Refinements of Barndorff-Nielsen and Shephard model: an analysis of crude oil price with machine learning

TL;DR

This paper enhances the BN-S model for crude oil prices by integrating machine learning techniques, improving its efficiency and ability to capture long-range dependence in market dynamics.

Contribution

It introduces a machine learning-based refinement of the BN-S model that reduces complexity and improves long-range dependence modeling in crude oil prices.

Findings

Refined model shows better long-range dependence capture.

Fewer parameters needed compared to traditional models.

Empirical validation confirms improved performance.

Abstract

A commonly used stochastic model for derivative and commodity market analysis is the Barndorff-Nielsen and Shephard (BN-S) model. Though this model is very efficient and analytically tractable, it suffers from the absence of long range dependence and many other issues. For this paper, the analysis is restricted to crude oil price dynamics. A simple way of improving the BN-S model with the implementation of various machine learning algorithms is proposed. This refined BN-S model is more efficient and has fewer parameters than other models which are used in practice as improvements of the BN-S model. The procedure and the model show the application of data science for extracting a "deterministic component" out of processes that are usually considered to be completely stochastic. Empirical applications validate the efficacy of the proposed model for long range dependence.