Forecasting Crude Oil Prices with Major S&P 500 Stock Prices: Deep Learning, Gaussian Process, and Vine Copula
Axioms
; 11(8):375, 2022.
Article
in English
| ProQuest Central | ID: covidwho-2023120
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.
Mathematics; oil prices; S&P 500; multivariate time series; Gaussian process model; vine copula; Bayesian variable selection; functional principal component analysis; nonlinear principal component analysis; Stock exchanges; Regression; Deep learning; Random variables; Principal components analysis; Forecasting; Modelling; Crude oil; Normal distribution; Securities markets; Multivariate analysis; Gaussian process; Mathematical models; Machine learning; Coronaviruses; Crude oil prices; Pricing; Internet stocks; Statistical methods; Time series; COVID-19; United States--US
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Language:
English
Journal:
Axioms
Year:
2022
Document Type:
Article
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