Exploring the Dynamics of Brent Crude Oil, S&P500 and Bitcoin Prices Amid Economic Instability
In this paper, we mainly investigate three variables from the price volatility point of view: Brent crude oil, S&P500 and Bitcoin (BTCUSD), aiming to underline the impact of price volatility. Brent crude oil accounts for two-thirds of the oil market. Its price volatility has a significant...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10445256/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832582319748153344 |
|---|---|
| author | Adela Bara Irina Alexandra Georgescu Simona-Vasilica Oprea Marian Pompiliu Cristescu |
| author_facet | Adela Bara Irina Alexandra Georgescu Simona-Vasilica Oprea Marian Pompiliu Cristescu |
| author_sort | Adela Bara |
| collection | DOAJ |
| description | In this paper, we mainly investigate three variables from the price volatility point of view: Brent crude oil, S&P500 and Bitcoin (BTCUSD), aiming to underline the impact of price volatility. Brent crude oil accounts for two-thirds of the oil market. Its price volatility has a significant impact on environmental, transportation, mobility, economic and social aspects that affect sustainability. This paper conducts an extensive examination of the forecasting capabilities of various GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, identifying the most suitable GARCH model for estimating Value at Risk (VaR) for Brent crude oil price. The assessment of VaR for different GARCH models is carried out using Kupiec’s Probability of Failure (POF) test and Christoffersen’s test. This study leverages Brent crude oil data spanning from 2019 to 2023. Additionally, to prove the robustness of the GARCH models, we further consider the West Texas Intermediate (WTI) and Dubai oil prices that are the dominant in the U.S and Asian market. The investigation identifies the TGARCH(1,1) Skewed Student model as the optimal choice among 9 models considered for VaR estimation. The results show that TGARCH Skewed Student model surpasses the other models in the study, proving its superiority in forecasting Brent crude oil price volatility and facilitating VaR estimation. A VaR of 0.044 with a 95% confidence level means that there is a 95% chance that the portfolio will not lose more than 4.4% of its value. By incorporating skewness in addition to volatility asymmetry, the Skewed GARCH-type models provide a more realistic representation of the underlying return distribution. Furthermore, based on some information criteria, the most appropriate GARCH-type model for WTI crude oil is EGARCH(1,1) Skewed Student, with a VaR coverage of 0.39. The most appropriate GARCH-type model for Dubai crude oil is TGARCH(1,1) Skewed Student, with a VaR coverage of 0.17. Both WTI oil and Dubai crude oil have a coverage that exceeds 5%, implying a more conservative approach to estimating potential losses. Furthermore, the unidirectional causalities BTCUSD<inline-formula> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula>BRENT and BTCUSD<inline-formula> <tex-math notation="LaTeX">$\to \text{S}$ </tex-math></inline-formula>&P500 are identified. The results of the current research have practical implications for both importing and exporting countries, policy makers and investors. For companies in the oil sector, VaR informs operational decisions, such as production levels, capital expenditure and inventory management, by providing insights into market risk. Moreover, understanding the risks associated with oil aids in long-term strategic planning. |
| format | Article |
| id | doaj-art-8fdbce04bc534adda2462acad1b75f16 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8fdbce04bc534adda2462acad1b75f162025-01-30T00:01:12ZengIEEEIEEE Access2169-35362024-01-0112313663138510.1109/ACCESS.2024.337002910445256Exploring the Dynamics of Brent Crude Oil, S&P500 and Bitcoin Prices Amid Economic InstabilityAdela Bara0https://orcid.org/0000-0002-0961-352XIrina Alexandra Georgescu1https://orcid.org/0000-0002-8536-5636Simona-Vasilica Oprea2https://orcid.org/0000-0002-9005-5181Marian Pompiliu Cristescu3https://orcid.org/0000-0003-3638-4379Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Bucharest, RomaniaDepartment of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Bucharest, RomaniaDepartment of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Bucharest, RomaniaFaculty of Economic Sciences, Lucian Blaga University of Sibiu, Sibiu, RomaniaIn this paper, we mainly investigate three variables from the price volatility point of view: Brent crude oil, S&P500 and Bitcoin (BTCUSD), aiming to underline the impact of price volatility. Brent crude oil accounts for two-thirds of the oil market. Its price volatility has a significant impact on environmental, transportation, mobility, economic and social aspects that affect sustainability. This paper conducts an extensive examination of the forecasting capabilities of various GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, identifying the most suitable GARCH model for estimating Value at Risk (VaR) for Brent crude oil price. The assessment of VaR for different GARCH models is carried out using Kupiec’s Probability of Failure (POF) test and Christoffersen’s test. This study leverages Brent crude oil data spanning from 2019 to 2023. Additionally, to prove the robustness of the GARCH models, we further consider the West Texas Intermediate (WTI) and Dubai oil prices that are the dominant in the U.S and Asian market. The investigation identifies the TGARCH(1,1) Skewed Student model as the optimal choice among 9 models considered for VaR estimation. The results show that TGARCH Skewed Student model surpasses the other models in the study, proving its superiority in forecasting Brent crude oil price volatility and facilitating VaR estimation. A VaR of 0.044 with a 95% confidence level means that there is a 95% chance that the portfolio will not lose more than 4.4% of its value. By incorporating skewness in addition to volatility asymmetry, the Skewed GARCH-type models provide a more realistic representation of the underlying return distribution. Furthermore, based on some information criteria, the most appropriate GARCH-type model for WTI crude oil is EGARCH(1,1) Skewed Student, with a VaR coverage of 0.39. The most appropriate GARCH-type model for Dubai crude oil is TGARCH(1,1) Skewed Student, with a VaR coverage of 0.17. Both WTI oil and Dubai crude oil have a coverage that exceeds 5%, implying a more conservative approach to estimating potential losses. Furthermore, the unidirectional causalities BTCUSD<inline-formula> <tex-math notation="LaTeX">$\to $ </tex-math></inline-formula>BRENT and BTCUSD<inline-formula> <tex-math notation="LaTeX">$\to \text{S}$ </tex-math></inline-formula>&P500 are identified. The results of the current research have practical implications for both importing and exporting countries, policy makers and investors. For companies in the oil sector, VaR informs operational decisions, such as production levels, capital expenditure and inventory management, by providing insights into market risk. Moreover, understanding the risks associated with oil aids in long-term strategic planning.https://ieeexplore.ieee.org/document/10445256/BacktestingBitcoinBrent crude oilGARCH modelprice volatilityS&P500 |
| spellingShingle | Adela Bara Irina Alexandra Georgescu Simona-Vasilica Oprea Marian Pompiliu Cristescu Exploring the Dynamics of Brent Crude Oil, S&P500 and Bitcoin Prices Amid Economic Instability IEEE Access Backtesting Bitcoin Brent crude oil GARCH model price volatility S&P500 |
| title | Exploring the Dynamics of Brent Crude Oil, S&P500 and Bitcoin Prices Amid Economic Instability |
| title_full | Exploring the Dynamics of Brent Crude Oil, S&P500 and Bitcoin Prices Amid Economic Instability |
| title_fullStr | Exploring the Dynamics of Brent Crude Oil, S&P500 and Bitcoin Prices Amid Economic Instability |
| title_full_unstemmed | Exploring the Dynamics of Brent Crude Oil, S&P500 and Bitcoin Prices Amid Economic Instability |
| title_short | Exploring the Dynamics of Brent Crude Oil, S&P500 and Bitcoin Prices Amid Economic Instability |
| title_sort | exploring the dynamics of brent crude oil s x0026 p500 and bitcoin prices amid economic instability |
| topic | Backtesting Bitcoin Brent crude oil GARCH model price volatility S&P500 |
| url | https://ieeexplore.ieee.org/document/10445256/ |
| work_keys_str_mv | AT adelabara exploringthedynamicsofbrentcrudeoilsx0026p500andbitcoinpricesamideconomicinstability AT irinaalexandrageorgescu exploringthedynamicsofbrentcrudeoilsx0026p500andbitcoinpricesamideconomicinstability AT simonavasilicaoprea exploringthedynamicsofbrentcrudeoilsx0026p500andbitcoinpricesamideconomicinstability AT marianpompiliucristescu exploringthedynamicsofbrentcrudeoilsx0026p500andbitcoinpricesamideconomicinstability |