Refinements of Barndorff-Nielsen and Shephard Model: An Analysis of Crude Oil Price with Machine Learning

  title={Refinements of Barndorff-Nielsen and Shephard Model: An Analysis of Crude Oil Price with Machine Learning},
  author={Indranil Sengupta and William E. Nganje and Erik D. Hanson},
  journal={Annals of Data Science},
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… 

Hedging and machine learning driven crude oil data analysis using a refined Barndorff–Nielsen and Shephard model

A refined Barndorff-Nielsen and Shephard (BN-S) model is implemented to find an optimal hedging strategy for commodity markets and the resulting model performs much better than the classical BN-S model.

Selection of Machine Learning Models for Oil Price Forecasting: Based on the Dual Attributes of Oil

It is found that reducing the dimension of the data can improve the accuracy of the model and the applicability of RNN and LSTM models and can most successfully capture the nonlinear characteristics of crude oil prices.

Analysis of stock index with a generalized BN-S model: an approach based on machine learning and fuzzy parameters

The results show that the new model, where fuzzy parameters are incorporated, can incorporate the long-term dependence in the classical Barndorff-Nielsen and Shephard model and effectively captures the stochastic dynamics of stock index time series.

Sequential Hypothesis Testing in Machine Learning, and Crude Oil Price Jump Size Detection

ABSTRACT In this paper, we present a sequential hypothesis test for the detection of the distribution of jump size in Lévy processes. Infinitesimal generators for the corresponding log-likelihood

Generalized BN-S Model Application: Analysis of Stock Index Option Price Volatility Based on Machine Learning and Fuzzy Parameters

The results show that the new model in a fuzzy environment solves the long-term dependence problem of the classic model with fewer parameter changes, and effectively analyzes the random dynamic characteristics of stock index option price time series.

Sequential hypothesis testing in machine learning driven crude oil jump detection

A sequential hypothesis test for the detection of general jump size distrubution and infinitesimal generators for the corresponding log-likelihood ratios are presented and analyzed.

A data-science-driven short-term analysis of Amazon, Apple, Google, and Microsoft stocks

A data-science-driven technique is executed that makes shortterm forecasts dependent on the price trends of current stock market data and incorporate it to improve the underlying stochastic model.

First Exit-Time Analysis for an Approximate Barndorff-Nielsen and Shephard Model with Stationary Self-Decomposable Variance Process

In this paper, an approximate version of the Barndorff-Nielsen and Shephard model, driven by a Brownian motion and a L\'evy subordinator, is formulated. The first-exit time of the log-return process

A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting

Conclusively SVM and ANN played prominent roles in tackling these issue to an extent and can be enhanced with their integration with other novel techniques resulting in hybrid methodologies and will lead students, researchers and financial enthusiasts to more potent approaches for Stock forecasting.

Multimodal price prediction

This research proposes five deep learning models to predict the price range of a product, one unimodal and four multimodal systems, and in proposed methods, convolutional neural network is an infrastructure.



Barndorff-Nielsen and Shephard model: oil hedging with variance swap and option

In this paper the Barndorff-Nielsen and Shephard (BN-S) model is implemented to find an optimal hedging strategy for the oil commodity from the Bakken, a new region of oil extraction that is

Volatility and Variance Swap Using Superposition of the Barndorff-Nielsen and Shephard type Lévy Processes

The main goal of this paper is to model variance and volatility swap using superposition of Barndorff-Nielsen and Shephard (BN-S) type models. In particular, in this paper we propose superposition of

Machine learning approach for crude oil price prediction with Artificial Neural Networks-Quantitative (ANN-Q) model

This paper will discuss the first part of the research, focusing on to (i) the development of Hierarchical Conceptual (HC) model and (ii) theDevelopment of Artificial Neural Networks-Quantitative (ANN-Q) model.

Analysis of variance based instruments for Ornstein–Uhlenbeck type models: swap and price index

In this paper a couple of variance dependent instruments in the financial market are studied. Firstly, a number of aspects of the variance swap in connection to the Barndorff-Nielsen and Shephard

Merton's portfolio optimization problem in a Black and Scholes market with non‐Gaussian stochastic volatility of Ornstein‐Uhlenbeck type

We study Merton's classical portfolio optimization problem for an investor who can trade in a risk‐free bond and a stock. The goal of the investor is to allocate money so that her expected utility


In this paper, a class of generalized Barndorff-Nielsen and Shephard (BN–S) models is investigated from the viewpoint of derivative asset analysis. Incompleteness of this type of markets is studied

Crude Oil Prices Forecasting: Time Series vs. SVR Models

  • X. He
  • Business
    Journal of International Technology and Information Management
  • 2018
This research explores the weekly crude oil price data from U.S. Energy Information Administration over the time period 2009 2017 to test the forecasting accuracy by comparing time series models such

Non‐Gaussian Ornstein–Uhlenbeck‐based models and some of their uses in financial economics

Non‐Gaussian processes of Ornstein–Uhlenbeck (OU) type offer the possibility of capturing important distributional deviations from Gaussianity and for flexible modelling of dependence structures.