Hidden causality between oil prices and exchange rates

This work investigates the interactions between oil prices and exchange rates of 6 typical oil importers (China, Japan, and India) and exporters (Canada, Russia, and Saudi Arabia) from 2006 to 2022. We employ a novel method to capture their causal interactions, namely pattern causality, and compare the results to that based on the volatility spillover method. The empirical analysis supports most existing findings that oil prices are bidirectional correlated with exchange rates. However, unlike previous studies that only investigate positive and negative causalities, we highlight dark causality as a more complex interaction. Moreover, dark causality suggests that successive increases (decreases) in oil prices tend to drive the exchange rates of oil exporters to act in an oscillatory manner rather than in a purely positive or opposite trend, and vice versa. Furthermore, we also reveal that dark causality shows dominance during crises, e.g., the global financial crisis, the European debt crisis, the epidemic of COVID-19, and the Russia-Ukraine conflict. Revealing three types of causalities between oil prices and exchange rates helps policymakers develop more diversified macroeconomic policies. Moreover, the newly identified dark causality can be a useful indicator for investors to risk management. © 2023

Can the green bond market enter a new era under the fluctuation of oil price?

This paper investigates how oil price (OP) influences the prospects of green bonds by utilising the quantile-onquantile (QQ) method and researching the interactions between OP and green bond index (GBI) from 2011:M1 to 2021:M11. We find that impacts from OP on the GBI are positive in the short run. The positive effects indicate that high OP can promote the development of the green bond market, indicating that green bonds can be considered an asset to avoid OP shocks. However, in the medium and long term, there is a negative impact due to the oversupply of the oil market and the increase in green energy industry profits. These results are identical to the supply and demand-based correlation model of green bonds and oil price, which underlines a specific effect of OP on GBI. The GBI effect on OP is consistently positive across all quantiles. It indicates that green bonds cannot be considered efficient measures to alleviate the oil crisis due to the instability of the Middle East COVID-19 and the small scale of green bonds. The issuers of green bonds can make decisions based on OP. Understanding the relationship between OP and GBI is also beneficial for investors. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

On the volatility of WTI crude oil prices: A time-varying approach with stochastic volatility

This study investigates the impacts of crude oil-market-specific fundamental factors and financial indicators on the realized volatility of West Texas Intermediate (WTI) crude oil price. A time-varying parameter vector autoregression model with stochastic volatility (TVP-VAR-SV) is applied to weekly data series spanning January 2008 to October 2021. It is found that the WTI oil price volatility responds positively to a shock in oil production, oil inventories, the US dollar index, and VIX but negatively to a shock in the US economic activity. The response to the EPU index was initially positive and then turned slightly negative before fading away. The VIX index has the most significant effect. Furthermore, the time-varying nature of the response of the WTI realized oil price volatility is evident. Extreme effects materialize during economic recessions and crises, especially during the COVID-19 pandemic. The findings can improve our understanding of the time-varying nature and determinants of WTI oil price volatility. © 2022

The nexus between oil and airline stock returns: Does time frequency matter?

This paper investigates the relationship between oil and airline stock returns under different time frequencies. First, we propose an Autoregressive moving average model with mixed frequency exogenous variable to analyse the different impacts of oil on airline stock returns on daily, weekly, and monthly basis. We consistently find a negative oil-airline stock return nexus on a daily basis, but a positive relationship on a weekly basis. While the former supports the economic-based channel, the latter is in line with the market inertia channel. Our findings help explain mixed results reported in the literature. Further, our time frequency connectedness analysis shows that the economic-based channel dominates the market inertia channel since the connectedness is more pronounced in the short-run compared to the medium- and long-run. Our block connectedness results highlight that business models of airline firms can play a significant role in affecting the connectedness, in which the low-cost airlines are more sensitive to the oil price changes. It is worth noting that there are distinguished drivers of the oil-airline stock return nexus in different time frequencies. The drivers also vary between the Global Financial Crisis and the COVID-19 pandemic. Our results are consistent under a battery of robustness checks and deliver important implications to investors, portfolio managers, and executives of airline firms. © 2022 Elsevier B.V.

Modelling the impact of disease outbreaks on the international crude oil supply chain using Random Forest regression

PurposeThe crude oil supply chain (COSC) is one of the most complex and largest supply chains in the world. It is easily vulnerable to extreme events. Recently, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (often known as COVID-19) pandemic created a massive imbalance between supply and demand which caused significant price fluctuations. The purpose of this study is to explore the influential factors affecting the international COSC in terms of consumption, production and price. Furthermore, it develops a model to predict the international crude oil price during disease outbreaks using Random Forest (RF) regression. Design/methodology/approachThis study uses both qualitative and quantitative approaches. A qualitative study is conducted using a literature review to explore the influential factors on COSC. All the data are extracted from Web sources. In addition to COVID-19, four other diseases are considered to optimize the accuracy of predictive results. A principal component analysis is deployed to reduce the number of variables. A forecasting model is developed using RF regression. FindingsThe findings of the qualitative analysis characterize the factors that influence international COSC. The findings of quantitative analysis emphasize that production and consumption have a higher contribution to the variance of the data set. Also, this study found that the impact caused to crude oil price varies with the region. Most importantly, the model introduced using the RF technique provides a high predictive ability in short horizons such as infectious diseases. This study delivers future directions and insights to researchers and practitioners to expand the study further. Originality/valueThis is one of the few available pieces of research which uses the RF method in the context of crude oil price forecasting. Additionally, this study examines international COSC in the events of emergencies, specifically disease outbreaks using machine learning techniques.

Risk diffusion of international oil trade cuts: A network-based dynamics model

International trade relations between countries have enabled the integration of global oil trade while also creating a platform for the risk diffusion of oil supply cuts. The global energy supply shortages sparked by the Covid-19 pandemic has made the problem even more pronounced. This study develops a network-based dynamics model based on the modified bootstrap percolation theory to simulate the cascading diffusion of oil supply shortages. We assess the destructive effects of exporting countries’ cuts in oil exports on global oil trade relations and the vulnerability of importing countries when they encounter oil supply shortages. The cascading diffusion of oil supply breakdown occurs less frequently from 2015 to 2019, proving that the entire network’s antirisk capacity strengthens over time. The coronavirus pandemic has impaired the robustness of the oil trade network in 2020. OPEC’s export influence has continued to decline in recent years. The diversified evolution of the oil supply is conducive to the stability of the oil trade economy. This paper analyzes the risk propagation mechanism in the global oil trade and conducts a case study. The results have a specific early warning effect on regional oil supply risks.

Oil price shocks and the term structure of the US yield curve: a time-frequency analysis of spillovers and risk transmission.

This paper investigates the influence of oil demand, oil supply, and risk-driven shocks on the yield curve in the US between 1995 and 2020. The US term-structure shape is modeled by three structural factors, the level, slope, and curvature. Their empirical analysis is performed according to the Diebold-Li modified variant of the widely used Nelson-Siegel model. The technique of wavelet analysis allows investigating the interrelation of shocks in oil prices and the US yield curve along time and frequency domains, simultaneously. We report on low, medium, and high coherence zones, relative to the oil price movements and the changes in the three yield-curve factors. The low coherence intervals indicate the potential for the three latent factors to be used for creating diversification strategies capable of hedging adverse dynamics in the oil market, potentially workable through global crises. We document the variability of dynamic patterns observable for the US sovereign yield factors on per-type-of-shock basis, evidencing the potential role of the US sovereign debt investments for designing cross-asset hedge strategies for commodity and fixed-income markets.

Does the source of oil price shocks matter for the systemic risk?

This paper investigates the impact of different oil price shocks on systemic risk under different market conditions. We show that the negative impact of negative oil price shocks on systemic risk is greater than the positive impact of positive oil price shocks. Systemic risk is always negatively affected by oil-specific demand shocks but positively affected by oil supply shocks when the market is under medium and low systemic risk levels. By testing the effect of crises, we find that the influence of positive and negative oil price shocks on systemic risk was declined due to the COVID-19 pandemic. © 2022 Elsevier B.V.