This paper examines the reaction of energy inflation to geopolitical risks in the European Economic Area between 1990 and 2015. The paper is an attempt to reveal the time-varying characteristics of the relationship by applying the nonparametric time-varying coefficient panel data model with fixed effects. In addition, to further reveal potential tail effects that may not have been captured by conditional mean-based regressions, the method of moments quantile regression was also employed. Conclusions reached on the basis of the findings are as follows; first, as these European countries get exposed to geopolitical tensions, it is expected that energy prices will surge. Second, the study shows that the ability of geopolitical risk to trigger energy inflation in recent times is not as powerful as it used to be. Third, results reveal that countries that are already better at managing inflation, when exposed to more geopolitical risks, experience smaller increases in energy inflation. On the other hand, countries that are already struggling with inflation control, when exposed to more geopolitical risk, experience even greater energy inflation. Overall, the study findings lead us to the conclusion that transitioning from non-renewable to renewable energy use is one channel through which the sampled countries can battle the energy inflation which geopolitical risks trigger. A sound macroeconomic policy for inflation control is a complementary channel through which the same goal can be achieved.
Research Article
Does Geopolitical Risk Trigger Energy Inflation in the European Economic Area? Evidence from a Panel Time-Varying Regression
https://doi.org/10.21203/rs.3.rs-2234181/v1
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European Economic Area
energy inflation
geopolitical risks
time-varying panel analysis
MMQR.
One of the greatest challenges of the energy sector in the European Economic Area is the dependence on energy imports. While the extent to which individual countries within the region rely on energy imports vary, the high level of interconnectivity of the energy markets in the region means that the excessive dependence of some countries is transmitted across the entire region. It is therefore not far-fetched to expect that geopolitical tensions to which countries in the region are exposed might affect energy prices; one might even further expect such impacts to spread across the region. This study therefore seeks to provide answers to two questions about the news-based geopolitical risk index (GPR) constructed by Caldara and Iacoviello (2018). First, the study attempts to reveal whether geopolitical risk affects movements in energy inflation in the European Economic Area. It also attempts to confirm the nature of the relationship over time across quantiles.
Energy price, demand and supply are critical issues that draw the attention of many economic stakeholders for the purpose of safeguarding their economies. Unlike other commodities, energy inflation matters more for the general economy because of its fundamental economic role. Energy price inflation has its source in the rising cost of major energy sources, especially oil, coal and natural gas. According to Rubene and Koester (2021), the price of oil is an outstanding factor in driving energy inflation. The global energy crises experienced in the past have been caused by increase in the price of energy, particularly the price of oil. These have been highly volatile with remarkable upward movements recorded in 2003-04 and 2008-09 (Joëts, 2015). Extreme movements in energy price have huge impacts on economies, while very high energy price signals economic recession (Hamilton, 1983, 2003; Kilian, 2008; Balcilar et al., 2022). For this reason, effective energy policies for economic growth require the identification of drivers of energy price beyond demand and supply.
Energy price movements are determined by exogenous factors (Kilian, 2008), among which are geopolitical events (Antonakakis et al., 2017; Su et al., 2019; Huang et al., 2021; Lee, Olasehinde-Williams & Akadiri, 2021). There are two channels by which geopolitical risk affects energy prices, first is the supply side channel and second is the demand channel (Li et al., 2021). This is based on the premise that commodity prices are subject to the gap between demand and supply; unanticipated geopolitical shocks are therefore reflected in energy prices through their effects on production and energy demand (Su et al., 2019; Olanipekun & Alola, 2020). On the supply side, energy price stability is dependent on producers’ capacity to meet demand (Olanipekun & Alola, 2020). Supply chain disruptions, disruption in production process, dwindling investment and supply uncertainty are part of the economic consequences of geopolitical tensions (Murray, 2018; Bouoiyour et al., 2019; Olanipekun & Alola, 2020). As tensions from military and diplomatic conflicts cause disruption in production and supply channels (Li et al., 2021), there is production loss and inability to expand production capacity amidst rising demand (Olanipekun & Alola, 2020). The energy market is faced with supply shortage or supply uncertainty (Li et al., 2021). For instance, when the major producers of oil in the world are at war amidst steady and increasing demand, supply falls short of demand, thus pushing up the prices of energy commodities (Bouoiyour et al., 2019). Second is the demand channel. Where geopolitical risk leads to disruption in normal economic activities, there is decline in demand for energy due to a halt in the economic activities that require the use of energy (Li et al., 2021; Olanipekun & Alola, 2020). Reduced energy price is implied when energy demand falls below energy supply, while increased energy price is implied when supply falls short of demand.
Increase in energy price has been differentiated from the increase in the prices of other commodities because of the sharp and sustained rise in energy prices that occur at a unique time (Kilian, 2008). This is attributed to the fact that uncertainty puts pressure on major energy prices, which are mainly priced in US dollar and the exchange rate variability (Kisswani & Elian, 2021). For net importers of energy commodities, it puts pressure on their import bills (Haider, Ahmed & Jawed, 2014). These have essential impact on energy prices and on the operations of energy firms, thereby stimulating energy inflation, especially for net importers (Kisswani & Elian, 2021).
Considering the energy security concerns of many countries (Joëts, 2015; Alqahtani & Taillard, 2019), the fundamental position of energy supply also implies that global geopolitical events would continually result in major energy price movements. Not all countries possess resources used in non-renewable energy. According to Murray (2018), key global energy sources are mostly sourced from less politically stable regions. It has become necessary to investigate this topic at this time because of the recurrent geopolitical events that the world has witnessed in recent decades. The most recent was the Russian attack on Ukraine which occurred on February 24, 2022 after several threats by the Russian leader to launch an attack on Ukraine. Also contributing to geopolitical uncertainty have been elections in influential countries, terrorist attacks, wars and tension between countries (see Caldara & Iacoviello, 2018; Bouoiyour et al., 2019). One or more of these events create geopolitical tensions, which have risk impact with economic consequences (Caldara & Iacoviello, 2018). There are studies which have analysed the behaviour of financial markets in response to geopolitical risks (Murray, 2018; Hoque, Soo Wah & Zaidi, 2019; Alqahtani & Klein, 2021; Ghosh, 2021; Khan, Su & Tao, 2021; Kumar, Khalfaoui & Tiwari, 2021). The energy market is also considered vulnerable to uncertainty and geopolitical factors (Antonakakis, Chatziantoniou & Filis, 2014; Antonakakis et al., 2017). However, there are few studies concerned with the energy market dynamics in this regard. A bunch of these focuses on the relationship between oil prices and geopolitical risk.
This study is unique in three ways. First, it expands the understanding of geopolitical risk consequences on energy inflation from a holistic perspective. Oil price alone is not enough to capture energy market because of the new policy direction focusing on renewable energy. Prices of other non-renewable energy such as natural gas, heating oil, wind energy and renewable energy such as electricity and solar should be part of the energy price change for a holistic assessment of the energy market. Second, this study does not assess energy price or its volatility as others have done, it assesses energy inflation. While expressing concerns about the economic impact of adverse geopolitical events happening around the world (Caldara & Iacoviello, 2018), it is expedient to know how this affects energy inflation because of the fundamental role of energy in stirring general inflation. This will suggest to policymakers how to improve resistance to the adverse economic effects caused by instabilities in energy prices. Third, on the empirical front, this study is the first to scrutinize the effect of geopolitical risks on energy inflation, using panel econometric techniques that are able to reveal the time-varying effects as well as the effects across quantiles.
The remainder of the study is sectioned thus: section 2 provides a review of relevant literature, section 3 details the methodology and data employed, section 4 presents the results and section 5 concludes the study.
Geopolitical events have been captured in the geopolitical risk (GPR) index developed by Caldara and Iacoviello (2018). This novel study provides inferences from the theoretical views that economic variables are driven by changes in macroeconomic uncertainty and expectations of adverse events. Notable research attention has been given to the nexus of uncertainty and energy prices, among which are Antonakakis et al. (2014) and Kang and Ratti (2013) on uncertainty and oil price shocks, Joëts (2015) on uncertainty and oil, gas, coal and electricity prices, Olanipekun, Olasehinde-Williams and Akadiri (2019) on gasoline prices and economic policy uncertainty, as well as Scarcioffolo and Etienne (2021) on economic policy uncertainty and oil and natural gas prices, to mention a few. These analyses uphold the premise that the degree of uncertainties and the intensity of market fluctuations determine the behaviour of market players and the pattern of energy price movements under uncertainty. Production of coal, natural gas, crude oil, electricity and renewable energy has also been established to have positive co-movement with uncertainty (Wei, Hu & Chang, 2021). Dogan, Majeed and Luni (2021) analyse the impacts of geopolitical risk and economic uncertainty on natural resources rents among developing economies and observe the negative impact of geopolitical risk on natural resources rents to be consistent.
As an important input factor, energy is connected to geopolitical conflicts. For instance, geopolitical risk stimulates the energy transition process by the increased growth and consumption of renewable energy (Rasoulinezhad et al., 2020; Cai & Wu, 2021). Cai and Wu (2021) show that increased use of renewable energy also tends to reduce geopolitical risk. Rasoulinezhad et al. (2020) provide evidence that the non-renewable energy sources are strongly connected with geopolitical tension. This is connected to the limited nature of the non-renewable energy sources. Crude oil especially plays a sensitive role in energy security (Ji, Zhang & Zhang, 2019). The studies on the relationship between non-renewable energy and geopolitical risk analyse this relationship with a focus on specific energy types, particularly crude oil.
On the relationship between geopolitical risk and oil prices, Duan et al. (2021) reveal a mutual causal relationship between the two. Lee, Lee and Li (2021) find a one-way Granger causality from changes in geopolitical risk to changes in oil price in the United States. Huang et al. (2021) establish a nonlinear relationship between geopolitical risk and oil prices, and a nonlinear mutual relationship between oil volatility and geopolitical risk. Su et al. (2019), through a wavelet analysis, establish the dependence of oil prices on geopolitical risk. On the reverse side, Ivanovski and Hailemariam (2022) confirm the negative effects of oil price on geopolitical risk, while oil price volatility has a positive effect on geopolitical risk. The effects of both on geopolitical risk however vary with time and across countries. Li, Huang, Gao and Zhang (2021) also find a time-varying spillover relationship among geopolitical risks, gold prices and oil prices in BRICS. This spillover relationship increased following major events that caused uncertainty. Gürsoy (2021) use the Hatemi-J asymmetric causality techniques to show the asymmetric relationship between geopolitical risk (GPR) index, geopolitical threats (GPR) index, and geopolitical acts (GPA) index and non-renewable energy prices—the Brent oil price and gas price. Bouoiyour et al. (2019) prove further that oil prices are moderately driven by higher shocks due to geopolitical threats and are strongly driven by higher shocks due to geopolitical acts. However, Alqahtani and Taillard (2019) observe no response in oil prices to shocks in geopolitical risk, using an impulse response analysis.
In a general assessment of energy price, Soliman, Lau and Zhang (2021) find evidence of energy price, Bitcoin and geopolitical risk co-movement. Murray (2018) show the significant impact of geopolitical shocks on commodity prices, particularly energy prices, through impulse response analyses; changes in geopolitical risk before the global financial crisis are also linked to changes in these commodity prices. Rubene and Koester (2021) show how energy inflation increased due to base effects associated with significant drop in oil prices in early 2020; energy inflation was further increased due to taxes and other surcharges. Haider et al. (2014) identify broad money exchange rate, international oil prices and adaptive expectations as prominent among many factors that determine energy inflation in Pakistan. Others include tax and energy import-gap ratio. The roles of monetary and fiscal authorities are also observed to complement the roles of exchange rate depreciation and international oil price shock in increasing energy inflation. Remarkably, global geopolitical risk has been found to have a long-run relationship with and long-run effects on exchange rates (Duan et al., 2021; Kisswani & Elian, 2021).
Clark & Terry (2010) show how core inflation in the United States fell rapidly in response to changes in energy prices. This suggests that the pass-through of energy inflation to core inflation was weakened. The authors link this to a remarkably higher volatility of shocks that impact energy prices. Punzi (2019) empirically analyses the macroeconomic implications of energy price volatility for an economy whose energy price is determined abroad. Economic slowdown is found to be the response to positive energy price shocks, while energy prices are found to be influenced by uncertainty in supply in the face of increasing demand. Similar to Kilian (2008), it is established that oil price increases are driven by exogenous factors that cause supply shocks. Supply shocks could be consequences of shocks to production capacity which are linked to geopolitical risks.
One stream of literature has analysed the link between energy production and geopolitical risk. Geopolitical rivalry is identified as one of the challenges of offshore wind energy generation in India (Aswani, Sajith & Bhat, 2021). In the Persian Gulf, both geopolitical risk and cost of oil damage are found to be responsible for negative shocks in crude oil production (Olanipekun & Alola, 2020). Shocks in global geopolitical risk significantly increase the costs of freight of both liquified natural gas (LNG) and liquified petroleum gas (LPG) (Michail & Melas, 2021). Geopolitical risk uncertainty influences the cash holding decision of the oil sector in China. Their upstream sector tends to hold on to more cash in reserve in order to combat uncertainty. However, geopolitical risk negatively affects cash holdings in the downstream sector, while there is no significant relation between geopolitical risk and cash holding in the midstream (Wang et al., 2021). Therefore, increased geopolitical risk suggests poor economic conditions for oil enterprises. Furthermore, there is a positive impact of geopolitical uncertainty on volatilities of energy commodities, namely: crude oil, heating oil and natural gas (Liu, Han & Xu, 2021). Li, Yang, Li and Failler (2021) explore the complex impact of geopolitics on energy trade in emerging economies and find geopolitics to have a negative impact on both import and export aspects of energy trade, but with a more negative impact on export. These corroborate the study of Caldara and Iacoviello (2018), which identifies decline in real activity as an adverse effect of an exogenous change in geopolitical risk. This is driven by high threats of adverse geopolitical events that disrupt the tranquility in local or international relations.
The shortcoming in the literature, which this study seeks to address, is the focus on a particular energy price, particularly oil price. This study expands the literature and the understanding on how the general energy inflation reacts to geopolitical risks.
3.1 Methodology
3.1.1 Nonparametric time-varying coefficient panel data model with fixed effects
Due to the restrictive nature of the assumptions about functional form that are required in parametric, time-invariant linear regression models, unreliable parameter estimates are often generated (Dogan, Smyth & Zhang, 2018). Moreover, instabilities and nonlinearities often occur as a result of economic cycles, economic fluctuations and changes in policy decisions (Lee & Olasehinde-Williams, 2021; Olasehinde‐Williams & Oshodi, 2021; Olasehinde-Williams, Lee & Folorunsho, 2022). We therefore employ a time-varying, nonparametric technique which has the following advantages. Firstly, it renders unnecessary the need to obtain prior information on the exact nature of functional forms. Secondly, under this approach, the linearity assumption that is generally required by parametric, time-invariant models is relaxed. Thirdly, it provides relative flexibility in data exploration. Fourthly, the approach is robust to challenges posed by parameter instabilities, nonstationarity, regime shifts, country-specific heterogeneities and time variations. Finally, the approach makes it possible to treat common trend functions as time functions in a manner that permits the capture of common global shocks that may be present (Lee & Robinson, 2015; Dogan et al., 2018; Liddle, Smyth & Zhang, 2020). The time-varying coefficient panel data approach with fixed effects proposed by Li, Chen and Gao (2011) is followed. The specified empirical model relating geopolitical risk to energy inflation in the European Economic Area is specified thus;
3.1.2 Method of moments quantile regression (MMQR)
As a robustness check, we further perform a panel quantile regression. In general, conditional mean-based regressions such as OLS assume that the conditional means, which is located at the mid-point of the distribution, is normally used in the determination of any relationship between two or more variables. This approach of course provides an incomplete description of the entire conditional distribution, and as such, can provide unreliable results especially when the effect is found in the tails of the distribution (see Ike, Usman & Sarkodie, 2020). To this extent, we use a quantile regression approach via the method of moments quantile regression (MMQR) with fixed effects. The method, as advanced by Machado and Silva (2019), does not only consider all information about different points in the conditional distribution but also identifies distributional asymmetry. It therefore has some main advantages over the quantile regression approach advanced by Koenker (2004), Canay (2011) and Powell (2016). First, it incorporates fixed effects through location parameters. The fixed effects are not just location shifters but their impacts are feasibly seen across the entire conditional distribution. Second, the approach is robust to outliers and thus provides robust estimates by controlling for heterogeneity and asymmetry through location and scale parameters. The MMQR model as proposed by Machado and Silva (2019) can be specified as:
3.2 Data
To achieve the study objective, a panel data structured yearly, stretching from 1990 to 2015 is used for empirical analysis. Countries belonging to the European Economic Area that are included in the study are: Belgium, Germany, Denmark, Spain, Finland, France, Hungary, Italy, Netherlands, Norway, Poland, Portugal and Sweden. Data availability dictated the choice of sample period and countries. The constructed dataset includes the dependent variable—energy consumer price index—the regressor of interest—geopolitical risks—as well as variables included as controls—energy dependence, oil price and environmental policy stringency.
Energy CPI data is taken from the World Bank’s cross-country database of inflation (https://www.worldbank.org/en/research/brief/inflation-database). Data on geopolitical risk is retrieved from the Economic Policy Uncertainty website (https://www.policyuncertainty.com/gpr.html). The index is developed using the methodology described by Caldara and Iacoviello (2022). Data on energy dependencies is obtained from the energy imports dependence database of Eurostat (https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_ind_id&lang=en). Oil price data is taken from the database of the Federal Reserve bank of St. Louis (https://fred.stlouisfed.org/series/DCOILBRENTEU). Environmental policy stringency data is freely downloadable at the OECD statistics database (https://stats.oecd.org/Index.aspx?DataSetCode=EPS). The logarithmic forms of all the variables are used for empirical analysis for ease of interpretation and also to deal with non-normality, non-linearity and heteroscedasticity in the data series. Figure 1 reveals the historical movement of the variables of interest (energy CPI and geopolitical risks), while Table 1 presents their summary statistics.
|
Table 1. Summary statistics |
||
|
Energy CPI |
Geopolitical risks |
|
|
Mean |
9.260 |
0.119 |
|
Median |
3.400 |
0.056 |
|
Maximum |
1151.600 |
0.965 |
|
Minimum |
-8.412 |
0.005 |
|
Std. Dev. |
63.743 |
0.158 |
|
Skewness |
17.178 |
2.528 |
|
Kurtosis |
307.150 |
9.896 |
|
Jarque-Bera |
1319438*** |
1030.092*** |
Note:(1) *** P < 0.01.
The estimation outcomes obtained from the nonparametric time-varying model with fixed effects are reported in Table 21. The year-on-year parameter estimates indicating the reaction of energy inflation to variations in geopolitical risks are positive and significant between 1993 and 2006. The estimates are insignificant in the preceding and succeeding years. This is an indication that geopolitical risks aggravate energy inflation in the European Economic Area. Also, judging by the variations in the size of the coefficients over time, we can infer that the impact of geopolitical risks on energy inflation has been on a gradual but consistent decline. The size of the coefficients dropped from 3.518 in 1993 to 1.347 in 2006. The graphical representation of the evolution of the coefficients indicating the effect of geopolitical risks on energy inflation reported in Table 2 can be seen in Fig. 2. The graph illustrates the gradual but consistent decline over time in the size of the coefficients, thus confirming the waning influence of geopolitical risks on energy inflation in the European Economic Area.
|
Year |
Coefficient |
Std. error |
Z-value |
P > Z |
[95% Conf. Interval] |
|---|---|---|---|---|---|
|
1990 |
4.799 |
3.295 |
1.46 |
0.145 |
-1.659 11.258 |
|
1991 |
4.282 |
2.815 |
1.52 |
0.128 |
-1.235 9.800 |
|
1992 |
3.854 |
2.401 |
1.61 |
0.108 |
-0.851 8.561 |
|
1993 |
3.518* |
2.118 |
1.66 |
0.097 |
-0.632 7.670 |
|
1994 |
3.251* |
1.868 |
1.74 |
0.082 |
-0.411 6.913 |
|
1995 |
3.021* |
1.642 |
1.84 |
0.066 |
-0.198 6.241 |
|
1996 |
2.815* |
1.443 |
1.95 |
0.051 |
-0.012 5.644 |
|
1997 |
2.627** |
1.275 |
2.06 |
0.039 |
0.126 5.127 |
|
1998 |
2.451** |
1.080 |
2.27 |
0.023 |
0.333 4.570 |
|
1999 |
2.286** |
0.930 |
2.46 |
0.014 |
0.462 4.111 |
|
2000 |
2.130** |
0.862 |
2.47 |
0.013 |
0.440 3.820 |
|
2001 |
1.982** |
0.800 |
2.48 |
0.013 |
0.413 3.550 |
|
2002 |
1.841*** |
0.671 |
2.74 |
0.006 |
0.525 3.156 |
|
2003 |
1.707*** |
0.563 |
3.03 |
0.002 |
0.603 2.811 |
|
2004 |
1.580*** |
0.564 |
2.80 |
0.005 |
0.473 2.686 |
|
2005 |
1.460** |
0.594 |
2.45 |
0.014 |
0.294 2.625 |
|
2006 |
1.347* |
0.716 |
1.88 |
0.060 |
-0.056 2.751 |
|
2007 |
1.242 |
1.018 |
1.22 |
0.222 |
-0.753 3.238 |
|
2008 |
1.146 |
1.037 |
1.11 |
0.269 |
-0.887 3.181 |
|
2009 |
1.061 |
1.262 |
0.84 |
0.401 |
-1.413 3.536 |
|
2010 |
0.987 |
1.270 |
0.78 |
0.437 |
-1.502 3.477 |
|
2011 |
0.929 |
1.205 |
0.77 |
0.441 |
-1.433 3.291 |
|
2012 |
0.889 |
1.307 |
0.68 |
0.496 |
-1.672 3.452 |
|
2013 |
0.877 |
1.317 |
0.67 |
0.506 |
-1.705 3.460 |
|
2014 |
0.902 |
1.274 |
0.71 |
0.479 |
-1.595 3.399 |
|
2015 |
0.951 |
1.434 |
0.66 |
0.507 |
-1.861 3.763 |
Note: (1) 10, 000 bootstrap replicates were performed. (2) *** ,**,* represent significance at 1%, 5% and 10% respectively.
|
Location |
Scale |
Lower Quantiles |
Middle Quantiles |
Upper Quantiles |
|||
|---|---|---|---|---|---|---|---|
|
VARIABLES |
Parameters |
Parameters |
\({Q}_{0.1}\) |
\({Q}_{0.3}\) |
\({Q}_{0.5}\) |
\({Q}_{0.7}\) |
\({Q}_{0.9}\) |
|
\(\text{l}\text{n}\text{G}\text{P}\text{R}\) |
0.210 |
0.1276 |
-0.00246 |
0.153 |
0.234** |
0.309** |
0.395** |
|
(0.131) |
(0.114) |
(0.278) |
(0.151) |
(0.113) |
(0.121) |
(0.173) |
|
|
\(\text{C}\text{o}\text{n}\text{s}\text{t}\text{a}\text{n}\text{t}\) |
3.888*** |
1.701*** |
|||||
|
(0.575) |
(0.2999) |
||||||
|
Observations |
299 |
299 |
299 |
299 |
299 |
299 |
|
| Standard errors in parentheses | |||||||
| *** p < 0.01, ** p < 0.05, * p < 0.1 | |||||||
To check the robustness of our findings, we apply the method of moments quantile regression (MMQR) with fixed effects and robust standard errors. Results are reported in Table 3. As can be seen, the effect of the geopolitical risk is positive across the conditional quantile distribution of the energy price inflation. This effect is only significant from the mid quantiles through the higher quantiles. It suggests that a change in geopolitical risk increases energy price inflation more in countries with higher levels of energy inflation than countries with lower levels of energy inflation. In other words, countries with a better management of energy inflation tend to have lower pass-through of geopolitical risks to energy inflation in comparison with countries with poor management of energy inflation. Moreover, as a country’s energy price inflation is reducing over time, the effect is gradually and consistently shrinking. This therefore corroborates the results of the time-varying model that the geopolitical risk reduction effect on energy inflation is gradual and consistent.
[1] For brevity, the results of the variables of interest are reported in this study. However, readers of this paper may wish to contact the corresponding author for the full results.
This paper studies the effect of geopolitical risks on energy inflation in the European Economic Area over the period 1990–2015. In order to reveal the time-varying characteristics of the relationship, the nonparametric time-varying coefficient panel data model with fixed effects is applied. In addition, to further reveal potential tail effects that may not have been captured by conditional mean-based regressions, we also employ the method of moments quantile regression. A number of conclusions are thereby drawn. First, rising geopolitical risk leads to greater energy inflation in the European Economic Area. As these European countries get exposed to geopolitical tensions, it is expected that energy prices will surge. This may be due to several reasons. Investors in the energy markets are prone to raising risk premium as the geopolitical environment becomes more volatile. Geopolitical conflicts generally threaten the smooth flow of energy supply across borders and these often spill over into energy prices. Rising geopolitical problems have the ability to trigger economic fluctuations that impact oil prices.
Second, as revealed by the variations in the size of the coefficients over time, the study shows that the impact of geopolitical risks on energy inflation experienced a gradual but consistent decline over time. This is an indication that the ability of geopolitical risk to trigger energy inflation in more recent times is not as powerful as it was in the past. The expanding renewable energy use in the region might be a key factor behind this phenomenon. As the share of renewables in the energy-mix of these countries within the European Economic Area increases and their energy import dependence falls, their vulnerability to geopolitical events are perhaps also declining.
Third, results obtained from the MMQR reveal that countries that are already better at managing inflation, when exposed to more geopolitical risks, experience smaller increases in energy inflation (those at the lower quantiles). On the other hand, as for countries that are already struggling with inflation control, when exposed to more geopolitical risk, they experience even greater energy inflation (those at higher quantiles). This suggests that the impact of geopolitical risk on energy inflation is dependent to an extent on the quality of macroeconomic management in the individual countries that make up the European Economic Area. Overall, the study findings lead us to the conclusion that transitioning from non-renewable to renewable energy use is one channel through which the sampled countries can battle the energy inflation which geopolitical risks trigger. A sound macroeconomic policy for inflation control is a complementary channel through which the same goal can be achieved.
Data Availability Statement: The data used and/or analyzed during the study are available with all the authors and they are ready to make it available upon a reasonable request.
Competing Interests: The authors declare that they have no known competing interests.
Authors’ Contributions:
Godwin Olasehinde-Williams: Conceptualization, Formal analysis, Investigation, Methodology, Data curation, Writing - original draft, Writing- review & editing. Ifedola Olanipekun: Writing -original draft, Writing- review & editing. Ojonugwa USMAN: Writing - original draft, Writing - review & editing, Methodology, and Visualization.
Funding: The authors received no funding for this research
Authors’ information:
Godwin Olasehinde-Williams holds a Ph.D. in Economics from Eastern Mediterranean University, North Cyprus. He is currently an Assistant Professor in the Department of Economics at Istanbul Ticaret University, Istanbul, Turkey.
Ifedola Olanipekun holds a Ph.D. in Economics from Eastern Mediterranean University, North Cyprus. She is currently a Senior Lecturer in the Department of Economics at Adeyemi College of Education, Ondo, Nigeria.
Ojonugwa USMAN holds a PhD in Economics from Eastern Mediterranean University, North Cyprus. He is currently an Assistant Professor in the Department of Economics at Istanbul Ticaret University, Istanbul, Turkey.
Acknowledgements: The Authors are grateful to Mrs. Morountodun Olasehinde-Williams who proof-read the entire manuscript
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