Forecasting Crude Oil Prices with Major S&P 500 Stock Prices: Deep Learning, Gaussian Process, and Vine Copula

Jong Min Kim, Hope H. Han, Sangjin Kim

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This paper introduces methodologies in forecasting oil prices (Brent and WTI) with multivariate time series of major S&P 500 stock prices using Gaussian process modeling, deep learning, and vine copula regression. We also apply Bayesian variable selection and nonlinear principal component analysis (NLPCA) for data dimension reduction. With a reduced number of important covariates, we also forecast oil prices (Brent and WTI) with multivariate time series of major S&P 500 stock prices using Gaussian process modeling, deep learning, and vine copula regression. To apply real data to the proposed methods, we select monthly log returns of 2 oil prices and 74 large-cap, major S&P 500 stock prices across the period of February 2001–October 2019. We conclude that vine copula regression with NLPCA is superior overall to other proposed methods in terms of the measures of prediction errors.

Original languageEnglish (US)
Article number375
JournalAxioms
Volume11
Issue number8
DOIs
StatePublished - Aug 2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors.

Keywords

  • Bayesian variable selection
  • functional principal component analysis
  • Gaussian process model
  • multivariate time series
  • nonlinear principal component analysis
  • oil prices
  • S&P 500
  • vine copula

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