arXiv research

Enhances random forest performance with exogenous randomness.

We consider the problem of identifying the causal direction between two discrete random variables using observational data. Unlike previous work, we keep the most general functional model but make an assumption on the unobserved exogenous variable: Inspired by Occam's razor, we assume that the exogenous variable is sim…

Paper proves causal direction can be inferred from data with limited randomness.

The study uses Random Matrix Theory to identify structural changes in stock markets during shocks.

The paper tackles reinforcement learning with exogenous variables and rewards.

Study asset price bubbles using random matching and stochastic factors.

Exogenous state variables and rewards can slow down reinforcement learning by injecting uncontrolled variation into the reward signal. We formalize exogenous state variables and rewards and identify conditions under which an MDP with exogenous state can be decomposed into an exogenous Markov Reward Process involving on…

We study an optimal investment problem under default risk where related information such as loss or recovery at default is considered as an exogenous random mark added at default time. Two types of agents who have different levels of information are considered. We first make precise the insider's information flow by us…

We address microscopic, agent based, and macroscopic, stochastic, modeling of the financial markets combining it with the exogenous noise. The interplay between the endogenous dynamics of agents and the exogenous noise is the primary mechanism responsible for the observed long-range dependence and statistical propertie…

Paper develops a new RL method for MDPs with uncertainty, achieving better regret bounds.

Study classifies stock price jumps as exogenous or endogenous using news data.

New method learns exogenous variable distributions for better causal optimization.

We study Exo-MDPs to reduce sample complexity in reinforcement learning.

This paper uses NARX neural networks for macroeconomic forecasting and goal setting.

New approach reveals causal and probabilistic relationships from equations.

Time series prediction has been studied in a variety of domains. However, it is still challenging to predict future series given historical observations and past exogenous data. Existing methods either fail to consider the interactions among different components of exogenous variables which may affect the prediction ac…

This paper introduces a new class of Dynkin games, where the two players are allowed to make their stopping decisions at a sequence of exogenous Poisson arrival times. The value function and the associated optimal stopping strategy are characterized by the solution of a backward stochastic differential equation. The pa…

Financial price changes obey two universal properties: they follow a power law and they tend to be clustered in time. The second regularity, known as volatility clustering, entails some predictability in the price changes: while their sign is uncorrelated in time, their amplitude (or volatility) is long-range correlate…

This study compares machine learning models for short-term stock price forecasting.

New approach uses 'forward-looking' counterfactuals for treatment choice.

Most real life systems have a random component: the multitude of endogenous and exogenous factors influencing them result in stochastic fluctuations of the parameters determining their dynamics. These empirical systems are in many cases subject to noise of multiplicative nature. The special properties of multiplicative…

The paper analyzes binary option markets with exogenous information and price sensitivity.

In this paper we explain the wild fluctuations of financial prices from the intrinsic amplifying feedback of speculative supply and demand. Formally, we show that an asset return follows a multiplicative random growth with exogenous input, which is well-known to be a generic power-law generating process, and which coul…

We enhance short-rate models to control implied volatility analytically.

HL algorithms improve resource allocation in cloud environments.

Systems with long-range persistence and memory are shown to exhibit different precursory as well as recovery patterns in response to shocks of exogeneous versus endogeneous origins. By endogeneous, we envision either fluctuations resulting from an underlying chaotic dynamics or from a stochastic forcing origin which ma…

Improved electricity price forecasting with NBEATSx model.

Method estimates exogenous and endogenous factors from event times.

Study liquidity provision with exogenous competition using a reference market maker.

This work tackles fitting Hawkes processes to interval-censored data.

Many statistical methods have been proposed to estimate causal models in classical situations with fewer variables than observations (p<n, p: the number of variables and n: the number of observations). However, modern datasets including gene expression data need high-dimensional causal modeling in challenging situation…

Paper simplifies complex causal identifiability problems with exogenous isomorphism.

The paper models ATM cash withdrawal chaos and forecasts using deep learning.

We reverse engineer dynamics of financial contagion to find the scenario of smallest exogenous shock that, should it occur, would lead to a given final systemic loss. This reverse stress test can be used to identify the potential triggers of systemic events, and it removes the arbitrariness in the selection of shock sc…

Randomizes AD models for better option pricing.

This study defines a multivariate Self--Exciting Threshold Autoregressive with eXogenous input (MSETARX) models and present an estimation procedure for the parameters. The conditions for stationarity of the nonlinear MSETARX models is provided. In particular, the efficiency of an adaptive parameter estimation algorithm…

Proposes a new test for validating multivariate dynamic regression models.

We study the problem of optimal portfolio selection in an illiquid market with discrete order flow. In this market, bids and offers are not available at any time but trading occurs more frequently near a terminal horizon. The investor can observe and trade the risky asset only at exogenous random times corresponding to…

Incomplete financial markets are considered, defined by a multi-dimensional non-homogeneous diffusion process, being the direct sum of an Itô process (the price process), and another non-homogeneous diffusion process (the exogenous process, representing exogenous stochastic sources). The drift and the diffusion matrix …

In stochastic control problems delicate issues arise when the controlled system can jump due to both exogenous shocks and endogenous controls. Here one has to specify what the controller knows when about the exogenous shocks and how and when she can act on this information. We propose to use Meyer-$σ$-fields as a flexi…

Designs for allocating resources to prioritize needy applicants while estimating treatment effects.

The paper shows how sketching data can simplify regression inference even when errors are heteroskedastic.

Proposes Exogenous Matching for efficient counterfactual estimation.

Paper proposes a new model to assess risks in energy storage systems considering both exogenous and endogenous uncertainties.

CB-RL solves complex decision-making problems with contextual information and exogenous events.

In this paper we consider a class of BSDEs with drivers of quadratic growth, on a stochastic basis generated by continuous local martingales. We first derive the Markov property of a forward--backward system (FBSDE) if the generating martingale is a strong Markov process. Then we establish the differentiability of a FB…

Paper introduces EnCounteR for estimating causal effects using encouragement data.

The study examines pricing American options with both exogenous and endogenous transaction costs.