The impact of geopolitical risk on the behavior of oil prices and freight rates

Monge, Manuel; Romero, M. F.; Gil-Alana, Luís A.

doi:10.1016/j.energy.2023.126779

Cited by 34 publications

(11 citation statements)

References 70 publications

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“…Several studies have been conducted to predict energy prices in situations where global uncertainty is maximized, such as the COVID pandemic and the subsequent Russia-Ukraine conflict. In particular, various advanced techniques have been applied to investigate the impact of current situations on crude oil price volatility, including empirical mode decomposition (EMD) [70], geometric Brownian motion (GBM) [71], a mixture of GBM and a Poisson process [72], and a fractional integration method [73]. In the future, the development of energy price prediction systems, including those for crude oil and NG, is expected to incorporate not only the JKM prediction model proposed in this study but also the four methodologies mentioned earlier.…”

Section: Discussionmentioning

confidence: 99%

A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices

Yang

Choi

2023

Energies

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The ongoing Russia–Ukraine conflict has exacerbated the global crisis of natural gas supply, particularly in Europe. During the winter season, major importers of liquefied natural gas (LNG), such as South Korea and Japan, were directly affected by fluctuating spot LNG prices. This study aimed to use machine learning (ML) to predict the Japan Korea Marker (JKM), a spot LNG price index, to reduce price fluctuation risks for LNG importers such as the Korean Gas Corporation (KOGAS). Hence, price prediction models were developed based on long short-term memory (LSTM), artificial neural network (ANN), and support vector machine (SVM) algorithms, which were used for time series data prediction. Eighty-seven variables were collected for JKM prediction, of which eight were selected for modeling. Four scenarios (scenarios A, B, C, and D) were devised and tested to analyze the effect of each variable on the performance of the models. Among the eight variables, JKM, national balancing point (NBP), and Brent price indexes demonstrated the largest effects on the performance of the ML models. In contrast, the variable of LNG import volume in China had the least effect. The LSTM model showed a mean absolute error (MAE) of 0.195, making it the best-performing algorithm. However, the LSTM model demonstrated a decreased in performance of at least 57% during the COVID-19 period, which raises concerns regarding the reliability of the test results obtained during that time. The study compared the ML models’ prediction performances with those of the traditional statistical model, autoregressive integrated moving averages (ARIMA), to verify their effectiveness. The comparison results showed that the LSTM model’s performance deviated by an MAE of 15–22%, which can be attributed to the constraints of the small dataset size and conceptual structural differences between the ML and ARIMA models. However, if a sufficiently large dataset can be secured for training, the ML model is expected to perform better than the ARIMA. Additionally, separate tests were conducted to predict the trends of JKM fluctuations and comprehensively validate the practicality of the ML models. Based on the test results, LSTM model, identified as the optimal ML algorithm, achieved a performance of 53% during the regular period and 57% d during the abnormal period (i.e., COVID-19). Subject matter experts agreed that the performance of the ML models could be improved through additional studies, ultimately reducing the risk of price fluctuations when purchasing spot LNG.

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Section: Discussionmentioning

confidence: 99%

A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices

Yang

Choi

2023

Energies

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“…As the preferred data source for empirical analysis, the index is considered to be the most comprehensive and trustworthy. It is widely used in research on global finance, energy and other fields (Liu et al 2019; Su et al 2020; Salisu et al 2022; Jin et al 2023; Monge et al 2023; Zhao 2023; Wang et al 2023b). The data of

italicEPU

and GPR index are from http://www.policyuncertainty.com.…”

Section: Data Source and Descriptive Analysismentioning

confidence: 99%

Economic policy uncertainty and geopolitical risk: evidence from China and Southeast Asia

Wang,

Liu

et al. 2023

Asian-Pac Economic Lit

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This paper investigates the connectedness between China's economic policy uncertainty (EPU) and the geopolitical risk (GPR) of the countries in the Association of Southeast Asian Nations (ASEAN) using quantile time‐varying parameter vector autoregression. The empirical results indicate that China's EPU becomes a net risk transmitter at the median quantile, demonstrating that ASEAN countries' GPR is vulnerable to China's economic shocks. Furthermore, extreme events such as COVID‐19 amplify the connection between EPU and GPR, leading to a higher total connectedness index value. Moreover, the pairwise spillover effect analysis reflects that the risk launcher effect of China's EPU is longer and more prominent for Thailand and the Philippines than for Malaysia and Indonesia, which enriches the hypothesis. The contributions are as follows. This paper selects emerging China as the research object and first discusses its economic influence on neighbouring countries. In addition, fully analysing the heterogeneous spillover effects of EPU on the GPR of different countries makes our suggestions diversified and practical. Therefore, improving recognisable ability and institutional quality will promote ASEAN countries to mitigate their vulnerability to China's EPU shocks. Meanwhile, China's government should quickly respond to external uncertainties, prudently adjust economic policies, and avoid large swings of EPU volatility.

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“…Finally, the research carried out by [56][57][58][59] suggests that misleading results will be found if we apply a typical cross-correlation to study statistical relationships between two multifractal time series.…”

Section: Wavelet Analysismentioning

confidence: 99%

Consumer Sentiment and Luxury Behavior in the United States before and after COVID-19: Time Trends and Persistence Analysis

Marcos Ceron,

Monge

2023

Mathematics

Self Cite

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This paper analyzes the stochastic properties of consumer sentiment to understand how they affected the luxury sector in the United States before and after COVID-19. The results were derived using fractional integration methodologies and suggest that, before the pandemic episode, both variables were expected to be mean reverting and the shocks were transitory, having similar behavior. However, after the appearance of COVID-19, results suggest that consumer sentiment recovered before the luxury sector. Results from the use of cointegration methodologies show that the effects of COVID-19 disappeared in the short-run. Finally, the sentiment of consumers acts as a leading indicator of the behavior of the luxury sector according to wavelet analysis. Thus, an increase in consumer sentiment implies an increase of 3.6% in the luxury sector.

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The impact of geopolitical risk on the behavior of oil prices and freight rates

Cited by 34 publications

References 70 publications

A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices

A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices

Economic policy uncertainty and geopolitical risk: evidence from China and Southeast Asia

Consumer Sentiment and Luxury Behavior in the United States before and after COVID-19: Time Trends and Persistence Analysis

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