arXiv research

The paper tackles reinforcement learning with exogenous variables and rewards.

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 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…

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.

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…

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

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

Enhances random forest performance with exogenous randomness.

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.

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…

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

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

SPECTRA improves probabilistic energy forecasting by separating trends and uncertainties.

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.

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…

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.

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…

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.

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

We introduce a new measure of activity of financial markets that provides a direct access to their level of endogeneity. This measure quantifies how much of price changes are due to endogenous feedback processes, as opposed to exogenous news. For this, we calibrate the self-excited conditional Poisson Hawkes model, whi…

A phenomenological investigation of the endogenous and exogenous dynamics in the fluctuations of capital fluxes is investigated on the Chinese stock market using mean-variance analysis, fluctuation analysis and their generalizations to higher orders. Non-universal dynamics have been found not only in $α$ exponents diff…

Study reveals 2020 stock crashes were mostly endogenous, not exogenous.

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

Deep state space model forecasts time series with uncertainty.

Method identifies unknown intervention targets in structural causal models from diverse data.

Study portfolio selection with exogenous and endogenous transaction costs using deep learning.

Research forecasts electricity spot prices using stochastic volatility models.

Bayesian method for multivariate autoregressive models with exogenous inputs.

We investigate the trading behavior of a large set of single investors trading the highly liquid Nokia stock over the period 2003-2008 with the aim of determining the relative role of endogenous and exogenous factors that may affect their behavior. As endogenous factors we consider returns and volatility, whereas the e…

This paper describes an agent-based model of interacting firms, in which interacting firm agents rationally invest capital and labor in order to maximize payoff. Both transactions and production are taken into account in this model. First, the performance of individual firms on a real transaction network was simulated.…

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

We perform an extended analysis of the distribution of drawdowns in the two leading exchange markets (US dollar against the Deutsmark and against the Yen), in the major world stock markets, in the U.S. and Japanese bond market and in the gold market, by introducing the concept of ``coarse-grained drawdowns,'' which all…

New approach measures systemic risk by absorbing shocks before financial systems deteriorate.

For this special issue, the article aims at discussing a few econophysics problems studied so far rather successfully. The following "applications" in micro-econo-physics are considered : (i) financial crashes; it is emphasized that one can distinguish between endogenous and exogenous causes; (ii) portofolio control, s…

Effective risk control must make a tradeoff between the microprudential risk of exogenous shocks to individual institutions and the macroprudential risks caused by their systemic interactions. We investigate a simple dynamical model for understanding this tradeoff, consisting of a bank with a leverage target and an unl…

We investigate the large-volatility dynamics in financial markets, based on the minute-to-minute and daily data of the Chinese Indices and German DAX. The dynamic relaxation both before and after large volatilities is characterized by a power law, and the exponents $p_\pm$ usually vary with the strength of the large vo…

Partially performative prediction studies how predictive models influence future data.

Forecasting stock market decline and recovery post-COVID-19.