Learning Probability Distributions in Macroeconomics and Finance

TL;DR

This paper introduces a deep learning method for probabilistic forecasting in macroeconomics and finance, capable of capturing complex, non-Gaussian, and non-linear patterns to improve decision-making under uncertainty.

Contribution

It presents a novel deep learning approach for learning probability distributions in macroeconomic and financial time series, handling high-dimensional data and complex distributional features.

Findings

Effective macroeconomic fan charts from high-dimensional data

Improved prediction of heavy-tailed, asymmetric stock return distributions

Demonstrated usefulness in real-world datasets

Abstract

We propose a deep learning approach to probabilistic forecasting of macroeconomic and financial time series. Being able to learn complex patterns from a data rich environment, our approach is useful for a decision making that depends on uncertainty of large number of economic outcomes. Specifically, it is informative to agents facing asymmetric dependence of their loss on outcomes from possibly non-Gaussian and non-linear variables. We show the usefulness of the proposed approach on the two distinct datasets where a machine learns the pattern from data. First, we construct macroeconomic fan charts that reflect information from high-dimensional data set. Second, we illustrate gains in prediction of stock return distributions which are heavy tailed, asymmetric and suffer from low signal-to-noise ratio.