Probabilistic Forecasting with Machine Learning and Big Data

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Probabilistic Forecasting with Machine Learning and Big Data

Learning Probability Distributions in Macroeconomics and Finance

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Tags:
probability finance learning economics fan chart



Courselet Content

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General Overview

Description

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.

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Last Updated 16th January 2023
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Meet the instructors !

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About the Instructor

Jozef Baruník is an Associate Professor at the Institute of Economic Studies, Charles University in Prague. He also serves as a head of the Econometrics department at the Czech Academy of Sciences. In his research, he develops mathematical models for understanding financial problems (such as measuring and managing financial risk), develops statistical methods and analyzes financial data. Especially, he is interested in asset pricing, high-frequency data, financial econometrics, machine learning, high-dimensional financial data sets (big data), and frequency domain econometrics (cyclical properties and behavior of economic variables).

Jozef’s work has appeared in the Review of Economics and StatisticsEconometrics JournalJournal of Financial EconometricsJournal of Financial MarketsEconometric ReviewsJournal of Economic Dynamics and ControlThe Energy Journal, he is an Associate Editor of the Digital Finance, Journal of Economic Interaction and Coordination, and Kybernetika, and also referees frequently for several journals and grant agencies in the fields of econometrics, finance, and statistics.