The effect of gas and oil prices on economic confidence: An empirical analysis for euro area

Abstract

Energy prices are determinants of economic confidence by directly affecting production, consumption, and transportation costs. Nevertheless, countries’ energy policies and macroeconomic and geopolitical developments may moderate the impact of energy prices on economic confidence. This study analyzes the effects of oil and gas prices on business and confidence in the Euro Area over the period 1994–2023. We employ non-linear correlation and regression models to investigate the spillover relationship between the variables. For this purpose, the Markov Switching Regression (MSR) model is employed to analyze the spillover effect of oil and gas prices on both consumer and business confidence. The empirical results show that oil and gas prices harm consumer confidence in all regimes. Oil prices have a positive effect on business confidence in a volatility regime. However, oil prices negatively affect business confidence in low volatility, but this effect is insignificant. The effect of gas prices on the business confidence index is negative and statistically significant during periods of high volatility. Besides, gas prices positively affect business confidence for low volatility.

Keywords

Business confidence consumer confidence oil prices gas prices Markov switching model

Introduction

Energy is one of the main determinants of economic activities. Thus, policymakers, managers, and researchers analyze the impact of changes in energy parameters on macroeconomic and business performance.¹ The volatility of natural gas and crude oil prices, the two essential components in the production process, has a noticeable impact on the real economy.² However, the source of this effect is controversial. Supply-side perspectives interpret the effect of energy on the real economy as external changes in the aggregate supply curve. On the other hand, demand-side views argue that energy prices can affect purchasing power and consumer perception.^3,4

The demand-side impact of energy prices may determine households from different aspects. Firstly, the rise in energy prices causes consumers to allocate an essential portion of their budgets to energy expenditures.⁵ Energy prices can also indirectly cause inflation. Producers pass on their costs to consumers, which increases with energy prices. Studies show that energy prices are one of the indicators of the economic outlook. Rising energy prices can send negative signals to consumers about the economy's future. Therefore, consumer confidence may decline.⁶ Stable and low energy prices increase confidence, with consumers perceiving that they can manage their budgets more efficiently. Stable and low energy prices increase confidence, with consumers perceiving that they can manage their budgets more efficiently. Thus, the demand effect may positively affect the real economy.⁷

Supply-side views argue that energy prices affect markets through business confidence. Energy costs reduce the operational costs of businesses. Energy cost pressures can reduce profitability.⁸ When energy costs experience a substantial increase in a particular region or country, businesses operating there may encounter a competitive disadvantage compared to their counterparts in regions with lower energy prices. As a result, this could diminish their competitiveness in the global market, as companies in regions with lower energy costs may possess a pricing advantage.¹ Like consumer confidence, high energy costs increase business uncertainty and undermine planning. Volatility in energy prices can determine the sustainability perception and efforts of businesses. Sustainability efforts are also an important variable affecting business performance.⁹

The impact of energy on economic and environmental sustainability has been analyzed in recent years. Energy is an essential input for economic activities. Therefore, energy prices are one of the determinants of economic soundness, especially inflation.¹⁰ The impact of energy on economies can also be considered in terms of environmental sustainability. Combating climate change, which threatens life and harms economic activities, leads to significant transformations in energy investments. Nevertheless, sustainability initiatives may clash with economic growth.¹¹ Thus, energy prices are a crucial driver of economic and environmental sustainability.¹²

The impact of energy prices on the real economy is analyzed from three perspectives in the literature. Most of the literature analyzes the impact of oil and gas price shocks on business or consumer confidence.¹³ Besides, studies are analyzing the impact of oil and gas price shocks on investor confidence.^14,15

This study investigates the impact of oil and gas price shocks on business and consumer confidence in the Euro Area for 1994–2023. After we found that the investigated variables showed non-linear properties, we used non-linear correlation and regression models to investigate the spillover relationships. For this purpose, the Markov Switching Regression model is employed to analyze the spillover effect of oil and gas prices on consumer and business confidence. Also, for dynamic correlation analysis, the DCC-GARCH model is employed. Studies examining the impact of oil and gas prices on confidence are limited. Our model analyzes both oil and gas prices. In addition, analyzing the supply and demand side of energy prices is another essential originality of the study. The period and the Euro Area sample can reveal significant historical effects, especially the effects of COVID-19 and the Russian-Ukrainian war. This study is a novel contribution in three aspects. Firstly, energy prices in Europe are evaluated in the context of behavioral economics. Secondly, the impact of energy prices on economic confidence is analyzed from consumer and business dimensions. This fills an essential gap in the literature. Finally, our model investigates the impact of two important conventional energy sources, oil and gas, on European economic confidence.

We present the theoretical background in Theoretical background, and Literature review deals with the literature. Data and methodology presents the methodology and data, and Empirical results discusses the results. Finally, Conclusion and policy implications concludes.

Theoretical background

The impact of energy prices on the real economy is discussed in the economic theories. One of the significant debates in the literature is whether energy prices are endogenous or exogenous. Early studies argue that the economic impact of energy prices is exogenous. These studies emphasize that energy prices are generally set in energy markets, shaped by global supply and demand dynamics, geopolitical events, natural resource availability, and international trade policies. These factors originate from outside the economic system and are not directly affected by the decisions made by economic agents within the economy. While fluctuations in energy prices, including oil or gas, can profoundly impact economic variables like inflation, production costs, and consumer purchasing power, the fundamental drivers of energy prices are typically regarded as exogenous. Therefore, studies analyze the effects of energy prices on macroeconomic data.^16,17

Studies in the 2000s argue that energy prices have an endogenous effect, not an exogenous effect. Various economic factors can shape energy prices, such as the interplay between demand and supply, technological advancements, production costs, government policies, and market competition. These factors are inherent to the economic system and are subject to the influence of economic interactions and the decisions made by consumers, producers, and policymakers. Studies arguing that energy is endogenous show that the real economy and energy prices affect each other.^18,19

We investigate the effect of energy price shocks on the real economy from a business and consumer perspective. Economic theories discuss the impact of energy prices on the real economy. One of these theories is the cost-push theory. According to the cost-push theory, energy price variations can impact the economy's general price level. When energy prices increase, it raises production costs for businesses since energy is a vital input for numerous industries. Elevated energy expenses can lead to higher production costs for goods and services, which may be passed on to consumers through increased prices. This can create inflationary pressures and reduce consumers’ purchasing power, potentially decreasing economic activity.²⁰

Economists such as John Maynard Keynes and Milton Friedman have examined the connection between energy prices and aggregate demand. Keynesian economics highlights the significance of aggregate demand in shaping financial results. In contrast, Friedman's monetarist approach focuses on the influence of changes in the money supply on aggregate demand.²¹

Energy prices can affect investment and productivity within the economy. When energy prices rise, it elevates business production costs, diminishing their profitability and potentially discouraging new investments. Furthermore, energy-intensive industries may witness reduced productivity due to higher input costs. Consequently, this can result in decreased levels of capital investment, a decline in productivity growth, and a slowdown in overall economic activity.²²

Fluctuations in energy prices can impact the distribution of resources across various sectors of the economy. When energy prices increase, it can make energy-intensive industries relatively costlier compared to sectors with lower energy requirements. Consequently, firms and consumers might alter their consumption and production patterns, favoring less energy-intensive goods and services. This reshuffling of resources can have implications for employment levels, investment choices, and overall economic output.²³

The theories explaining the impact of energy prices on economic confidence are the theoretical framework of our analysis. One of these theories is the Real Business Cycle Theory. Real business cycle theory posits that energy price shocks play a significant role in the ebbs and flows of the business cycle. According to this theory, variations in energy prices impact productivity and production costs, consequently triggering shifts in output, employment, and overall economic activity. Positive energy price shocks can stimulate economic growth, whereas adverse shocks, like a sudden surge in energy prices, can result in economic downturns. The theory highlights the importance of supply-side factors and productivity shocks in shaping the dynamics of business cycles.²⁴

New Keynesian economics integrates principles from Keynesian economics while emphasizing the persistence of price stickiness and nominal rigidities. According to this framework, energy price shocks can exert short-term influences on the economy by affecting production costs and inflation. Positive energy price shocks may temporarily reduce production costs and inflation, generating expansionary effects. Conversely, adverse energy price shocks raise costs and inflation, possibly leading to economic slowdowns. New Keynesian models frequently examine the repercussions of energy price shocks within the realms of monetary policy and the dynamics of price adjustments.²⁵

The resource curse theory examines the relationship between natural resource abundance (including energy resources) and economic outcomes. It suggests that countries heavily reliant on energy exports face specific challenges, such as Dutch disease and rent-seeking behavior. Energy price shocks can exacerbate these challenges by introducing volatility in export revenues, leading to economic instability and reduced economic diversification. This theory emphasizes the structural and institutional factors that shape the economic consequences of energy price shocks in resource-rich countries.²⁶ In addition, Approaches such as Marginal Utility Theory and Permanent Income Hypothesis explain the impact of energy price shocks on the economic confidence index with income and wealth.²⁷

The impact of oil and gas prices on economic confidence has limited literature. Studies analyze economic confidence only in terms of supply or demand. Our model contributes to the behavioral dimension of the issue by analyzing energy prices with economic confidence. Analyzing economic confidence in terms of business and confidence fills an essential gap in the literature. Thus, the impact of energy prices on producers and consumers can be observed together, and original findings are presented in the literature, especially on business confidence.

Literature review

The impact of energy prices on the real economy has extensive literature, and these studies go back decades.²⁸ Recent studies focus on the price effects of energy demand and supply and their determinants on macroeconomic variables and stock markets.^29,30

One of the issues investigated in these studies is economic confidence.⁵ Studies analyze the effect of energy prices on the real economy from the supply and demand sides. Thus, the literature is divided into business and consumer confidence. These studies usually analyze the oil and gas prices by employing VAR models.

This section reviews the literature on the impact of energy prices on economic confidence. For this purpose, we first review studies that analyze the effect of energy prices on economic and environmental sustainability. Then, studies analyzing the issue with consumer and business confidence dimensions are included.

The effect of energy prices on economic and environmental sustainability

As a vital input for economic activities, energy mainly affects economic stability, including inflation rates. Additionally, energy is pivotal in environmental sustainability, given its connection to climate change and the necessary transformations in energy investments. However, sustainability initiatives aiming to combat climate change sometimes conflict with the pursuit of economic growth. Therefore, energy prices emerge as a critical determinant influencing economic and environmental sustainability.¹²

Studies find that energy prices are decisive for countries’ development and mitigate against climate change. Wang et al.¹⁰ argue that the negative effect of oil prices on the economic development of exporting and importing countries. Cologni and Manera³¹ show that oil prices affect financial stability and cash flow in G7 countries. Akinlo and Apanisile³² analyze the macroeconomic effects of oil prices in Sub-Saharan countries. Empirical results show that oil-exporting countries are positively affected by oil prices. Li and Leung³³ argue that there is an interaction between energy prices, renewable energy investment, and growth in Europe. Apergis and Payne³⁴ find that renewable energy has an essential impact on economic growth. Benkraiem et al.³⁵ find that gas prices affect the S&P500O. According to Oberndorfer,³⁶ the effect of oil and coal prices on Eurozone stock markets is statistically significant.

Mohamued et al.¹¹ find that energy prices affect carbon emissions. This effect varies according to countries’ status in energy trade. Aguirre and Ibikunle³⁷ show that conventional energy prices may determine government policies and investments in renewable energy. Shan et al.³⁸ analyze the impact of energy prices on environmental sustainability in OECD countries using CS-ARDL and AMG techniques. According to the empirical results, energy prices are determinants of carbon emissions.

Similarly, Ike et al.³⁹ argue that energy prices affect carbon emissions in G7 countries. Anser et al.⁴⁰ highlight that alternative and nuclear energy may initially contribute to carbon damage. At the same time, carbon emissions decrease during the later phases of atomic energy expansion, underscoring the importance of nuclear power growth for achieving long-term sustainable development.

Studies analyze the economic and economic sustainability of energy from different perspectives. The most studied are macroeconomic variables, financial markets, renewable energy investments, and carbon emissions. However, the impact of energy prices on economic confidence has limited literature.

The impact of energy prices on consumer confidence

The significance of consumer sentiment in driving economic activity has been acknowledged for a considerable time. Forecasters carefully track changes in consumer sentiment, and these measures are commonly employed as leading indicators in forecasting models. Studies include energy prices in consumer confidence models.

A significant part of the literature examines the effect of energy prices on the macroeconomic and stock markets. Edelstein and Kilian⁶ analyze the effect of gasoline prices on consumer expenditure in the US. VAR models show that gasoline price shock affects consumer confidence and determines consumer spending in the US. Güntner and Linsbauer¹ examine whether energy supply and demand shocks affect consumer sentiment in the US. Authors emphasize that Consumer sentiment is minimally affected by oil supply shocks. In contrast, other oil demand shocks exert a substantial negative influence for up to two years, and aggregate demand shocks manifest various effects. Alsalman and Karaki⁴¹ research the impact of structural oil shocks on personal consumption expenditures (PCE) in the US by employing the SVAR model and discover that the nature of crude oil price shocks significantly influences the response of PCE. Empirical results show that positive oil demand shocks lead to a negative response in aggregate PCE, whereas oil supply shocks have a limited effect. Geiger and Scharler⁴² apply a survey of US consumers to determine the effect of oil price shocks on the macro economy. Survey findings indicate that expectations of inflation and unemployment rise in reaction to shocks, leading to elevated crude oil prices.

Some studies examine the impact of energy price shocks on consumer confidence indices. Clerides et al.⁵ investigate the relationship between oil prices, gasoline prices, and consumer sentiment in the Euro Area. VAR models show that gasoline price shock negatively affects consumer sentiment. Li and Ouyang⁴³ find that consumer and entrepreneur sentiment experiences significantly positive effects when oil supply and demand shocks increase oil prices. Binder and Makridis⁴⁴ analyze the effect of gas price shock on consumer sentiment in the US and European countries. According to VAR models, gas price shock harms consumer sentiment. Su et al.⁴⁵ examine the relationship between oil prices and consumer confidence in China. The authors argue that the stability of oil prices determines consumer confidence.

Energy price shock and consumer confidence studies mainly focus on macroeconomic data on the stock market. Studies that include the consumer index in the model are limited.

The impact of energy prices on business confidence

Energy price shocks impact businesses in diverse ways, mainly through the emergence of inflationary pressures. Studies analyze these effects. The impact of energy price shocks on business confidence has limited literature than consumer confidence.

The impact of energy prices is explored in the literature by analyzing variables such as the business confidence or consumer index. Adekoya and Oliyide⁴⁶ investigate how oil price shocks affect business confidence in OECD countries with an Augmented Mean Group (AMG) estimator. Bildirici and Badur⁴⁷ examine oil prices and business confidence in India, China, and Russia. According to the Markov Switching Vector Auto-Regressive (MS-VAR) model, there is a significant relationship between oil prices and business confidence in all three countries. de Mendonça and Finn⁴⁸ find that electricity prices harm business confidence in Brazil. Bildirici and Badur⁴⁹ analyze the impact of oil and gasoline prices on business confidence in Turkey and the US. Empirical results show a bidirectional relationship between gasoline price and confidence. Besides, there is a bidirectional causality between oil prices and the confidence index in the US in all regimes.

Energy price shocks can influence investors’ confidence, as these shocks introduce uncertainty and volatility into the market, raising concerns among investors. Notably, sudden and substantial fluctuations in energy prices can have this effect. Moreover, the higher costs of production resulting from elevated energy prices have the potential to impact the profitability and investment decisions of businesses. Hence, studies examine the impact of energy price shocks on investor confidence. Apergis et al.¹⁴ analyze the determinators of investor sentiment in the US. Quantile regression results show the importance of natural gas prices in investor confidence. Li et al.⁵⁰ show that heterogeneous dynamic correlations and lead-lag relationships exist between crude oil prices (shocks) and investor sentiment in China. Degiannakis et al.⁵¹ show that oil and gas price shocks significantly impact stock markets in Europe. Nasim and Downing⁵² argue that energy price shocks negatively affect banking sector profit in G7 countries. In summary, the macroeconomic impacts of energy are analyzed from different perspectives according to the transformation of the economic discipline. Early studies focus on the impact of energy on the economy, its contribution to economic growth, and whether it is exogenous. As a result of environmental sensitivity, the impact of energy on the economy is examined in terms of environmental sustainability. Economic confidence indices have gained importance in recent years by reflecting behavioral theories. Consequently, the effects of energy prices on economic confidence are being investigated.

Gas and oil have an essential role in the energy mix of European countries regarding energy security, economic sustainability, and environmental sustainability. The response of European markets to changes in these energy sources provides essential insights for Europe with a policy of reducing its dependence on these sources. Therefore, studies analyze the impact of energy prices in Europe from different dimensions, mainly macroeconomic indicators. Nevertheless, these two vital resources in the European energy mix must be sufficiently investigated from a behavioral economics perspective. Moreover, the current literature considers the impact of oil and gas prices on economic confidence unidimensionally. This study contributes to the current literature by examining the impact of oil and gas prices on consumer and business confidence. Our findings are original in terms of the impact of energy on behavioral economics and our model.

Data and methodology

In the empirical modeling, we investigate the effects of oil/gas prices and interest rates on both business and consumer confidence for Euro Area countries for covering January 1994–February 2023 periods. We employ the Business and Consumer Confidence Index variables for business and consumer confidence, respectively, and obtained data from the OECD Data website.⁵³ Also, we obtained oil and gas price data from World Bank Monthly Prices.⁵⁴ Finally, for interest rates, we used short-term interest rate variables obtained from the OECD Data website.⁵⁵ Natural logarithms of the variables are employed in the empirical analysis to obtain elasticity coefficients similar to the literature. Table 1 shows the list of variables:

Table 1.

Variables list.

Variable name	Abbreviations	Definition
Business Confidence	LBUS	OECD Business Confidence Index (Monthly).
Consumer Confidence	LCONS	OECD Consumer Confidence Index (Monthly).
Gas Prices	LGAS	Europe Natural Gas Prices (Monthly).
Oil Prices	LOL	Crude Oil Prices (Monthly).
Short-Term Interest Rate	INT	Short-term interest rates refer to the rates at which financial institutions engage in short-term borrowings or at which short-term government paper is issued or traded in the market monthly.

Our model can contribute to the behavioral dimension of the impact of energy on economic sustainability. Analyzing the two central conventional energy prices and the supply-demand side of the economy can reflect the economic outlook, energy policies, and geopolitical developments. Additionally, there is no study analyzing these variables together. Therefore, our results are original.^5,14

This study also contains essential findings in terms of sample. The European Union (EU) has a substantial political, economic, and environmental impact on the world. The European Union is an important center of international relations, promoting democracy, human rights, and the rule of law in its external relations. It serves as a model for these values and encourages their global adoption. The European Union (EU) is the world's largest integrated economic and trade bloc. As one of the world's largest trading partners, the EU is an important center for economic stability and a decisive role in world monetary policy.⁵⁶ Energy dependency is a significant handicap for the EU economy, reaching 60%. Besides, the Russian-Ukrainian war highlighted the role of energy geopolitics in international relations. During the war period, the EU's energy security was jeopardized, and the EU reviewed its energy policies.⁵⁷ Another impact of energy on the EU is related to the environment. The EU has organized the Green Deal to contribute to the fight against climate change around the world and increase the contribution of the environment to production. Clean energy sources and revising the energy mix are transformations highlighted in the Green Deal.⁵⁸

Energy prices in the EU can affect households and businesses through environmental, economic, and geopolitical dimensions. An analysis of the supply and demand side of the impact of conventional energy prices on economic confidence in the central and energy-dependent EU may contain essential findings. Table 2 presents the descriptive statistics.

Table 2.

Descriptive statistics.

	LBUS	LCONS	LGAS	LOL	INT
Mean	4.607	4.604	1.758	3.813	0.994
Median	4.608	4.606	1.708	3.975	1.383
Maximum	4.641	4.629	4.249	4.897	2.244
Minimum	4.545	4.557	0.454	2.282	−7.601
Std. Dev.	0.016	0.015	0.699	0.681	1.074
Skewness	−0.903	−0.655	0.643	−0.326	−2.658
Kurtosis	4.939	3.032	3.414	1.901	15.674
Jarque-Bera	101.531	24.847	26.359	23.612	2730.948
Probability	0.000	0.000	0.000	0.000	0.000
Observations	347	347	347	347	347

Notes: LBUS, LCONS, LGAS, LOIL, and INT represent the natural logarithm of the Business Confidence Index, Consumer Confidence Index, gas price, oil price, and interest rates, respectively.

According to descriptive statistics, the kurtosis values indicate that the distributions of variables tend to be flat. The negative skewness values show that the marginal distributions are skewed to the left. According to Jarque–Bera, values are highly significant. Series are generally not normally distributed. Figures 1 and 2 present a graphical representation of the variables.

Figure 1.

Graphical representation of dependent variables.

Figure 2.

Graphical representation of independent variables.

For empirical analysis, to analyze the dynamic relationship between oil and gas prices and interest rates on business and consumer confidence, we employed dynamic correlation and dynamic regression-based models. The regression model used in the empirical model is denoted in Equations 1 and 2, respectively.

L C O N S_{t} = α_{0} + α_{1} L O I L_{t} + α_{2} L G A S_{t} + α_{3} I N T_{t} + ε_{t}

(1)

L B U S_{t} = β_{0} + β_{1} L O I L_{t} + β_{2} L G A S_{t} + β_{3} I N T_{t} + ε_{t}

(2)

where

L C O N S_{t}

is the natural logarithm of the consumer confidence index,

L B U S_{t}

is the natural logarithm of the business confidence index,

L O I L_{t}

is the natural logarithm of the oil price,

L G A S_{t}

is the natural logarithm of the gas price, and

I N T_{t}

is the interest rates at time t.

ε_{t}

denotes the error term.

For empirical modeling, we first investigate stationarity properties of the investigated variables by employing the conventional Ng-Perron Test and Zivot and Andrews⁵⁹ test, which take structural break into account.

After the stationarity check, we investigate the linearity properties of the variables by employing the BDS test, which indicates non-linearity properties for the investigated variables.

For this reason, we employ non-linear regression and correlation models to investigate the spillover relationship between the variables.

To investigate the dynamic correlation relationship between consumer and business confidence indexes and oil and gas prices, we utilize the DCC-GARCH model. The DCC model is an extension of Bollerslev's⁶⁰ constant conditional correlation model, initially developed by Engle.⁶¹ A critical superiority of the DCC model is its ability to capture changing conditional correlations over time, enabling the exploration of dynamic correlations among the variables of interest. Furthermore, the DCC model yields robust results even in heteroscedasticity.⁶² However, critics of the DCC-GARCH model, as expressed by Caporin and McAleer,⁶³ argue that while it could be helpful as a filter or diagnostic check, not as a basic model.

After dynamic correlation analysis, we investigate dynamic regression analysis between the variables by employing the MSR model. For dynamic regression analysis, we employ a non-linear MSR Model. In the MSR model,^a it is an enhanced version of simple exogenous probability models. Two regimes, namely high and low volatility, are defined and distinguished by their variances. Utilizing the MSR model offers superiority over single regime models, such as minor volatility persistence and superior forecasting performance.^65–67 The transition between Regime 1 and 2 (or vice versa) is determined based on the unobserved state variable s_t, which follows a first-order Markov process represented as $P r (s_{t} = j | s_{t - 1} = i) = p_{i j}$ .⁶⁸ The simple version of the two states transition probability matrix is denoted in Equation 3.

p = [\begin{matrix} p_{11} & p_{12} \\ p_{21} & p_{22} \end{matrix}], where \sum_{j = 1}^{2} p_{i j} = 1 for every row i .

(3)

The MSR model setup for our study is presented in Equations (3)–(6):

Low volatility regime

L C O N S_{t} = α_{1, 0} + α_{1, 1} L O I L_{t - i} + α_{1, 2} L G A S_{t - i} + α_{1, 3} I N T_{t - 1} + ε_{1, t}

(4)

L B U S_{t} = β_{1, 0} + β_{1, 1} L O I L_{t - i} + β_{1, 2} L G A S_{i} + β_{1, 3} I N T_{t - 1} + ε_{2, t}

(5)

High volatility regime

L C O N S_{t} = α_{2, 0} + α_{2, 1} L O I L_{t - i} + α_{2, 2} L G A S_{t - i} + α_{2, 3} I N T_{t - 1} + ε_{3, t}

(6)

L B U S_{t} = β_{2, 0} + β_{2, 1} L O I L_{t - i} + β_{2, 2} L G A S_{t - i} + β_{2, 3} I N T_{t - 1} + ε_{4, t}

(7)

where (

α_{10}

α_{20}

β_{10}

β_{20}

) are regime dependent constants (

α_{11}

α_{21}

α_{12}

α_{22}

α_{13}

α_{23}

β_{11}

β_{21}

β_{12}

β_{22}

β_{13}

β_{23}

) are the autoregressive coefficients. (

ε_{1 t}

ε_{2 t}

ε_{3 t},

ε_{4 t}

) are the white-noise error terms, respectively.

Empirical results

Before introducing the model, we determine the stationary of variables. The conventional Ng and Perron⁶⁹ test and structural break Zivot and Andrews⁵⁹ test are employed for stationarity check. Both Ng and Perron⁶⁹ and Zivot and Andrews⁵⁹ unit root tests indicate that all variables are I(1)^b.

After the stationarity check, we analyze the variables’ linearity by using the BDS test. BDS test results are denoted in Table 3.

Table 3.

BDS results.

Dimension	LCONS	LBUS	LOL	LGAS	INT
2	0.179 [0.000]	0.180 [0.000]	0.187 [0.000]	0.186 [0.000]	0.192 [0.000]
3	0.297 [0.000]	0.297 [0.000]	0.317 [0.000]	0.312 [0.000]	0.326 [0.000]
4	0.371 [0.000]	0.370 [0.000]	0.406 [0.000]	0.396 [0.000]	0.418 [0.000]
5	0.416 [0.000]	0.414 [0.000]	0.465 [0.000]	0.452 [0.000]	0.480 [0.000]
6	0.441 [0.000]	0.435 [0.000]	0.504 [0.000]	0.488 [0.000]	0.521 [0.000]

Notes: The brackets indicate P-values.

The BDS test results indicate the rejection of the null hypothesis, which means dependence of the variables linearly. This denotes the non-linearity of the investigated variables.

After we detect the non-linear properties of the investigated variables, we employ dynamic non-linear correlation and regression models for empirical modeling. To investigate the time-varying correlation relationship between consumer and business confidence indexes and oil and gas prices, we employ the DCC-GARCH model, which has superior properties over constant correlation models. Later, taking the criticism in the literature about the DCC-GARCH model into account, it could be used as a filter or diagnostic check rather than the primary model. We employed the MSR model to examine the dynamic regression relationship after dynamic correlation analysis. We prefer the MSR model due to its advantage over single regime models, including minor volatility persistence and superior forecasting performance.

Dynamic correlation analysis: DCC-GARCH model

We first investigated the linear static correlation relationship between the variables. Table 4 presents the static correlation results.

Table 4.

Static correlation coefficients.

	LCONS	LBUS	LGAS	LOIL	INT
LCONS	1	0.496	−0.521	−0.439	−0.094
LBUS	0.496	1	0.225	0.178	−0.172
LGAS	−0.521	0.225	1	0.853	−0.214
LOIL	−0.439	0.178	0.853	1	−0.338
LINT	−0.094	−0.172	−0.214	−0.338	1

After a static correlation check, the DCC-GARCH model is applied to explore the dynamic correlation coefficients between consumer and business confidence and oil and gas prices. The resulting dynamic time-varying correlation coefficients, obtained from the DCC-GARCH model, are shown in Figures 3–6 below.

Figure 3.

Dynamic correlation coefficients between consumer confidence and oil price.

Figure 4.

Dynamic correlation coefficients between consumer confidence and gas price.

Figure 5.

Dynamic correlation coefficients between business confidence and oil price.

Figure 6.

Dynamic correlation coefficients between business confidence and gas price.

Figures 3–6 show the correlation between consumer confidence and oil/gas prices increased in 1996–1998. There is a high correlation between consumer confidence and oil/gas prices in 2002–2006. There is a low correlation between consumer confidence and oil/gas prices in 2008–2010.

The 2008 financial crisis and the post-crisis period may explain this correlation. Finally, a high correlation is observed between the two variables in 2019–2021. The correlation between business confidence and oil/gas prices increased from 1996–1999. There is a high correlation between business confidence and oil/gas prices in 2001–2004. Finally, a high correlation is observed between the two variables in the 2016–2018 and 2021–2022 periods.

Some milestones affected the energy sector in the late 1990s and early 2000s. Geopolitical risks, the Asian banking crisis, fluctuations in energy production, and OPEC interventions in the late 90s may explain the high correlation during this period. Besides, during this period, developed countries implemented significant energy regulations.^70–72

Environmental regulations, starting with the Kyoto Protocol, the increase in renewable energy investments, the Energy Policy Act of the USA, and the Iraq War affected countries’ energy policies in the early 2000s.^73,74 Sustainability activities that gained momentum with the Green Deal, the COVID-19 pandemic, and the Russian-Ukrainian War are the factors affecting oil and gas prices in the 2018–2022 period.^75–77

Dynamic regression analysis: MSR model

After the dynamic correlation check, we employed the MSR model to investigate the dynamic regression relationship between the variables. MSR model results for consumer and business confidence equations are presented in Table 5.

Table 5.

MSR model results for economic confidence.

	1) Dependent variable	Consumer confidence
	Regime 1: high volatility	Coef.	P Value
LOL		−0.065*	0.000
LGAS		−0.099*	0.000
C		4.641*	0.000
	Regime 2: low volatility	Coef.	P Value
LOL		−0.043*	0.002
LGAS		−0.079*	0.000
C		4.647*	0.000
	Control variable	Coef.	P Value
INT		−0.014*	0.001
Observations		350
	2) Dependent variable	Business confidence
	0 Regime 1: high volatility	Coef.	P Value
LOL		0.034*	0.000
LGAS		−0.038*	0.000
C		4.525*	0.000
	Regime 2: low volatility	Coef.	P Value
LOL		−0.003	0.094
LGAS		0.011*	0.000
C		4.603*	0.000
	Control variable	Coef.	P Value
INT		0.000**	0.0126
Observations		350

Notes: * 1% and ** % 5 significance levels respectively.

According to Table 5, the effects of oil, gas, and interest rates on the consumer confidence index are adverse and statistically significant for both low and high-volatility regimes. For the low volatility regime, a % one increase in oil and gas prices caused a %0.04 and %0.08 decrease in consumer confidence, respectively. For the high volatility regime, a %1 increase in the oil prices and gas prices caused a % 0.07 and %0.10 decrease in consumer confidence, respectively. The interest rate variable is used as a control variable, and a %1 increase in the interest rates caused a % 0.01 decrease in consumer confidence for Euro Area Countries.

The negative effect of oil and gas prices on consumer confidence is explainable. Price increases can lead to inflationary pressures on consumers. Costs of goods, services, and transportation may adversely affect consumers. High prices can also send negative signals about the outlook for the economy.¹⁹ Our results are consistent with previous literature. Clerides et al.⁵ show that oil prices negatively affect consumer confidence in the Euro Area. Güntner and Linsbauer¹ argue that oil price shocks harm consumer confidence. According to the authors, the impact of oil demand shocks is more substantial than supply shocks. Binder and Makridis⁴⁴ emphasize that gas prices negatively affect consumer sentiment.

Table 5 shows that oil prices have a positive effect on business confidence in a volatility regime. However, oil prices negatively affect business confidence in low volatility, but this effect is insignificant. The effect of gas prices on the business confidence index is negative and statistically significant during periods of high volatility. Besides, gas prices and short-term interest rates have a positive effect on business confidence for low volatility.

Studies on the effects of oil/gas prices on business confidence in Eurozone countries are quite limited. Hence, our results are remarkable. The effects of oil and gas prices on business confidence differ across regimes. In contrast to consumer confidence, business confidence studies argue that oil and gas prices have different effects on business confidence depending on the macroeconomic characteristics of countries. These characteristics include countries’ oil/gas demand, energy policies, foreign trade competitiveness, level of industrialization, geopolitical location, and hedging opportunities.^46,49 Our results support these views.

In the Eurozone, gas and oil prices have different effects on business confidence. Business confidence in Eurozone countries is negatively affected by high volatility in gas prices. In Eurozone Countries, sudden movements in gas prices are more costly for business. According to Eurostat data,⁷⁸ gas plays an essential role in the European Union's energy dependence. Natural gas, the second most used resource after nuclear energy in electricity generation, was one of the top two most used resources in industrial production between 1990 and 2023. Besides, these countries import most of their gas from Russia. Gas prices are a risk factor for European countries due to Russia's relations with the Western world and the role of energy in the geo-economy in recent years.⁷⁹ Scarcely, Oil dependency in Eurozone countries is reduced because of the use of renewable energy and nuclear energy sources. Our findings show the importance of the stability of gas prices in Eurozone countries with higher energy dependence. The price volatility of oil, which has a smaller share in the energy mix, has a positive impact on business confidence through export competitiveness and alternative investments.

Our model has some advantages over the relevant literature. Relevant Literature deals with the impact of gas and oil price movements on economic confidence only in terms of increase/decrease.^1,44 Some studies analyze the impact of energy price increases, especially uncertainty, on economic confidence.⁴⁶ The Markow Switching Model adds flexibility to the findings by revealing the impact of oil/gas prices on economic confidence in different regimes. Besides, the DCC-GARCH model provides a historical perspective on the relationship between economic confidence and energy prices.⁶³

Conclusion and policy implications

Consumer and business confidence are essential indicators for economic stability, predictive power, production, consumption, investment decisions, and policy implications. There are financial and non-financial factors that affect consumer and business confidence. One of these factors is energy prices. Energy, essential for the continuity and quality of life, drives economic development. Nevertheless, countries make energy decisions based on geopolitical developments, combating climate change, trade balance, and economic development.

This study analyzes the impact of oil and gas prices on consumer and business confidence in Eurozone countries by employing non-linear correlation and regression models. The empirical results show that oil and gas prices negatively affect consumer confidence in all regimes. Higher oil and gas prices undermine consumers’ views on economic stability. An important question arises: Is inflationary pressure the primary cause of these findings? Oil and gas are two crucial conventional energy sources on which European countries depend. Therefore, geopolitical developments, sustainability, and foreign trade may be behind the findings. Diversification of the energy mix, new energy agreements, and increased use of renewable energy sources can contribute to the demand side of economic confidence. Due to the behavioral dimension of the issue, it is essential to communicate correctly with consumers about energy policies.

According to the Markov Switching Model, the effects of oil and gas prices on business confidence have complex results. Oil prices positively affect business confidence at high volatility. Nevertheless, gas prices harm business confidence at high volatility and have a positive impact at low volatility. Declining oil dependence in the period 1990–2023 prevents the negative impact of oil prices on business confidence. Gas, which has the highest share in the final energy consumption of European countries, damages business confidence during periods of high volatility. Different findings of consumer confidence and business confidence contribute to the behavioral dimension of the issue. Consumer confidence typically reflects the sentiment and expectations of individuals regarding their own financial situation and the overall economy. Business confidence, on the other hand, represents the sentiment and outlook of businesses and industries. These two groups may have distinct views on economic conditions based on their unique experiences, priorities, and economic indicators they monitor. In addition, business-related savings decisions can be taken primarily to meet basic needs in energy crises. The findings reveal the negative impact of high-volatility gas prices on business confidence. Although the Eurozone has reduced its oil dependence, it is still gas-dependent. Reducing gas dependency, updating energy trade policies, and encouraging businesses to produce their own energy are essential to managing the impact of gas on business confidence.

This study has some limitations. We analyze energy prices from an oil and gas perspective. However, the energy dimension of environmental sustainability affects economies. Since we could not access monthly renewable energy data, eco-friendly energy cannot be included in the model because we could not access monthly renewable energy data. Besides, we focus on the Euro Area because of the multidimensional impact of energy security on Europe. Therefore, our sample is limited in reflecting energy trade and environmental sustainability. Another limitation is the methodology. For future studies, spillover analysis between oil and gas prices and economic confidence could be investigated by employing dynamic connectedness methods. Also, oil and gas exporter countries could be analyzed for future studies. In addition, the impact of renewable energy prices on consumer and business confidence can be analyzed to contribute to the literature.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Bertaç Şakir Şahin

Notes

Sabri Burak Arzova has PHD in Accounting and Finance and working as a Lecturer at Marmara University, Türkiye.

Hasan Murat Ertuğrul has PHD in Economics and working as a Lecturer at Anadolu University, Türkiye.

Bertaç Şakir Şahin has PHD in Accounting and Finance and working as a Lecturer at Yıldız Technical University, Türkiye.

References

Güntner

Linsbauer

. The effects of oil supply and demand shocks on US consumer sentiment. J Money Credit Bank 2018; 50: 1617–1644.

Narayan

Gupta

. Has oil price predicted stock returns for over a century? Energy Econ 2015; 48: 18–23.

Hamilton

. Causes and consequences of the oil shock of 2007–08. Brookings Pap Econ Act 2009; 40: 215–283.

Behrangrad

. A review of demand side management business models in the electricity market. Renew Sust Energy Rev 2015; 47: 270–283.

Clerides

Krokida

Lambertides

, et al.

What matters for consumer sentiment in the euro area? World crude oil price or retail gasoline price?

Energy Econ 2022; 105: 105743.

Edelstein

Kilian

. How sensitive are consumer expenditures to retail energy prices? J Monet Econ 2009; 56: 766–779.

Barnes

Olivei

. Consumer attitudes and their forecasting power for consumer spending. J Money Credit Bank 2017; 49: 1031–1058.

Adekoya

Oliyide

. Business confidence as a strong tracker of future growth: is it driven by economic policy uncertainty and oil price shocks in the OECD countries?. Future Bus J 2021; 7: 1–13.

Azhgaliyeva

Kapsalyamova

Mishra

. Oil price shocks and green bonds: empirical evidence. Energy Econ 2022; 112: 106108.

10.

Wang

Sharma

Jain

, et al. The relationship among oil prices volatility, inflation rate, and sustainable economic growth: evidence from top oil importer and exporter countries. Resour Policy 2022; 77: 102674.

11.

Mohamued

Ahmed

Pypłacz

, et al. Global oil price and innovation for sustainability: the impact of R&D spending, oil price and oil price volatility on GHG emissions. Energies 2021; 14: 1757.

12.

Maarof

Ahmed

Samour

. Fiscal policy, oil price, foreign direct investment, and renewable energy—A path to sustainable development in South Africa. Sustainability 2023; 15: 9500.

13.

Kilian

. Explaining fluctuations in gasoline prices: a joint model of the global crude oil market and the US retail gasoline market. Energy J 2010; 31: 87–112.

14.

Apergis

Cooray

Rehman

. Do energy prices affect US investor sentiment? J Behav Finance 2018; 19: 125–140.

15.

Ding

Liu

Zhang

, et al. The contagion effect of international crude oil price fluctuations on Chinese stock market investor sentiment. Appl Energy 2017; 187: 27–36.

16.

Hamilton

. A neoclassical model of unemployment and the business cycle. J Polit Econ 1988; 96: 593–617.

17.

Turhan

Hacihasanoglu

Soytas

. Oil prices and emerging market exchange rates. Emerg Mark Finance Trade 2013; 49(sup1): 21–36.

18.

Kilian

Vega

. Do energy prices respond to US macroeconomic news? A test of the hypothesis of predetermined energy prices. Rev Econ Stat 2011; 93: 660–671.

19.

Kilian

. The economic effects of energy price shocks. J Econ Lit 2008; 46: 871–909.

20.

Lima

Setterfield

. Pricing behavior and the cost-push channel of monetary policy. Rev Polit Econ 2010; 22: 19–40.

21.

Amusa

Mabugu

. Aggregate demand for electricity in South Africa: an analysis using the bounds testing approach to cointegration. Energy Policy 2009; 37: 4167–4175.

22.

Kavvadias

. Energy price spread as a driving force for combined generation investments: a view on Europe. Energy 2016; 115: 1632–1639.

23.

Arrow

. Samuelson collected. J Polit Econ 1967; 75: 730–737.

24.

Kydland

Prescott

. Time to build and aggregate fluctuations. Econometrica 1982; 50: 1345–1370.

25.

Clarida

Gali

Gertler

. The science of monetary policy: a new Keynesian perspective. J Econ Lit 1999; 37: 1661–1707.

26.

Sachs

Warner

. The curse of natural resources. Eur Econ Rev 2001; 45: 827–838.

27.

Marshall

. Principles of economics. London: Macmillan, 1890.

28.

Sadorsky

. Oil price shocks and stock market activity. Energy Econ 1999; 21: 449–469.

29.

Barsky

Kilian

. Do we really know that oil caused the great stagflation? A monetary alternative. NBER Macroecon Annu 2001; 16: 137–183.

30.

Lambertides

Savva

Tsouknidis

. The effects of oil price shocks on US stock order flow imbalances and stock returns. J Int Money Finance 2017; 74: 137–146.

31.

Cologni

Manera

. On the economic determinants of oil production: theoretical analysis and empirical evidence for small exporting countries. Energy Econ 2014; 44: 68–79.

32.

Akinlo

Apanisile

. The impact of volatility of oil price on the economic growth in sub-Saharan Africa. Br J Econ Manag Trade 2015; 5: 338–349.

33.

Leung

. The relationship between energy prices, economic growth and renewable energy consumption: evidence from Europe. Energy Rep 2021; 7: 1712–1719.

34.

Apergis

Payne

. Renewable energy consumption and economic growth: evidence from a panel of OECD countries. Energy Policy 2010; 38: 656–660.

35.

Benkraiem

Lahiani

Miloudi

, et al. New insights into the US stock market reactions to energy price shocks. J Int Financial Mark Inst Money 2018; 56: 169–187.

36.

Oberndorfer

. Energy prices, volatility, and the stock market: evidence from the eurozone. Energy Policy 2009; 37: 5787–5795.

37.

Aguirre

Ibikunle

. Determinants of renewable energy growth: a global sample analysis. Energy Policy 2014; 69: 374–384.

38.

Shan

Ahmad

Tan

, et al. The role of energy prices and non-linear fiscal decentralization in limiting carbon emissions: tracking environmental sustainability. Energy 2021; 234: 121243.

39.

Ike

Usman

Alola

, et al. Environmental quality effects of income, energy prices and trade: the role of renewable energy consumption in G-7 countries. Sci Total Environ 2020; 721: 137813.

40.

Anser

Ahmad

Khan

, et al. Progress in nuclear energy with carbon pricing to achieve environmental sustainability agenda: on the edge of one’s seat. Environ Sci Pollut Res 2021; 28: 34328–34343.

41.

Alsalman

Karaki

. Oil prices and personal consumption expenditures: does the source of the shock matter? Oxf Bull Econ Stat 2019; 81: 250–270.

42.

Geiger

Scharler

. How do consumers assess the macroeconomic effects of oil price fluctuations? Evidence from US survey data. J Macroecon 2019; 62: 103134.

43.

Ouyang

. Oil price shocks and China’s consumer and entrepreneur sentiment: a Bayesian structural VAR approach. Empir Econ 2023; 65: 2241–2271.

44.

Binder

Makridis

. Stuck in the seventies: gas prices and consumer sentiment. Rev Econ Stat 2022; 104: 293–305.

45.

Wang

Mirza

, et al. The impact of consumer confidence on oil prices. Energy Economics 2023; 124: 106820.

46.

Adekoya

Oliyide

. Business confidence as a strong tracker of future growth: is it driven by economic policy uncertainty and oil price shocks in the OECD countries? Future Bus J 2021; 7: 1–13.

47.

Bildirici

Badur

. Correction: the effects of oil prices on confidence and stock return in China, India and Russia. Quant Finance Econ 2019; 3: 46–52.

48.

de Mendonça

Finn

. Can credibility offset electricity price effect on business confidence? An empirical investigation from a large emerging economy. Appl Econ 2022; 54: 1229–1242.

49.

Bildirici

Badur

. Correction: the effects of oil prices on confidence and stock return in China, India and Russia. Quant Finance Econ 2019; 3: 46–52.

50.

Huang

Failler

. Dynamic correlation between crude oil price and investor sentiment in China: heterogeneous and asymmetric effect. Energies 2022; 15: 687.

51.

Degiannakis

Filis

Kizys

. The effects of oil price shocks on stock market volatility: evidence from European data. Energy J 2014; 35: 1–16.

52.

Nasim

Downing

. Energy shocks and bank performance in the advanced economies. Energy Econ 2023; 118: 106517.

53.

OECD. Economy; Leading Indicators. 2023a. https://data.oecd.org/economy.htm#profile-Leading%20indicators (12.06.2023).

54.

World Bank. World Bank. Monthly Commodity Prices. 2023. https://www.worldbank.org/en/research/commodity-markets (12.06.2023).

55.

OECD. Finance; Interest Rates. 2023b. https://data.oecd.org/finance.htm#profile-Interest%20rates (12.06.2023).

56.

Bianchi

Cordella

. Does circular economy mitigate the extraction of natural resources? Empirical evidence based on analysis of 28 European economies over the past decade. Ecol Econ 2023; 203: 107607.

57.

Kuzemko

Blondeel

Dupont

, et al. Russia’s war on Ukraine, European energy policy responses & implications for sustainable transformations. Energy Res Soc Sci 2022; 93: 102842.

58.

Hainsch

Löffler

Burandt

, et al.

Energy transition scenarios: what policies, societal attitudes, and technology developments will realize the EU Green Deal?

Energy 2022; 239: 122067.

59.

Zivot

Andrews

DWK

. Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. J Bus Econ Stat 1992; 10: 251–270.

60.

Bollerslev

. Modeling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH model. Rev Econ Stat 1990; 72: 498–505.

61.

Engle

. Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models. J Bus Econ Stat 2002; 20: 339–350.

62.

Chiang

Jeon

. Dynamic correlation analysis of financial contagion: evidence from Asian markets. J Int Money Finance 2007; 26: 1206–1228.

63.

Caporin

McAleer

. Do we really need both BEKK and DCC? A tale of two multivariate GARCH models. J Econ Surv 2012; 26: 736–751.

64.

Ertuğrul

Seven

. Dynamic spillover analysis of international and Turkish food prices. Int J Finance Econ 2023; 28: 1918–1928.

65.

Lamoureux

Lastrapes

. Forecasting stock return variance: toward an understanding of stochastic implied volatilities. Rev Financial Stud 1993; 5: 293–326.

66.

Dueker

. Markov switching in GARCH processes and mean-reverting stock-market volatility. J Bus Econ Stat 1997; 15: 26–34.

67.

Cai

. A Markov model of switching-regime ARCH. J Bus Econ Stat 1994; 12: 309–316.

68.

Ertugrul

Ozturk

. The drivers of credit default swap prices: evidence from selected emerging market countries. Emerg Mark Finance Trade 2013; 49(sup5): 228–249.

69.

Perron

. Lag length selection and the construction of unit root test with good size and power. Econometrica 2001; 69: 1519–1554.

70.

Floros

Galifianakis

. Bubbles in crude oil and commodity energy index: new evidence. Energies 2020; 13: 6648.

71.

Arango

Larsen

. Cycles in deregulated electricity markets: empirical evidence from two decades. Energy Policy 2011; 39: 2457–2466.

72.

Hancock

Vivoda

. International political economy: a field born of the OPEC crisis returns to its energy roots. Energy Res Soc Sci 2014; 1: 206–216.

73.

Bang

. Energy security and climate change concerns: triggers for energy policy change in the United States? Energy Policy 2010; 38: 1645–1653.

74.

Bielecki

. Energy security: is the wolf at the door? Q Rev Econ Finance 2002; 42: 235–250.

75.

Chong

Van Fan

Lee

, et al. Post COVID-19 ENERGY sustainability and carbon emissions neutrality. Energy 2022; 241: 122801.

76.

Nerlinger

Utz

. The impact of the Russia-Ukraine conflict on energy firms: a capital market perspective. Finance Res Lett 2022; 50: 103243.

77.

Kougias

Taylor

Kakoulaki

, et al. The role of photovoltaics for the European Green Deal and the recovery plan. Renewable Sustainable Energy Rev 2021; 144: 111017.

78.

European Commission. European Commission. EU Energy Statistics Overwiev. 2023. https://ec.europa.eu/eurostat/statistics-explained/index.php? title=Energy_statistics_-_an_overview#Imports_and_exports (10.07.2023).

79.

Halser

Paraschiv

. Pathways to overcoming natural gas dependency on Russia—the German case. Energies 2022; 15: 4939.