Seasonality in crude oil returns

Quayyoum, Sobia; Khan, Mushtaq Hussain; Shah, Syed Zulfiqar Ali; Simonetti, Biagio; Matarazzo, Michela

doi:10.1007/s00500-019-04329-0

Cited by 9 publications

(6 citation statements)

References 61 publications

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“…Over the weekends, the markets are inactive, but news still keeps coming out and affects the markets on Monday when they open. These ndings differ from the research conducted by Quayyoum et al (2020), which reported that the Monday effect was present in the Brent market instead of the WTI market. This discrepancy could be due to the different time frames of the studies or because Brent is used as a benchmark by OPEC countries, most of which work on Saturdays and Sundays with only Friday being a day off, thus the release of news on these days does not have an impact on Monday.…”

Section: Resultscontrasting

confidence: 99%

“…The results suggest that there is no signi cant January effect, but there is a signi cant November and December effect for WTI crude oil returns only. This is different from previous ndings byQuayyoum et al (2020), who reported November and December effects for both WTI and Brent crude oil markets. Meanwhile, Olowe (2011) reported that there is no monthly seasonal effect in Brent crude oil returns, which is consistent with the results of this study.…”

contrasting

confidence: 99%

“…One such study is by Quayyoum (2020), who investigated the month-of-the-year effect and day-of-the-week effect for December 1987 to January 2016 for Brent and WTI crude oil returns. The study reported a negative Monday effect and the highest returns for Thursday.…”

Section: Seasonality In Crude Oil Pricesmentioning

confidence: 99%

“…All weekdays except Monday have positive returns. Previous studies byQuayyoum et al (2020) reported Wednesday, Thursday, and Friday effects, and Shih (2011) reported the Wednesday effect for oil markets.…”

mentioning

confidence: 92%

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Understanding the Seasonality in Crude Oil Returns for WTI and Brent

Khan

Saha

Ekundayo

2023

Preprint

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There is a lack of research on the seasonal anomalies in the crude oil market. This study aims to fill that gap by examining the effect of days-of-the-week and month-of-the-year on crude oil price returns. To achieve this, West Texas Intermediate (WTI) daily and monthly data from January 1986 to July 2022 and Brent data from May 1987 to July 2022 were analyzed. The regression analysis utilizing dummy variables for days and months was used to determine the effect of days-of-the-week and month-of-the-year on crude oil price returns. The results reveal that Monday has the lowest average crude oil price returns for both WTI and Brent, while Thursday has the highest average returns. In addition, November and December have the lowest average returns for both WTI and Brent, while May has the highest average returns. The effect of Thursday and Friday was observed in the Brent market, while the effect of Monday, Wednesday, Thursday, and Friday was noticed in the WTI crude oil market. The results show that there is no month-of-the-year effect in Brent, but November and December have an effect on the WTI crude oil market.

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Section: Resultscontrasting

confidence: 99%

contrasting

confidence: 99%

Section: Seasonality In Crude Oil Pricesmentioning

confidence: 99%

mentioning

confidence: 92%

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Understanding the Seasonality in Crude Oil Returns for WTI and Brent

Khan

Saha

Ekundayo

2023

Preprint

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“…One of the major financial markets to be addressed is the trend of oil return and price in the future, which significantly impacts the global economy. Some recent studies have demonstrated the importance of crude oil price or return prediction (Deng et al, 2020;Quayyoum et al, 2020;Wang and Wang, 2020;Zhang and Wang, 2021). This paper aims to forecast Brent Crude Oil's return accurately by using deep learning methods.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based methods for forecasting Brent Crude Oil return considering COVID-19 pandemic effect

Khodaee

Sajadi

Hajizadeh

et al. 2022

Preprint

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Forecasting return and profit is a primary concern for financial practitioners. It is even more critical when it comes to forecasting energy market returns. This research attempts to propose an effective method for predicting the Brent Crude Oil return, which results in remarkable performance compared to the well-known models in the return prediction. The proposed model is a hybrid model based on LSTM and CNN networks where ARIMA and GARCH outputs are used as features along with return lags, price, and macroeconomic variables for training the models, resulting in significant improvement in the model's performance. According to the obtained results, our proposed model performs better than other models, including ANN, PCA-ANN, LSTM, CNN. In the second part of this study, by considering the spread of the COVID-19 and its impact on the financial markets, we present a precise LSTM model that can reflect this disease's impact on the Brent Crude Oil return. This paper uses the significance test and correlation measures to show similarity between the series of Brent Crude Oil during the SARS period and COVID-19; after that, we use SARS period data along the COVID-19 to train the LSTM. The results demonstrate that the proposed LSTM model, tuned by the SARS data, can better predict the Brent Crude Oil return during the COVID19 pandemic.

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