Volatility Modelling in Crude Oil and Natural Gas Prices

Saltık, Ömür; Değirmen, Süleyman; Ural, Mert

doi:10.1016/s2212-5671(16)30219-2

Cited by 9 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To capture volatility in the series the symmetric GARCH model was employed in this study. The volatility in a return series is more accurately represented by the symmetric GARCH model than the ARCH model (Saltik, Degirmen, & Ural, 2016). The GARCH (p,q) model is expressed and defined as follows:…”

Section: Garch Modelmentioning

confidence: 99%

Forecasting volatility in oil returns using asymmetric GARCH models: evidence from Tanzania

Mbwambo¹,

Letema²

2023

IJRBS

View full text Add to dashboard Cite

Crude oil is, without a doubt, one of the most significant commodities in the modern world. The highly contagious coronavirus, the conflict between Ukraine and Russia, and not to mention the unusual turn of events worldwide have all significantly impacted crude oil prices. Since oil is required for all critical economic activities, such as production and transportation, a forecast for crude oil prices is essential. Using a range of GARCH models at such an intense time, this study attempted to close this gap by forecasting crude oil volatility. To forecast the returns of Brent crude oil prices from January 2002 to February 2022, this study uses a family of GARCH models. In the respective family of models, GJRGARCH (1,1) was the most effective in predicting the volatility of crude oil prices. The GJRGARCH model was chosen since it had a higher likelihood value and a lower information criteria value. A diagnostic check was done to evaluate the produced model further to ensure that the proposed model was good enough for forecasting crude oil volatility. The study suggests employing the GJRGARCH technique to predict future fluctuations in exceptional circumstances.

show abstract

Section: Garch Modelmentioning

confidence: 99%

Forecasting volatility in oil returns using asymmetric GARCH models: evidence from Tanzania

Mbwambo¹,

Letema²

2023

IJRBS

View full text Add to dashboard Cite

show abstract

“…So far, the existing literature has mostly focused on the relations between NG and other commodities or securities (see, for instance, [38][39][40][41][42][43][44][45][46][47]), as well as on modeling price volatility (e.g., [48][49][50][51][52][53][54][55]), demand and supply (e.g., [56][57][58][59][60][61][62][63][64]), spot prices (e.g., [65][66][67][68][69][70][71][72][73][74][75][76]) or futures prices of individual contracts (e.g., [77][78][79]). Relatively less attention has been paid to NG futures prices term structure modeling and forecasting and only a few studies have partly tackled the issues we are dealing with.…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling

Castello

Resta

2023

Energies

View full text Add to dashboard Cite

This work studies the term structure dynamics in the natural gas futures market, focusing on the Dutch Title Transfer Facility (TTF) daily futures prices. At first, using the whole dataset, we compared the in-sample fitting performance of three models: the four-factor dynamic Nelson–Siegel–Svensson (4F-DNSS) model, the five-factor dynamic De Rezende–Ferreira (5F-DRF) model, and the B-spline model. Our findings suggest that B-spline is the method that achieves the best in-line fitting results. Then, we turned our attention to forecasting, using data from 20 January 2011 to 13 May 2022 as the training set and the remaining data, from 16 May to 13 June 2022, for day-ahead predictions. In this second part of the work we combined the above mentioned models (4F-DNSS, 5F-DRF and B-spline) with a Nonlinear Autoregressive Neural Network (NAR-NN), asking the NAR-NN to provide parameter tuning. All the models provided accurate out-of-sample prediction; nevertheless, based on extensive statistical tests, we conclude that, as in the previous case, B-spline (combined with an NAR-NN) ensured the best out-of-sample prediction.

show abstract

“…As such, the ability to anticipate changes in crude oil prices is pivotal for informed decision-making by governments, corporations, and investors. This is a challenging task because of crude oil's high volatility (Saltik et al 2016), making prices susceptible to sudden fluctuations driven by multiple factors. Developing prediction models for crude oil prices has been the focus of some researchers.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Deep Learning Approach for Crude Oil Price Prediction

Aldabagh,

Zheng,

Mukkamala

2023

JRFM

View full text Add to dashboard Cite

Crude oil is one of the world’s most important commodities. Its price can affect the global economy, as well as the economies of importing and exporting countries. As a result, forecasting the price of crude oil is essential for investors. However, crude oil price tends to fluctuate considerably during significant world events, such as the COVID-19 pandemic and geopolitical conflicts. In this paper, we propose a deep learning model for forecasting the crude oil price of one-step and multi-step ahead. The model extracts important features that impact crude oil prices and uses them to predict future prices. The prediction model combines convolutional neural networks (CNN) with long short-term memory networks (LSTM). We compared our one-step CNN–LSTM model with other LSTM models, the CNN model, support vector machine (SVM), and the autoregressive integrated moving average (ARIMA) model. Also, we compared our multi-step CNN–LSTM model with LSTM, CNN, and the time series encoder–decoder model. Extensive experiments were conducted using short-, medium-, and long-term price data of one, five, and ten years, respectively. In terms of accuracy, the proposed model outperformed existing models in both one-step and multi-step predictions.

show abstract

Volatility Modelling in Crude Oil and Natural Gas Prices

Cited by 9 publications

References 21 publications

Forecasting volatility in oil returns using asymmetric GARCH models: evidence from Tanzania

Forecasting volatility in oil returns using asymmetric GARCH models: evidence from Tanzania

A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling

A Hybrid Deep Learning Approach for Crude Oil Price Prediction

Contact Info

Product

Resources

About