Geopolitical turmoil and energy dynamics: Analyzing the impact on inflation in selected European economies

Abstract

This paper attempts to examine the impact of energy price shocks, monetary policy, and geopolitical events on inflation in selected European countries across different periods of time by applying a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with stochastic volatility. The analysis displays the diverse impact of gas prices on the Consumer Price Index (CPI), a gauge for inflation, in the short, medium, and long term. Moreover, the findings show that oil price shocks with deflationary trends during COVID-19 have inflationary consequences. The results also show that the European Central Bank's response to the pandemic-induced economic downturn has affected long-term inflation trends. Furthermore, significant geopolitical risks that arose from the Russia-Ukraine war amplified inflationary trends. The findings contribute valuable insights into the multifaceted dynamics shaping inflation in European economies.

1. Introduction

The world has faced many challenges in recent years. These challenges have caused not only political but also economic problems. Policymakers and regulators have come up with a diverse set of policies to overcome the problems. Yet, these policies have side effects. One of the most crucial and urgent side effects is inflation. Unfortunately, inflation is a swiftly soaring worldwide issue and a root of economic instability. Inflation, once a concern for only developing countries, has become a problem for developed nations, too [1]. This situation has been caused by globalization since the 1980s, and thereby, the rising interdependence among countries. Due to the interdependence of countries, a possible tension or an event in one country might easily affect economic stability levels in other countries. Especially in recent years, conflicts, disputes, and confrontations among countries have had political and economic repercussions on other countries. To illustrate, The US-China trade war has had significant effects on the global economy. In addition, the Russia-Ukraine war has created adverse consequences globally. Still, the war has the most significant impact on European Union countries.

The Ukraine-Russia war has significant repercussions on inflation. The conflict has led to a rise in global inflation, with projections indicating an increase of about 2 % in 2022 and 1 % in 2023 [2]. The conflict has also significantly affected the energy sector, influencing oil prices recently [3]. In addition, severe repercussions occurred due to the war and financial sanctions imposed on Russia. This affects Russia and countries implementing financial sanctions, including the USA, the UK, Canada, and the EU [4,5]. Global economic stability has also been affected by the war, which has wreaked havoc on the global supply chain [6]. The collapsing competition between Russia and Ukraine in gas shipment to Europe is a major effect of the war [7]). Besides, the war has adversely affected stock markets, energy, supply chains, etc., leading to European inflation. According to reports, the global stock market has become more unpredictable and volatile as a result of the conflict [8].

The geopolitical events following the invasion of Ukraine by Russian forces have not only heightened security concerns but have also presented a significant economic challenge for Europe. The continent's heavy dependence on Russian gas, primarily delivered through pipelines, creates a vulnerability beyond geopolitical tensions. The potential disruption in the energy supply chain poses a direct risk to inflation in European countries.

Fig. 1 highlights the susceptibility of certain European countries to a disruption in the Russian gas supply. For instance, Russia provides approximately half of Germany's gas needs. Besides, Russian sources supply a quarter of France's gas. Moreover, Italy, relying on Russian gas for 46 percent of its supply, is majorly dependent on Russian gas sources.

Fig. 1.

GDP of the largest countries in Europe and their dependencies on Russian gas.

Considering Russia's primary dependence on gas revenues to Ukraine and the European Union, the war has affected the oil markets in Europe [3]. Hence, the war has damaged European stock market returns, supply chains, and energy markets, causing inflation to soar up [8].

Determining the factors of inflation appropriately requires analyzing the diverse conditions of energy-dependent countries while considering differing breakdowns like geopolitical, monetary, and energy price shocks. A proper measure is essential in the analysis of varying conditions among countries. Hence, this paper applies the Caldara-Iacoviello geopolitical risk (GPR) index. With the use of the GPR index, the analysis captures the impact of geopolitical tensions. Extensive use of the GPR index in macroeconomic studies supports its reliability in quantifying the economic implications of geopolitical uncertainty.

Inflation dynamics are materially affected by Geopolitical tensions, monetary policy changes, and fluctuations in energy prices. Therefore, comprehending and elucidating the interaction among these factors is crucial for a comprehensive analysis of inflationary pressures. Hence, this study attempts to investigate the determinants of inflation in the three largest economies of Europe, namely Germany, France, and Italy, with a focus on the interaction among energy price shocks, monetary policies, and geopolitical risks within a dynamic framework.

This paper employs a time-varying approach and captures the temporal effects of the factors above. In spite of a large body of literature on inflation factors, there exist significant gaps in the literature. First, the literature has mainly examined oil price fluctuations, often neglecting gas price dynamics, particularly in highly energy-dependent economies like those in Europe. Second, while geopolitical risks have been analyzed in the context of broader economic impacts, their direct and dynamic interactions with inflation remain underexplored. Third, conventional models such as SVAR and classical VAR methods fail to capture the evolving nature of these relationships over time. This study bridges these gaps by: (1) incorporating gas price volatility alongside oil price shocks to provide a comprehensive analysis of energy dependency, (2) dynamically analyzing the temporal effects of geopolitical risks on inflation using the Caldara-Iacoviello GPR index, and (3) employing a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model to uncover country-specific and time-sensitive dynamics in Germany, France, and Italy, the three largest economies in the EU.

Our research reveals that significant geopolitical risks substantially impact increasing inflation. Through comparative analyses conducted on the three largest individual economies within the European Union—Germany, France, and Italy—we also bring to light variations in the impact of geopolitical risks on different economic entities. In line with the literature on the complex relationship between uncertainty and inflation [[9], [10], [11]], the adverse effect of escalated uncertainty on inflation is supported by the findings.

2. Literature review and theoretical background

Previous literature mainly deals with monetary policy, prices for energy, and geopolitical risk variables to analyze the factors that influence inflation. Yet, previous works typically focus on oil prices as a standalone factor and neglect gas prices.

The Quantity Theory of Money (QTM) is the basis for the initial studies on the determinants of inflation [12]. The QTM analyses the effects of changes in the money supply of an economy on the overall price level. This theory is typically expressed by the equation MV = PY, where M represents the money supply, V represents the velocity of money (the rate at which money circulates per unit), P denotes the general price level, and Y represents real income or output. In the early versions of the model, velocity is assumed to be stable in a short period. On the other hand, classical economists, such as Irving Fisher, asserted that at full employment levels, both wages and prices are flexible in subsequent versions. Consequently, the theory argued that an increase in the money supply would only lead to an increase in the price level [13]. Demand-pull inflation, deeply rooted in the Quantity Theory of Money (QTM), highlights the role of monetary policy and aggregate demand shocks. For instance, increases in the money supply or post-recession consumption recovery can exacerbate inflationary pressures. Cost-push inflation, however, is primarily linked to rising input costs, particularly energy prices, and is exacerbated by external shocks such as geopolitical risks. This paper adopts a time-varying framework to disentangle these two forces, providing a comprehensive analysis of their individual and joint effects on inflation in Germany, France, and Italy.

Regarding the empirical basis of the theory, researchers across various regions have explored the relationship between changes in the money supply and inflation. Studies in the United States highlight strong evidence for this relationship [[14], [15], [16]]. Similarly, research in Australia [17], as well as cross-regional studies encompassing the US, the UK, the Euro area, and Japan, underscores the inflationary effects of changes in the money supply [18].

In Europe, analyses conducted in the Czech Republic [19], Italy [[20], [21], [22]], and Turkey [[23], [24], [25], [26]] confirm these findings, with studies focusing on the relationship between monetary changes and inflation. Research in China [[27], [28], [29]], Pakistan [[30], [31], [32]], and Russia [2] aligns with these results, emphasizing the broader applicability of the theory. Additional studies in England [33,34], the Gulf Cooperation Council (GCC) [35], and Vietnam [36] further support the theory's validity, and Europe [37] further support the theory's validity, as do investigations in 15 industrialized economies [38].

However, not all studies agree on the direct link between money supply and inflation. Some argue that external factors, such as energy prices, play a moderating role. For instance, research has shown that while changes in the money supply are important, inflation is also influenced by external variables such as energy price volatility [39,40].

The literature on the impact of energy shocks on inflation primarily focuses on the pass-through effect of crude oil prices to domestic prices, often utilizing an augmented Phillips curve. The Phillips curve, which links current and expected inflation, predicts future inflation and measures economic conditions, has long been central to understanding the relationship between oil prices and inflation. Since Hamilton's seminal paper, which highlighted the crucial role of oil price shocks in influencing inflationary pressures, scholars have explored how changes in oil prices affect inflation dynamics [41].

The findings in the literature, however, are mixed. Many studies suggest that an increase in oil prices generally leads to higher inflation, particularly in oil-importing economies where energy prices account for a significant portion of production and consumption costs. Some of the studies in literature support this view, emphasizing that the rise in production costs, due to higher energy prices, is passed on to consumers, contributing to inflation [[42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53]]. These studies also point to indirect effects, such as the increased demand for alternative energy sources, which further push up prices across various sectors.

However, some studies argue the opposite, suggesting that rising oil prices do not always lead to higher inflation, particularly in economies where oil price changes are less correlated with broader price movements. These studies show that a rise in oil prices might have a small or zero effect on inflation in countries with powerful monetary policy structures or in those with short-lived oil price hikes and insignificantly affected aggregate demand [[54], [55], [56]].

On the contrary, some other studies prove that oil price changes have a constrained impact on inflation upon specific economic conditions. Works by Refs. [57,58] highlight the role of factors such as the level of economic openness, the degree of oil dependency, and the flexibility of labor and product markets. These studies propose that the inflationary effect of oil price shocks may be moderated in economies with well-developed financial markets, diversified energy sources, and effective policy responses.

The COVID-19 pandemic has introduced a new complexity to the relationship between oil prices and inflation. Due to the pandemic, unprecedented demand-side shocks have taken place, leading to oil price volatility and rising inflationary pressures. Some studies indicate that sudden changes in demand throughout the pandemic have caused rises in the volatility of oil prices and exacerbated inflation, especially in economies highly dependent on oil [50]. These results underpin that it is essential to take both market-specific and global factors when examining the inflationary impact of oil price shocks. In addition, the findings highlight significant variations of effects across different countries with diverse structural differences and reliance on oil.

In conclusion, the existing literature presents contradictory findings regarding the relationship between oil price changes and inflation. These discrepancies can largely be attributed to variations in economic structures, the degree of oil dependency, and other moderating factors such as monetary policy responses and global demand shocks.

The literature on the relationship between inflation and geopolitical uncertainty has yielded limited and often contradictory results. Some of these studies rely on the hypothesis that geopolitical risks contribute to increased energy prices, leading to higher inflation [[9], [10], [11]]. On the contrary, another set of studies proposes a hypothesis suggesting that geopolitical risks might decrease demand, and the demand reduction, despite not lowering inflation, could mitigate its upward pressure [59] Geopolitical risk measures have been extensively used to analyze the impact of geopolitical events on economic variables. This index aggregates news-based metrics to quantify geopolitical risks, distinguishing between threats and realized acts. Other studies have proposed alternative approaches, such as survey-based measures or composite indices integrating regional conflict data. For example, a time-varying structural VAR framework was applied to analyze the impact of geopolitical risks on energy prices and inflation in Europe [10], while panel TVP-VAR analysis was used to explore the interconnectedness of geopolitical risks, inflation, and currency values [60]. These methodologies highlight the utility of GPR indices as dynamic tools for economic analysis, providing a robust foundation for the study.

Findings in the literature vary across sample characteristics, especially countries and regions in the sample. Considering Europe, several factors have been identified as key drivers, including oil [[61], [62], [63], [64], [65]] and monetary shock [[66], [67], [68], [69]] and geopolitics risk [10,[70], [71], [72]] in studies investigating the determinants of inflation. These variables have been extensively analyzed to understand their impact on European inflation. Still, very few studies analyze European countries; none of these studies use gas prices as a determinant of inflation.

The review of existing literature reveals a fragmented understanding of the interconnected influences of energy price shocks, geopolitical risks, and monetary policy on inflation. This study aims to bridge this gap by integrating gas price dynamics into the analysis and leveraging a dynamic TVP-VAR model to uncover temporal variations. The primary motivation for this study stems from three key factors. Firstly, there is a scarcity of research exploring the link between geopolitical risk and inflation. Only a few studies have considered this issue [[11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53]], [[54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [85], [86], [87], [88], [89], [90]] have considered this issue. Secondly, research on how changes in petrol prices affect inflation is lacking. In light of the growing reliance on Russia by EU member states, we investigate the effects of shifts in oil prices and the effects of petrol price fluctuations on inflation. Finally, unlike conventional techniques based on static methods, like SVAR and classical VAR methods, we apply a dynamic approach, Time-Varying Parameter (TVP)-Vector Autoregressive (VAR). TVP-VAR might be more accurate and nuanced on complicated analysis dynamics and capture the shifting interactions between factors over time. Based on the literature, this study hypothesizes that (H1) geopolitical risks significantly drive inflation, (H2) oil price shocks have a pronounced inflationary effect, and (H3) monetary policy responses are contingent on energy dependencies. These three hypotheses are as follows.

Hypothesis 1 (H1)

Geopolitical risks have a significant positive effect on inflation in European countries.

Hypothesis 2 (H2)

Oil price shocks exert a more substantial inflationary impact than gas price shocks.

Hypothesis 3 (H3)

The effects of monetary policy on inflation vary across countries depending on their energy dependencies.

These hypotheses are tested using a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model.

3. Data and methods

3.1. Sample and description of data

Our sample consists of three European countries, Germany, France, and Italy, to examine the interrelated dynamics of monetary policy, geopolitical risks, energy price shocks, and inflation. We pick these economies based on two primary considerations. Firstly, as highly developed countries, sample countries are the three leading economies in European countries and have attracted significant interest from scholars. These three countries are the largest economies in the European Union, collectively contributing approximately 47.9 % of the EU's GDP in 2023. Their substantial economic weight makes them key representatives for understanding energy price impacts on the EU economy as a whole [73,74]. Moreover, these countries exhibit highly diversified economies spanning industrial, service, and agricultural sectors. This diversity allows for a comprehensive analysis of how energy prices impact different sectors, offering broader insights into the EU's economic dynamics [74]. We aim to provide valuable insights into the complex connections between inflation, geopolitical risks, monetary policy, and energy price shocks across different economic landscapes. Secondly, these countries show varying reliance on Russia for energy supply. By delving into the inflation dynamics within the context of this energy interdependence, we can discern and analyze potential inflationary effects arising from these differing levels of energy reliance.

We utilize monthly data on CPI, gas prices, oil prices, and money supply (M3) from March 2010 to August 2023. We obtained inflation and money supply data from the European Statistical Office and the Federal Reserve Bank oil and gas prices from the Trading Economics website. We use the Dutch TTF gas daily Futures price as the primary gas pricing hub for the European gas market. We perform the analysis by converting the variables into their growth rates. The geopolitical risk (GPR) index used in this study is sourced from Caldara and Iacoviello's widely cited dataset, which measures geopolitical risks based on a textual analysis of newspaper articles. This global index captures both geopolitical acts (e.g., wars, terrorist attacks) and threats, making it a comprehensive indicator for analyzing risk-induced economic fluctuations. While the primary GPR index is global, its application in this study is contextualized to capture country-specific implications by intersecting the index with energy dependency levels. This approach aligns with prior studies exploring geopolitical risks in specific regional contexts [10,60]. In addition, we use each country's own GPR in the analysis. Table 1 represents the variables, sources, units, and descriptions.

Table 1.

Data sources, units, and descriptions.

Variable	Source	Units	Description
CPI	European Statistical Office (Eurostat) and Federal Reserve Bank	Growth Rates	Consumer Price Index, used to approximate inflation rates
Money Supply	Federal Reserve Bank	Growth Rates	Broad money supply index, reflecting monetary policy trends (m3)
Oil Prices	Trading Economics (Global Oil Prices)	Growth Rates	Crude oil prices in global markets represented as growth rates
Gas Prices	Trading Economics (Dutch TTF Gas Daily Futures)	Growth Rates	Primary pricing hub for the European gas market represented as growth rates
Geopolitical Risk (GPR)	Baker, Bloom, and Davis Geopolitical Risk Index (GPR)	Growth Rates	The geopolitical risk index, measuring risks associated with geopolitical events, represented as growth rates

The motivation for focusing on France, Germany, and Italy in your analysis can be justified by their reliance on Russian gas markets and the role of spot pricing in their gas imports. These countries tend to purchase gas through spot markets, which means they are more exposed to the volatility and geopolitical risks associated with energy crises. For example, disruptions in gas flows or shifts in Russian policy directly affect their energy costs and supply security. In contrast, countries like Romania, Slovakia, and Hungary often rely on long-term contracts for their gas supplies [75] These contracts typically lock in predetermined prices, insulating them from short-term fluctuations and spot price volatility. However, these nations remain vulnerable in other ways, as they are highly reliant on Russian gas and have fewer alternative supply sources [76]. Additionally, the aspect of Eurozone membership, which was raised, also matters. The economic mechanisms within the Eurozone, such as monetary policy tools and fiscal stability mechanisms, offer a buffer against external shocks for member states like France, Germany, and Italy. Non-Eurozone countries like Hungary and Romania, however, lack this shared economic infrastructure, potentially making them more susceptible to external economic pressures, even if their energy pricing structure is less volatile. Moreover, GPR data for countries like Hungary and Slovakia is not available.

Table 2 in Appendix (a) provides a comprehensive overview of descriptive statistics for key economic variables across Germany, France, Italy, and energy prices. All countries exhibit a positive mean in the inflation rate, with Germany having the highest mean inflation rate. When considering the minimum, maximum, and standard deviation values, it is observed that the volatility of the inflation variable is highest in Italy. Additionally, Italy holds the highest mean values for money supply and geopolitical risks. The Jarque-Bera test statistics are statistically significant at the 1 % level for all variables, rejecting the null hypothesis of normality. According to the Jarque-Bera test, which indicates that the variables do not follow a normal distribution, it seems appropriate to employ nonlinear methods such as TVP-VAR in our study.

3.2. Time-varying parameter Vector Autoregressive (TVP-VAR)

We employ a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with stochastic volatility to assess how energy price shock, monetary policy, and geopolitical shock affect the consumer price index of selected European countries during various periods. This modeling approach, suggested by previous studies [[77], [78], [79]], offers a crucial comparative advantage by allowing the explanatory dynamics of macroeconomic relationships to be dissected into two key components. The first step separates the effects of the volatile changes, and the second step points out the effects of the coefficient. As a critical advantage, the TVP-VAR model is capable of analyzing and separating the varying impacts of multiple effects into their mechanisms and variables. The use of the TVP-VAR method in this study provides a more nuanced perspective compared to traditional VAR models by uncovering the time-varying economic dynamics of the analyzed countries. For example, the method allows for dynamic observation of how the impacts of different energy import regimes and geopolitical risks vary across countries over time. This enables policymakers to develop strategies that are adaptive to these temporal changes.

There are five equations in a TVP-VAR model: four in state space, one in measurement space, and three in transition space. The model incorporates changes in parameters and stochastic volatilities simultaneously. Hence, it helps to elucidate the complex dynamics in the economic structure. The details of the TVP-VAR model are in Appendix (b).

4. Results and discussion

4.1. Pre- estimation test results

The Jarque-Bera test in descriptive statistics indicates that the test results suggest the possibility of nonlinearity in the variables. To ensure confidence in this observation, researchers commonly use the BDS non-linear test, which is frequently applied in the literature. We applied for this test in our study as well.

Table 3 in Appendix (c) presents the outcomes of the BDS (Brock, Dechert, and Scheinkman) non-linearity test for various economic variables and lags [80]. Each row corresponds to a different variable, such as Consumer Price Index (CPI), Money Supply (M3), Government Policy Rate (GPR), Oil Prices (OIL), and Gas Prices (GAS), while different columns represent the test results at varying lags (m = 2, m = 3, m = 4, m = 5, m = 6). The p-values associated with each test indicate the likelihood of rejecting the null hypothesis of linearity.

After identifying nonlinearity through the Jarque-Bera and BDS tests, the subsequent step involves examining whether the variables exhibit a unit root. In order to tackle this issue, our study employs the KSS Nonlinear Unit Root Test. The findings of this examination are displayed in Table 4 in Appendix (c). Based on the KSS Nonlinear Unit Root Test results, all variables in our analysis exhibit stationarity at the level, suggesting the absence of a unit root. This result provides essential knowledge on the stationary characteristics of the variables, hence improving our comprehension of their behavior within the analytical framework.

4.2. Estimations of parameters

Table 5 shows the estimation results for the sample countries: Italy, France, and Germany. The TVP-VAR methodology helps to clarify the effects of geopolitical risk, energy price volatility, and monetary shocks on the consumer price index and provides dynamic insights. In addition, the method spotlights critical periods that might affect the interactions among variables. The table displays that the lag order of the model is 2 using the Bayesian Information Criterion. Then, we perform 10,000 Markov Chain Monte Carlo (MCMC) samples.

Table 5.

Estimation Results of the TVP-VAR models.

Germany
Paramater	Mean	Stdev	95%U	95%L	Geweke	Inef.
sb1	0.0023	0.003	0.0018	0.0028	0.515	10.38
sb2	0.0023	0.003	0.0018	0.0029	0.514	9.56
sa1	0.0054	0.016	0.0033	0.0094	0.775	44.06
sh1	0.4609	0.0825	0.3171	0.6413	0.611	19.55
sh2	0.3460	0.1015	0.1840	0.5712	0.689	39.30
France
Paramater	Mean	Stdev	95%U	95%L	Geweke	Inef.
sb1	0.0023	0.0003	0.0018	0.0029	0.094	11.08
sb2	0.0023	0.0003	0.0018	0.0029	0.271	11.87
sa1	0.0055	0.0016	0.0033	0.0094	0.602	47.70
sh1	0.4978	0.0974	0.3245	0.7116	0.001	33.69
sh2	0.2905	0.0911	0.1495	0.5037	0.812	57.37
Italy
Paramater	Mean	Stdev	95%U	95%L	Geweke	Inef.
sb1	0.0023	0.003	0.0018	0.0028	0.018	6.67
sb2	0.0023	0.003	0.0018	0.0029	0.407	4.84
sa1	0.0054	0.0014	0.0033	0.0087	0.829	31.39
sh1	0.4484	0.0870	0.2994	0.6400	0.159	31.63
sh2	0.3377	0.1004	0.1855	0.5637	0.487	66.77

Table 5 also shows the standard deviations, upper and lower bounds of the 95 % confidence intervals, Geweke convergence diagnostic statistics, and inefficiency values. Accordingly, the Geweke statistics suggest that posterior Markov Chain Monte Carlo (MCMC) simulations are effective. In addition, a low value of inefficiency factors implies efficient sampling, and cumulative forecasts are in the range of confidence interval. In our case, the ineffectiveness factor is below 100, suggesting a highly efficient sampling process for all sample countries [81]. In line with Fig. 2 in Appendix (d), results indicate a rapid decline in sample autocorrelation functions, and the sample routes have a consistent structure.

4.3. Time-varying impulse response analysis

The Time-Varying Parameter Vector Autoregressive (TVP-VAR) model is able to capture temporal variations in the transition of structural shocks arising from the dynamic interplay among variables. Impulse response functions measure variables’ response to shock in the TVP-VAR model at each sample period date by incorporating time-varying parameters and stochastic shock volatility [79,82,83].

We fix the magnitude of the first shock and assume a constant average stochastic volatility for all series to calculate impulse response functions. At each time point, simultaneous interactions are applied [79,84]. Time-varying coefficients are performed to measure the recursive innovation of a variable at the end of the sample period. Then, the future periods’ coefficients are fixed to constant for handiness.

In this study, our choice of periods (1-3-6-12) differs from the common selections (1-4-8-12 or 1-3-6) in previous studies [10,83]. Since inflation has idiosyncratic dynamic features compared to other macroeconomic factors, the 1-3-6-12 period has a valid foundation. Besides, both very short-term and short-term effects, as well as medium and long-term effects on inflation, can easily be observed from this selection of period. Fig. 2, panels a to D, depicts the time-varying responses of the consumer price index (CPI) to energy prices, monetary shock, and geopolitical shocks for 1, 3, 6, and 12-month periods across sample countries.

Fig. 2.

(a) Time-varying responses consumer price index to gas price (b) Time-varying responses of consumer price index to oil price (c) Time-varying responses to consumer price index to the money supply (d) Time-varying responses consumer price index to geopolitics risk.

Note: The green dot represents the 1-month period, the blue dashed line represents the 3-month period, the red line represents the 6-month period, and the green dashed line represents the 12-month period.

Fig. 2a demonstrates the cost-push dynamics of gas price shocks on inflation in Germany, Italy, and France. These shocks, particularly pronounced during the 2022 Russia-Ukraine war, highlight the dependency of European economies on Russian gas. The medium- and long-term inflationary effects underscore the persistent upward pressure on production costs, especially in countries like Germany and Italy, which rely heavily on Russian energy. In contrast, France exhibits a milder response due to its diversified energy portfolio, including significant reliance on nuclear power. Furthermore, it is noteworthy that over time and across countries, the impact of an increase in gas prices exhibits distinct variations in the very short term, short term, medium term, and long term. Generally, an increase in gas price surges inflation in all periods except for very short intervals for all countries. The rapid increase in gas prices due to the Russia war that began in 2022 has increased inflation in all sample countries. However, as of 2023, this effect has turned negative in France, while in Germany and Italy, the impact remains positive. As indicated in Fig. 1, the main reason for this situation is Germany and Italy's high dependence (close to 50 percent) on Russian gas. Moreover, Fig. 2a highlights how the inflationary effects of gas price increases intensified significantly during the Ukraine war due to the high dependency of Germany and Italy on Russian gas. The pre-2020 period lacks such stark dependence-driven shocks, making the 2022 episode unique.

The COVID-19 pandemic has significantly impacted the global oil market, leading to a substantial reduction in oil demand [49,[85], [86], [87], [88]]. This decrease in demand has subsequently caused a decline in oil prices. This trend is clearly depicted in Fig. 2b, illustrating the time-varying responses of the Consumer Price Index (CPI) to oil price shocks for 1, 3, 6, and 12-month periods across Germany, France, and Italy. In Fig. 2-b, Generally, an oil price shock tends to have an inflationary effect across all countries. However, in March 2020, coinciding with the onset of the COVID-19 pandemic and the subsequent decrease in demand, oil prices experienced a sharp decline. This situation is evident in the graph, indicating a deflationary impact on inflation in all three countries. The reduced demand during the pandemic-induced economic downturn resulted in a notable decrease in oil prices, contrasting the typical inflationary response to oil price shocks [80,89]. In addition, Fig. 2b demonstrates that the COVID-19 period is distinct due to deflationary effects stemming from a global collapse in oil demand, contrasting with inflationary trends typically observed pre-2020. This distinction clarifies that the pandemic introduced unprecedented market dynamics.

Central bank policies play a crucial role in managing inflation, especially demand-pull inflation. Fig. 2c illustrates the long-term inflationary impact of increased money supply during the COVID-19 recovery phase. The expansionary monetary policies implemented across the Eurozone in 2020 boosted aggregate demand, driving inflationary pressures. While tightening measures by the European Central Bank (ECB) moderated these effects in Germany and France, Italy's extensive recovery package sustained upward inflationary trends, reflecting the demand-pull dynamics in this period. One of the most successful examples of this is the European Central Bank's ability to keep inflation targeting around 2 % for the Eurozone and maintaining this situation for an extended period. As seen in Fig. 2c, the long-term effects of the market supply shock on inflation are positive for all countries except France after 2022. When examining short-term impacts, the influence of money supply on inflation remains close to zero across all nations. More importantly, following the declaration of the COVID-19 pandemic in March 2020, the increase in the Eurozone money supply due to countries' recovery package announcements in late 2020 has led to a long-term rise in inflation. The increase in money supply, coupled with other factors, has led to a significant rise in inflation in the Eurozone due to COVID-19 packages. Consequently, the European Central Bank has initiated a tightening monetary policy by raising interest rates. While tight monetary policy in Germany and France has had a mitigating effect on inflation, long-term effects in Italy have remained upward. This is primarily due to Italy being the most affected European country by COVID-19, with its recovery package totaling 88 billion euros, significantly surpassing Germany's 27.7 billion euros and France's 38.8 billion euros. The substantial increase in money supply in Italy continues to contribute to the persistence of inflation. Furthermore, Fig. 2c displays how monetary expansions during COVID-19 recovery efforts (e.g., Eurozone stimulus) led to long-term inflationary effects, particularly in Italy, which are not mirrored in earlier periods.

Fig. 2d shows the impact of geopolitical risks on the consumer price index (CPI) of Germany, France, and Italy. Accordingly, geopolitical risks can affect countries' inflation dynamics by creating uncertainty and economic volatility. This effect is realized through mechanisms such as energy price fluctuations, supply chain disruptions, and a decline in investor confidence. Specific factors, including energy reliance (notably gas imports from Russia), may have contributed to the growth in Germany's CPI, particularly during energy crises. France's economic strategies may alleviate the effects of geopolitical threats via social expenditure and expansionary fiscal measures. France's initiatives to diversify its energy sources may mitigate the influence of geopolitical threats on the CPI in comparison to Germany and Italy. The fragility of Italy's economic structure, elevated public debt, and reliance on oil may amplify the effects of geopolitical threats on the CPI. Geopolitical risks may persistently exert inflationary pressures over the long term, particularly over enduring alterations in energy supplies or security apprehensions. These effects may be more pronounced, particularly in more vulnerable economies like Italy. In economies like Germany and France, modifications to energy policy and economic resilience may mitigate these consequences.

4.4. Robustness check: time point estimation

We conducted a more in-depth analysis of static impulse responses at various time points, akin to the traditional approach used in VAR model impulse response analysis. The key distinction lies in the ability of the former to consider multiple time points, presenting static impulse responses based on the TVP-VAR model. This analysis serves to test and complement the preceding dynamic impulse results.

We specifically focus on three time points: March 2020 (green), February 2022 (blue), and July 2022 (red). These moments coincide with significant events, namely, the declaration of COVID-19 as a pandemic by the World Health Organization and a drastic fall in oil prices; the outbreak of the Russia-Ukraine war leading to a sudden increase in gas prices; and the initiation of the European Central Bank's tightening monetary policy accompanied by a sharp rise in interest rates.

When we evaluate the commentary based on time, as depicted in Fig. 3-b, it is observed that the declaration of COVID-19 (green dashed line) as a pandemic by the World Health Organization in March 2020 coincided with a decrease in global oil demand. Hence, there is reducing impact on inflation for the sample countries in this case. The travel bans and lockdowns in response to the eruption of the pandemic and worldwide economic downturn are likely to be the cause of mitigating effects [91,92]. During the pandemic, sample countries have experienced significant changes in their inflationary basis due to the striking decline in oil consumption.

Fig. 3.

Different responses of Consumer Price index to Energy Price, Money Supply, and Geopolitics Shocks.

Note: The Green Line indicates the responses in March 2020; the blue line indicates the response in February 2022; the red line indicates the responses in July 2022.

A drop in oil demand has various macroeconomic effects, leading to the mitigation of inflationary pressures. First, firms benefit from lower production and transportation costs, which might reduce the cost-push inflation. Second, consumers take advantage of decreasing gasoline prices and might canalize their increased disposable income to other expenditures, supporting economic activity and mitigating inflation trends. Third and last, the central banks of the sample countries took immediate measures to tackle the economic difficulties of the pandemic. The central banks sustained low interest rates to revive the economy and address inflation. The decrease in oil demand has contributed to a reduction in inflation. Still, it is essential to acknowledge that other factors, including shifts in consumer behavior, disruptions in the supply chain, and governmental measures, impact the overall state of the economy. Collectively, all of these factors contribute to the sophisticated economic ramifications of the pandemic.

Fig. 3a–d presents the Ukraine-Russia war period (represented by the blue line) as the second important impact period. The figures indicate that the war primarily affected inflation via oil prices and geopolitical risks in Fig. 3b and d, respectively. Geopolitical risk and, thereby, disruptions in the energy sector adversely affected the economic environment and inflationary pressures. Moreover, volatility in gas prices in Fig. 3a due to the increasing geopolitical risks impacted production costs and consumer spending leading to influence inflation dynamics. The blue dashed lines in Fig. 3a and d shows that the rise in geopolitical risk and gas restrictions of Russia have affected inflation in the sample countries.

Lastly, focusing on a pivotal moment, the European Central Bank's (ECB) tightening monetary policy, indicated by the red dashed line in Fig. 3c, reveals that while the initial response in all three countries is a reduction in interest rates, over time, this impact diminishes. Moreover, it becomes evident that the effectiveness of these interest rate reductions in influencing inflation wanes, proving insufficient in the long run.

5. Policy implications

The Russia-Ukraine war has significant policy implications, particularly in the domain of energy dependency within the Eurozone. To address cost-push inflation, policymakers must prioritize energy diversification and investment in renewable technologies. Reducing reliance on volatile energy markets is crucial for mitigating production cost shocks. Meanwhile, demand-pull inflation necessitates prudent monetary policy measures. Central banks should balance post-recovery liquidity injections with timely tightening to prevent overheating. A dual-pronged strategy targeting both cost-push and demand-pull sources of inflation is vital for ensuring long-term economic stability. Energy supply from Russia has caused some conflicts and highlighted the inherent risks related to energy dependence on external sources, requiring a fancy policy response. This response should reduce vulnerability to a single supplier and increase sustainable energy practices within the Eurozone. Reducing dependency on specific energy suppliers through diversification is essential at this point, leading to the transition towards green energy in Europe. Reducing dependency to specific energy suppliers through diversification is essential at this point. Geopolitical risk measures, such as the GPR index, serve as critical tools for policymakers to anticipate and mitigate the economic impacts of geopolitical shocks. By monitoring shifts in geopolitical risk levels, governments and central banks can better prepare for inflationary pressures linked to energy price volatility and uncertainty. Future policy frameworks should incorporate dynamic risk assessment tools to enhance resilience against geopolitical and economic disruptions. The war has proved the importance of sustainable energy sources in Europe. Countries in the region have comprehended the need for an all-inclusive shift to sustainable energy sources and have started to surge their renewable energy investments. Energy security has been a crucial target in light of the Russia-Ukraine war, entailing the foundation of strategic reserves, improving infrastructure resilience, and increasing collaborative initiatives among European Union member states. In addition, regional cooperation is pivotal in building a more resilient energy infrastructure. Lastly, The TVP-VAR model in this study is able to identify the country-specific variations in the short-term effects of energy price fluctuations on inflation. For instance, countries that rely on spot prices for energy imports (e.g., Germany and Italy) were found to be more sensitive to inflationary shocks, whereas those relying on long-term energy contracts (e.g., Romania and Slovakia) were less affected. This finding underscores the need for tailored policy approaches that consider these differing vulnerabilities.

6. Conclusion and discussion

This study investigates the impact of energy price shocks, monetary policy, and geopolitical events on CPI in selected European countries. By distinguishing between demand-pull and cost-push inflation, the analysis provides a nuanced understanding of inflation dynamics. Cost-push pressures dominate during periods of heightened geopolitical risks and energy price volatility, as seen in the aftermath of the Russia-Ukraine war. Conversely, demand-pull effects emerge prominently during post-recovery phases, driven by expansive monetary policies and aggregate demand surges. These dual forces underline the complex and time-varying nature of inflationary pressures in the studied economies. We perform the TVP-VAR model to illustrate the dynamic effects of the variables. The contribution of the study to the literature is twofold. First, there is a lack of study regarding the interaction between geopolitical risk and inflation. Only a small number of authors, namely [[9], [10], [11],59] have addressed the issue. Second, there has been inadequate scrutiny of the impact of gas price changes on inflation. In contrast to conventional models, such as SVAR and classical VAR methods, our research adopts a dynamic approach. The TVP-VAR approach captures the evolving relationships between variables, leading to a more detailed and precise depiction of the intricate dynamics. The findings corroborate the notion that increases in energy costs have an inflationary impact, encompassing not only oil but also petrol prices. Moreover, notable geopolitical risks considerably influence the escalation of inflation. We also highlight differences in the impact of geopolitical threats on different economic entities by comparative assessments conducted on the three largest individual economies within the European Union—Germany, France, and Italy. This conclusion supports earlier research that suggests increased uncertainty increases inflation and sheds light on the complex relationship between uncertainty and inflation [[9], [10], [11],59].

The major difficulty confronting countries and leading to inflation – or perhaps stagflation (a combination of inflation and sluggish economic growth) in some economies – is the massive increase in energy prices. Between 2010 and 2020, the Dutch TTF gas future price, which is generally 20 euros per MWh, rose to a record high of 187 euros per MWh in 2021. The post-COVID-19 surge in demand and insufficient inventories drove this increase. With the outbreak of the Russia-Ukraine war, the Dutch TTF price shattered all previous records, reaching €345/MWh on March 7. Faced with this increase, for instance, France's budget committed to lowering home energy bills is expected to reach at least 75 billion euros between 2022 and 2023 through programs such as the energy voucher and the tariff shield [93]. Furthermore, the pandemic has altered economic agents' conduct, resulting in a "cautionary" or "wait and see" approach, slowing the transmission of monetary policy to inflation in most economies [94]. COVID-19 has had an impact on inflation expectations, consumer spending patterns, and monetary policy transmission.

According to previous research, economic structure and oil intensity/dependency may determine the extent to which changes in oil prices affect the economy. Increasing oil prices can lead to inflation depending on the significance of oil as an energy source. Countries with a higher reliance on oil may be more affected by fluctuations in oil prices than those with a lesser reliance on oil. As an example, when oil prices rise, European economies are more vulnerable to wage-price spirals than American ones. Europe is more sensitive to spikes in oil prices than the US, despite lower energy and oil use, due to the strength of its labor unions, which are actively seeking greater wages [57].

Nonetheless, because of the ECB's crucial role in handling price shocks and crises, inflation remains relatively low in Europe's three major economies, notably Germany, France, and Italy. These measures have maintained inflation substantially below the levels seen in most European economies. Furthermore, independent energy sources have reduced France's reliance on fossil fuel products, making it less sensitive to variations in energy prices. For example, starting in 2021, nuclear power will provide 69.33 percent of French electricity, compared to 14.8 percent in the UK and 11.8 percent in Germany. France's, heavily dependent on nuclear energy, aggressive actions to diversify its energy sources have been crucial in mitigating inflation and vulnerability to fluctuations in energy prices France's proactive efforts to broaden its energy supplies, namely its significant dependence on nuclear power, have been essential in reducing inflation and vulnerability to fluctuations in energy prices. France's measures have strengthened the country's energy security and sustainability. Also, the measures have helped to reduce inflation in France than in other European countries. Yet, inflation still tends to be affected by volatile energy prices in the sample countries compared to others in the European region.

To conclude, this study provides insights to comprehend the factors affecting the impact of inflation in European countries. The analysis clarifies the role of oil and gas prices, geopolitical events, and monetary policy decisions on inflation trends. Considering temporal dynamics while conducting economic analysis across time is essential. The results highlight the necessity for flexible policies that take into consideration changing economic circumstances, adding to the larger discussion on methods for managing inflation. The nuanced responses observed across different periods underscore the importance of considering temporal dynamics in economic analyses. The integration of the Caldara-Iacoviello GPR index enhances the robustness of this study by providing a systematic and credible measure of geopolitical risks. Its alignment with existing literature establishes a strong empirical foundation, while its application to European economies highlights the differentiated impacts of geopolitical shocks. Expanding the use of region-specific or composite risk measures could further enrich future studies, offering deeper insights into the interplay between geopolitical risks and inflation.

Despite its significant contributions, this study is subject to several limitations. First, the TVP-VAR model, while robust in capturing time-varying dynamics and country-specific interactions, relies on certain assumptions about variable behavior that may not fully reflect the complexities of real-world economic relationships. Second, the analysis focuses on Germany, France, and Italy—the largest economies in the EU—thereby potentially limiting the generalizability of the findings to smaller or non-EU economies with differing energy dependencies. Third, while gas and oil prices are examined in depth, other energy sources, such as renewable energy, and structural shifts in energy policies are beyond the scope of this study. Finally, although country-specific geopolitical risk (GPR) indices are employed to enhance granularity, these indices may not fully capture micro-level variations or unique national contexts that could influence inflation dynamic”s. The existence of other factors in the actual dynamics might be possible. Second, generalizing the findings to a broader context should be done cautiously, as the study only covers a few European nations. Future studies might incorporate different factors or elements and extend the sample size for more solid results.

Consent to participate

Authors have agreed to authorship, read and approved the manuscript, and given consent for submission of the manuscript.

Consent to publish

The authors have given consent for subsequent publication of the manuscript.

Data availability statement

Data will be made available on request. For requesting data, please write to the author.

Ethical approval

The manuscript does not report on or involve the use of any animal or human data, etc.

Funding

The funding does not apply to this research.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix.

Table 2.

Descriptive Statistics

Country	Variables	Mean	Minimum	Maximum	Std.dev.	Jarque-Bera
Germany	CPI	0.021	−0.010	0.088	0.021	95.208a
	M3	0.054	−0.007	0.093	0.019	11.294a
	GPR	0.394	−0.762	6.615	1.190	622.282a
France	CPI	0.016	−0.003	0.062	0.016	70.958a
	M3	0.050	−0.024	0.178	0.038	36.395a
	GPR	0.283	−0.772	8.050	1.049	3465.59a
Italy	CPI	0.019	−0.005	0.118	0.026	149.884a
	M3	0.030	−0.085	0.110	0.035	10.874a
	GPR	0.663	−0.962	7.578	1.646	188.531a
Energy Prices	GAS	0.456	−0.853	6.356	1.419	338.619a
	OIL	0.076	−0.741	2.526	0.431	281.380a

Note: “a” denotes significance levels of 1 %.

The TVP-VAR model is illuminated as follows:

$$y_t=c_t+{\beta }_{1,t}{y}_{t-1}+\cdots \cdots +{\beta }_{s,t}{y}_{t-s}+{{\rm A}}_t^{-1}\sum _t{\epsilon }_t$$ (1)

where $y_t$ is a $k\times 1$ vector of observed variables ${\beta }_{i,t}$ are $k\times k$ matrices of time-varying coefficients; ${\epsilon }_t$ is i.i.d(0, $I_k)$; $A_t$ is the lower triangular matrix

$$A_t=\left(\begin{array}{cccc}1& 0& \cdots & 0\\ {\alpha }_{21,t}& 1& \ddots & ⋮\\ ⋮& \ddots & \ddots & 0\\ {\alpha }_{k1,t}& \cdots & {\alpha }_{kk-1,t}& 1\end{array}\right)$$ (2)

and $\sum _t$ is the diagonal matrix:

$${\sum }_t=\left(\begin{array}{cccc}{\sigma }_{1,t}& 0& \cdots & 0\\ 0& \ddots & \ddots & ⋮\\ ⋮& \ddots & \ddots & 0\\ 0& \cdots & 0& {\sigma }_{k,t}\end{array}\right)$$ (3)

Moreover, the model can be expressed as:

$$y_t=X_t{\beta }_t+{{\rm A}}_t^{-1}\sum _t{\epsilon }_t,t=1,\dots \dots \dots ,n.$$ (4)

where the par all time varying. Let ${\alpha }_t$ be a stacked vector of the lower triangular elements in $A_t\text{,}$ and $h_t={\left(h_{1,t,\cdots \cdots \cdots \cdots }{h}_{kt}\right)}^{\prime }$ with $h_{jt}=\mathit{log}{\sigma }_{jt}^2$ for $j=1,\cdots \cdots ,k$ and $t=s+1,\cdots \cdots ,n\text{.}$ The parameters are considered to adhere to a random walk pattern in the following manner:

$${\beta }_{t+1}={\beta }_t+{\mu }_{\beta t}$$ (5)

$${\alpha }_{t+1}={\alpha }_t+{\mu }_{\alpha t}$$ (6)

$$h_{t+1}=h_t+{\mu }_{ht}$$ (7)

$${\beta }_{s+1}\sim N\left({\mu }_{{\beta }_0},\sum _{{\beta }_0}\right)$$ (8)

$${\alpha }_{s+1}\sim N\left({\mu }_{{\alpha }_0},\sum _{{\alpha }_0}\right)$$ (9)

$$h_{s+1}\sim N\left({\mu }_{h_0},\sum _{h_0}\right)$$ (10)

The incorporation of a random walk process assumption accommodates the potential for both temporary and permanent shifts in the coefficients. This enables the model to capture both gradual alterations and structural breaks. The variance-covariance matrix of the innovations in the model exhibits a block diagonal structure.

$$\left(\begin{array}{l}{\epsilon }_t\\ \begin{array}{c}{\mu }_{\beta t}\\ {\mu }_{at}\\ {\mu }_{ht}\end{array}\end{array}\right)\sim \left(0\left(\begin{array}{cccc}1& 0& 0& 0\\ 0& & 0& 0\\ 0& 0& & 0\\ 0& 0& 0& \end{array}\right)\right)$$ (11)

Table 3.

BDS Test Results

Variable	m = 2	p-value	m = 3	p-value	m = 4	p-value	m = 5	p-value	m = 6	p-value
Panel A Germany
CPI	24.918	0.000	25.771	0.000	26.510	0.000	27.905	0.000	30.009	0.000
M3	22.307	0.000	22.793	0.000	23.626	0.000	25.030	0.000	26.923	0.000
GPR	5.668	0.000	6.532	0.000	6.471	0.000	6.711	0.000	6.957	0.000
Panel B France
CPI	19.931	0.000	20.863	0.000	22.063	0.000	23.964	0.000	26.641	0.000
M3	21.518	0.000	22.662	0.000	23.846	0.000	25.689	0.000	28.357	0.000
GPR	4.963	0.000	4.752	0.000	4.530	0.000	4.450	0.000	4.235	0.000
Panel C Italy
CPI	18.949	0.000	19.744	0.000	20.790	0.000	22.532	0.000	25.022	0.000
M3	22.761	0.000	22.417	0.000	26.113	0.000	28.519	0.000	31.813	0.000
GPR	5.076	0.000	5.411	0.000	5.438	0.000	5.441	0.000	5.630	0.000
Panel D Energy Prices
OIL	24.918	0.000	25.771	0.000	26.510	0.000	27.905	0.000	30.009	0.000
GAS	15.092	0.000	15.891	0.000	16.903	0.000	18.332	0.000	20.278	0.000

Table 4.

Nonlinear Unit Root Test Results

Variables	KSS Stat.	Prob.	Lags
Panel A Germany
CPI	−2.427a	(0.000)	14
M3	−1.279a	(0.001)	12
GPR	−2.869a	(0.000)	3
Panel B France
CPI	−2.511a	(0.000)	14
M3	−2.665a	(0.000)	16
GPR	−3.433a	(0.000)	12
Panel C Italy
CPI	−4.654a	(0.000)	13
M3	−0.980a	(0.002)	15
GPR	−1.141a	(0.001)	16
Panel D Energy Prices
OIL	−9.052a	(0.000)	12
GAS	−7.132a	(0.000)	12

Note: “a” denotes significance levels of 1 %, and the Akaike Information Criterion determines the optimal lag length.

Fig. 2.

Sample autocorrelations, paths, and posterior densities of Markov Chain Monte Carlo estimation.