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 analys...
Main Authors: | Jong-Min Kim, Hope H. Han, Sangjin Kim |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-07-01
|
Series: | Axioms |
Subjects: | |
Online Access: | https://www.mdpi.com/2075-1680/11/8/375 |
Similar Items
-
Vine copulas structures modeling on Russian stock market
by: Eugeny Yu. Shchetinin
Published: (2019-12-01) -
Modeling Dependence with C- and D-Vine Copulas: The R Package CDVine
by: Eike Christian Brechmann, et al.
Published: (2013-11-01) -
Stochastic Modeling of Hydroclimatic Processes Using Vine Copulas
by: George Pouliasis, et al.
Published: (2021-08-01) -
MATVines: A vine copula package for MATLAB
by: Maximilian Coblenz
Published: (2021-06-01) -
Modeling Asymmetric Dependence Structure of Air Pollution Characteristics: A Vine Copula Approach
by: Mohd Sabri Ismail, et al.
Published: (2024-02-01)