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

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 (...

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Bibliografische Detailangaben

Hauptverfasser:	Jong-Min Kim, Hope H. Han, Sangjin Kim
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	MDPI AG 2022-07-01
Schriftenreihe:	Axioms
Schlagworte:	oil prices S&P 500 multivariate time series Gaussian process model vine copula Bayesian variable selection
Online-Zugang:	https://www.mdpi.com/2075-1680/11/8/375
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