Predicting the Oil Market

66 Pages Posted: 18 Oct 2021 Last revised: 10 Oct 2024

See all articles by Charles W. Calomiris

Charles W. Calomiris

Columbia University - Columbia Business School; National Bureau of Economic Research (NBER)

Harry Mamaysky

Columbia University - Columbia Business School

Nida Cakir Melek

Federal Reserve Bank of Kansas City

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Date Written: October 2021

Abstract

We study the performance of many traditional and novel, text-based variables for in-sample and out-of-sample forecasting of oil spot, futures, and energy company stock returns, and changes in oil volatility, production, and inventories. After controlling for small-sample biases, we find evidence of in-sample predictability. Our text measures, derived using energy news articles, hold their own against traditional variables. While we cannot identify ex-ante rules for selecting successful out-of-sample forecasters, an analysis of all possible two-variable models reveals out-of-sample performance above that expected under random variation. Our findings provide new directions for identifying robust forecasting models for oil markets, and beyond.

Suggested Citation

Calomiris, Charles W. and Mamaysky, Harry and Cakir Melek, Nida, Predicting the Oil Market (October 2021). NBER Working Paper No. w29379, Available at SSRN: https://ssrn.com/abstract=3944427

Charles W. Calomiris (Contact Author)

Columbia University - Columbia Business School ( email )

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National Bureau of Economic Research (NBER)

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Harry Mamaysky

Columbia University - Columbia Business School ( email )

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Nida Cakir Melek

Federal Reserve Bank of Kansas City ( email )

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Kansas City, MO 64198
United States

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