the rough Heston model are special cases. We show that AFV models can be characterized by the affine form of their cumulant generating function, which can be obtained as solution of a convolution Riccati equat…

New algorithm offers costless model selection in contextual bandits.

Derives operational-time variance kernel for reaction boundaries in financial markets.

The study compares parametric and nonparametric models for estimating mean-variance mixtures and finds that nonparametric models perform better.

We introduce a faithful representation of the heavy tail multivariate distribution of asset returns, as parsimonous as the Gaussian framework. Using calculation techniques of functional integration and Feynman diagrams borrowed from particle physics, we characterize precisely, through its cumulants of high order, the d…

This paper presents a multinomial method for option pricing when the underlying asset follows an exponential Variance Gamma process. The continuous time Variance Gamma process is approximated by a discrete time Markov chain with the same firsts four cumulants. This approach is particularly convenient for pricing Americ…

Derives variance kernel for reaction boundary in financial models.

Forecast stock return distributions using neural networks.

In this paper we extend temporal difference policy evaluation algorithms to performance criteria that include the variance of the cumulative reward. Such criteria are useful for risk management, and are important in domains such as finance and process control. We propose both TD(0) and LSTD(lambda) variants with linear…

The paper analyzes worst-case distortion risk metrics and weighted entropy under partial information.

This study optimizes stock portfolios for Indian sectors using historical data.

Derives a general derivative identity for conditional mean in Gaussian noise.

PS-IG improves feature attribution by reducing noise and variance.

We determine the variance-optimal hedge when the logarithm of the underlying price follows a process with stationary independent increments in discrete or continuous time. Although the general solution to this problem is known as backward recursion or backward stochastic differential equation, we show that for this cla…

Paper proposes a method to estimate confidence bands for survival random forests.

The paper proves a regret bound for a sub-Gaussian mixture on unbounded data.

The objective in a traditional reinforcement learning (RL) problem is to find a policy that optimizes the expected value of a performance metric such as the infinite-horizon cumulative discounted or long-run average cost/reward. In practice, optimizing the expected value alone may not be satisfactory, in that it may be…

The paper develops a robust algorithm for contextual bandits with heavy-tailed rewards.

New algorithm reduces regret in linear mixture SSPs without cost bounds.

A new method reduces variance in PG methods for RL, improving efficiency and convergence.

Study identifies contagion in aggregated defaults despite environmental changes.

New method estimates SW distance using CDFs for scalable data parallelism.

The stochastic multi-armed bandit (MAB) problem is a common model for sequential decision problems. In the standard setup, a decision maker has to choose at every instant between several competing arms, each of them provides a scalar random variable, referred to as a "reward." Nearly all research on this topic consider…

Cryptocurrency markets show similar returns but different volatility responses to infrastructure and regulatory shocks.

New method quantifies uncertainty in reinforcement learning models.

Neural Thompson Sampling uses deep neural networks for contextual bandit problems.

The paper proposes a new method for clustering survival data using smoothed log-hazard trajectories.

Improved regret bounds for online convex optimization under stochastic and adversarial settings.

We consider a stochastic bandit problem with infinitely many arms. In this setting, the learner has no chance of trying all the arms even once and has to dedicate its limited number of samples only to a certain number of arms. All previous algorithms for this setting were designed for minimizing the cumulative regret o…

The Gaussian mechanism is an essential building block used in multitude of differentially private data analysis algorithms. In this paper we revisit the Gaussian mechanism and show that the original analysis has several important limitations. Our analysis reveals that the variance formula for the original mechanism is …

This paper addresses dueling bandits with contextual information, improving regret bounds by accounting for variance.

Significant improvements in regret analysis for adaptive online learning problems.

Study tackles infinitely many-armed bandits with rotting rewards, achieving tight regret bounds.

This paper analyzes OCBA algorithms' convergence rates for DEDS optimization.

In this study we prove the existence of statistical arbitrage opportunities in the Black-Scholes framework by considering trading strategies that consists of borrowing from the risk free rate and taking a long position in the stock until it hits a deterministic barrier level. We derive analytical formulas for the expec…

Paper approximates Kelly betting for wealth growth.

The paper studies empirical processes from nearest neighbors in regression.

We extend and test empirically the multifractal model of asset returns based on a multiplicative cascade of volatilities from large to small time scales. The multifractal description of asset fluctuations is generalized into a multivariate framework to account simultaneously for correlations across times scales and bet…

This paper optimizes importance sampling for rare-event options pricing under the Heston model.

This paper optimizes cryptocurrency portfolios by integrating sentiment analysis with technical indicators.

Estimates CDF over complex regions using normalizing flows.

We consider the performance of non-optimal hedging strategies in exponential Lévy models. Given that both the payoff of the contingent claim and the hedging strategy admit suitable integral representations, we use the Laplace transform approach of Hubalek et al. (2006) to derive semi-explicit formulas for the resulting…

Model predicts stock price changes and forecasts using tokenized data.