Next Article in Journal
Textual Analysis of Sustainability Reports: Topics, Firm Value, and the Moderating Role of Assurance
Previous Article in Journal
The Impact of Economic Freedom on Economic Growth in Western Balkan Countries
Previous Article in Special Issue
Forecasting Systemic Risk in the European Banking Industry: A Machine Learning Approach
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Dynamic Connectedness Among Energy Markets and EUA Climate Credit: The Role of GPR and VIX

1
Department of Management, Polytechnic University of Marche, Piazzale Martelli 8, 60121 Ancona, Italy
2
Department of Industrial and Information Engineering and Economics, University of L’Aquila, Via G. Mezzanotte, 67100 L’Aquila, Italy
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2025, 18(8), 462; https://doi.org/10.3390/jrfm18080462
Submission received: 4 July 2025 / Revised: 1 August 2025 / Accepted: 12 August 2025 / Published: 20 August 2025
(This article belongs to the Special Issue Banking Practices, Climate Risk and Financial Stability)

Abstract

Energy raw materials are the basis of the economic system. From this emerges the need to examine in more detail how various uncertainty indices interact with the dynamic of spillover connectedness among energy markets. The TVP-VAR model is used to investigate connectedness among US, European, and Indian oil and gas markets and the S&P carbon allowances Eua index. Following this, the wavelet decomposition technique is used to capture the dynamic correlations between uncertainty indices (GPR and VIX) and connectedness indices. First, the results indicate that energy market spillovers are time-varying and crisis-sensitive. Second, the time–frequency dependence among uncertainty indices and connectedness indices is more marked and can change with the occurrence of unexpected events and geopolitical conflicts. The VIX index shows a positive dependence on total dynamic connectedness in the mid-long-term, while the GPR index has a long-term effect only after 2020. The analysis of the interdependence among the connectedness of each market and the uncertainty indices is more heterogeneous. Political tensions and geopolitical risks are, therefore, causal factors of energy prices. Given their strategic and economic importance, policy makers and investors should establish a risk warning mechanism and try to avoid the transmission of spillovers as much as possible.

1. Introduction

Commodity and energy markets have been the engine of economic development and have allowed many countries to increase their success. In this way, economic, commercial, and socio-cultural interdependence between various nations has been fostered. In fact, oil and gas are key determinants of energy security, energy geopolitics, and the international political economy. These dynamics have undergone changes when socioeconomic and climate events and geopolitical tensions have had their effects on the markets, especially energy markets, producing sudden variations in direction. Geopolitical risk can be defined as the probability that events of a political, social, or military nature occurring in one state could potentially jeopardize the stability of a country, region, or even the global economy. Thus, geopolitical risk is a global risk that goes beyond geographical borders and specific economic sectors and that implies the failure of global governance. For these reasons it is inevitable that this risk will have a considerable impact on international financial markets (Choi, 2024), with a corresponding effect on energy commodity price volatility. This is corroborated by the plethora of studies that have addressed this issue. The correlation between geopolitical risk and the variability of energy commodity prices was fully confirmed following the outbreak of the Russia–Ukraine conflict in 2022; this global energy crisis has disrupted the financial and energy markets (Manelli et al., 2024).
In recent years, the academic literature on geopolitical risk and its implications for commodity markets has been expanded by a substantial number of contributions examining various aspects of this topic (Micallef et al., 2023; Ozdemir et al., 2025). One area of research that has emerged is the examination of the relationship between geopolitical risk and energy commodity prices (Liang et al., 2020; Tiwari et al., 2021). Gong and Xu (2022) posit that geopolitical risk is the primary factor contributing to instability in the energy market. They assert that geopolitical risk significantly affects the dynamics of energy product supply and demand, leading to heightened volatility in energy prices. Additionally, the authors highlight that at the macroeconomic level, countries with emerging economies have exerted a pronounced influence on energy commodity demand and price volatility. In their research work, Abdel-Latif and El-Gamal (2020) relate three variables: financial liquidity, oil prices and geopolitical risk. The authors confirm the existence of a self-sustaining cycle. Specifically, a negative oil price shock leads to an increase in geopolitical risk and a decrease in global financial liquidity. Conversely, a positive shock to geopolitical risk leads to an increase in oil prices. Abid et al. (2023) conclude that there is a relationship between geopolitical risk and the prices of five commodities (energy, precious metals, agriculture, industrial metals, and livestock products) when analyzing the data from 2013 to 2023. All commodities demonstrate responsiveness to geopolitical risk shocks, with energy products exhibiting greater sensitivity than the others. Additionally, Mo et al. (2024) highlight the heightened susceptibility of the energy sector to geopolitical risk, in comparison to non-energy sectors. In a research work, Bompard et al. (2017) developed a survey methodology for evaluating a country’s energy security, encompassing both external supply and the reliability of its internal infrastructure. The application of the analytical model to the Italian context, a country with a low level of energy self-sufficiency, revealed the high sensitivity of the crude oil market to shocks caused by geopolitical risks. This finding underscores the strategic importance of energy diversification for risk reduction. Further studies have addressed the topic either by considering traditional energy markets or by focusing on a single country. In fact, in their analysis of the relationships between geopolitical risk, the traditional energy sector (coal, oil, and gas), and the carbon market, Jiang et al. (2024) suggest that geopolitical risk exerts a more significant influence on other markets in the medium to long-term, whereas in the short-term, this impact is more variable. Additionally, a distinctive feature of the carbon market emerges, whereby it appears to exert a greater influence on geopolitical risk than vice versa. A study on the combined impact of Chinese economic policy uncertainty and Chinese geopolitical risk on the global commodities market was conducted by Hu et al. (2023). The analysis revealed that from 2006 to 2023, Chinese commodity prices were influenced by shocks in economic policy and geopolitical risk. Notably, the latter also had a significantly positive impact on commodity prices during the global financial crisis. Furthermore, about the Chinese economic context, the study by Meng and Li (2024) adopts a contrasting approach, examining the impact of natural resource volatility on geopolitical risk. The authors conclude that geopolitical risk is asymmetrically influenced by natural resource volatility and propose that policymakers should prioritize the adoption of renewable energy sources, invest in the extraction of natural resources and reduce oil imports to mitigate geopolitical risk. Finally, an intriguing contribution to the discourse was suggested by Jiao et al. (2023) who concentrated on the indirect mechanisms through which geopolitical risk exerts an influence on oil prices. The transmission modes of geopolitical risk are divided into two categories: micro media (demand, supply, and speculative behavior) and macro media (global economy). The analysis yielded the following results: oil prices are influenced by geopolitical risk through supply and demand dynamics, with the effect being amplified in periods of high geopolitical tensions due to the speculative behavior assumed by investors.
A second area of research concerns the role played by geopolitical risk in the functioning mechanisms of financial markets (Bouras et al., 2019). From this perspective, it becomes evident that the existence of highly integrated and globalized financial markets gives rise to a considerable risk contagion phenomenon, which serves to exacerbate the instability of international economic and financial systems (Forbes & Warnock, 2021). Indeed, a substantial body of empirical evidence demonstrates that geopolitical risk exerts a pronounced influence on financial markets, affecting both financial liquidity and investor behavior (Su et al., 2019).
Zheng et al. (2023) examine the interrelationship between geopolitical risk and diverse segments of the financial market, encompassing both short-term and long-term perspectives. These include the stock market, bond market, foreign exchange market, and crude oil market. The findings suggest that the oil market is more closely associated with geopolitical risk than other markets. In the same field, Alqahtani and Klein (2021) examine the impact of oil prices and geopolitical risks on equities in the Gulf Cooperation Council (GCC) countries. The results of the analysis demonstrate that local geopolitical risk exerts a significant negative influence on the stock markets of the surveyed countries. Furthermore, equity markets exhibit sensitivity to fluctuations in oil prices, except for Qatar, where global geopolitical risk has a detrimental impact.
In the wake of the recent upheavals in Europe (the Russian invasion of Ukraine) and the Middle East (the war between Israel and Palestine, with subsequent spillovers into Lebanon and Syria), and the concomitant rise in global tensions, the literature on geopolitical risk has witnessed a surge in contributions seeking to ascertain the extent and direction of the impact of geopolitical risk induced by wars on energy markets (Kostaridou et al., 2024). In a similar vein, Khan et al. (2024) investigate the impact of geopolitical risk on the performance of global commodities, contextualizing their analysis within the broader temporal framework of the ongoing conflicts, namely the Russia–Ukraine war and the Israeli–Palestinian conflict. The study demonstrates a low interconnection between geopolitical risk and commodity prices in the pre-crisis periods. Additionally, it indicates a heterogeneous reaction of commodities to geopolitical shocks, which are perceived as very positive. This highlights the necessity for diversified investment strategies. Conversely, during crisis periods, there are significant opportunities for investors to diversify their portfolios, as commodities demonstrate considerable resilience to shocks, both positive and negative, that arise from geopolitical tensions. A similar analysis of geopolitical risk during conflicts was conducted by Wang et al. (2022), who focused specifically on the impact on systematic commodity risk during the conflict between Russia and Ukraine. The data confirm a notable increase in spillover indices during the conflict and an increase in the volatility of commodity markets. It is also possible to include in this line of research contributions that have analyzed the implications of geopolitical risk in situations that are not overt conflicts, but which are characterized by tension between different states. In particular, the subject of geopolitical risk and the political relations between two states, specifically China and the United States, was addressed by Mignon and Saadaoui (2024). The authors examined two indicators, the Political Relationship Index (PRI) and the Geopolitical Risk Index (GPR), with the objective of understanding the relationship between these two factors. The analysis demonstrates that an improvement in political relations between states and an increase in geopolitical risk are both associated with higher oil prices. The authors conclude that political tensions are related to consumer expectations, while geopolitical risk is related to energy market expectations.
Although, the current literature offers a various perspective on energy market analysis, there is still room for improvement. Firstly, many studies consider only the US energy market, focusing primarily on the relationship between energy, commodity, and financial markets separately. In a context dominated by uncertainty generated by numerous geopolitical events of various kinds, in which energy prices have exhibited anomalies and violent fluctuations, the objective of this paper is to provide new evidence of the interaction between various uncertainty indices and the dynamic connection between energy markets of different countries and EUA index within a unified framework. To this end, it uses a time-varying vector autoregression model (TVP-VAR) to explore the dynamic spillover effect of returns between these markets and clearly obtain the strength and direction of information transfer between the energy markets of various countries and the EUA index. Furthermore, the time–frequency dependence between uncertainty indices and the dynamic connectedness of different energy market returns is studied using wavelet coherence analysis, with the aim of examining how various uncertainty indices interact with connectedness in energy markets across different time scales. Here, we hypothesize the following:
H1: 
The oil markets are the main transmitters of information to other markets.
H2: 
The geopolitical risk index (GPR) and the VIX index influence the energy markets’ connectedness.
H3: 
Geopolitical and significant events can change the previous situation and increase the dependences between energy markets and uncertainty indices.
Our research stems from the growing importance of global geopolitical risks and their effects on economic and financial markets. It is well known how geopolitical tensions predict global oil price volatility, and the effects that they cause on energy markets (Leone et al., 2025). Considering the importance of energy markets our study attempts, for the first time, to analyze how are the various uncertainty indices interrelated with the dynamic connectedness among different oil and gas markets? How does mutual interdependence manifest itself? Therefore, this article will try to answer these questions.
The reminder of this paper is organized as follows. Section 2 presents the methodology; Section 3 outlines the data used and shows our empirical findings; and Section 4 offers this study’s conclusions and implications.

2. Materials and Methods

2.1. TVP-VAR Model

This study uses a dynamic connectedness TVP-VAR model to identify transmission mechanisms in energy markets. Dynamic connectedness measures (Diebold & Yılmaz, 2014) originate from the results of a TVP-VAR with time-varying covariances (Koop & Korobilis, 2014). Although there are numerous measures of connectedness to study markets risks, among others Balcilar et al. (2021) and Balli et al. (2023), we apply the TVP-VAR model in the spirit of Antonakakis et al. (2020) to gain a better understanding of the underlying risk within a network of variables. This methodology more flexibly and robustly adapts possible changes in the underlying data structure without having to arbitrary set the size of the rolling-window or losing observations. Furthermore, compared to other methods it is less sensitive to outliers. The TVP-VAR model is as follows:
y t = A t z t 1 ε t   ε t | Ω t 1 ~ N ( 0 , Σ t )
v e c A t = v e c A t 1 + ξ t   ξ t | Ω t 1 ~ N ( 0 , Ξ t )
with
z t 1 = y t 1 y t 2 y t p   A t = A 1 t A 2 t A p t
where Ω t 1 indicates the information available until t 1 , y t and z t 1 are m × 1 and m p × 1 vectors, A t and A i t are m × m p and m × m dimensional matrices, ε t is m × 1 vector, ξ t is m 2 p × 1 dimensional vector, Σ t and Ξ t are m × m and m 2 p × m 2 p time-varying variance–covariance matrices, respectively; v e c A t is the vectorization of A t which is m 2 p × 1 dimensional vector.
To compute the generalized connectivity procedure based on generalized forecast error variance decompositions (GFEVDs) Koop et al. (1996) and Pesaran and Shin (1998), the time-varying parameter model is transformed into a TVP-VMA leading back to Wold’s representation theorem. The TVP-VMA can be represented as follows:
x t = i = 1 p Φ i t x t 1 + ε t = j = 1 Λ j t ε t j + ε t
Using the GFEVD we construct the total connectedness index (TCI) that measures the total information spillover and expresses how a shock to one variable propagates to the other variables.
C i H = j = 1 , i j m Φ ~ i j , t ( H ) m × 100
The total directional connectedness TO others describes how a shock in a variable is able to influence all other variables. The equation of TO is:
C i j , t H = j = 1 , i j m Φ ~ i j , t ( H ) i = 1 m Φ ~ i j , t ( H ) × 100
The total directional connectedness FROM others describes how a variable is able to receives a shock in from other variables. The equation FROM is:
C i j , t H = j = 1 , i j m Φ ~ i j , t ( H ) i = 1 m Φ ~ i j , t ( H ) × 100
The net total directional connectedness (NET) indicates the net contribution of a variable to the system. The equation of NET is:
C i , t g = C i j , t C i j , t
If C i t is positive, the variable is defined as a net transmitter; if it is negative, the variable is defined as a net receiver.
The net pairwise directional connectedness (NPDC) describes the bilateral transmission process between variable i and j .
N P D C i j H = Φ ~ j i , t H Φ ~ i j , t H
If N P D C i j H is positive, the variable i is driving variable j ; if it is negative, variable i is driven by variable j .

2.2. Wavelet Coherence Analysis

After the TVP-VAR analysis that allows to identify the transmission mechanism within the energy markets, the wavelet coherence analysis (WTC) is employed to examine the interdependence between the latter and the uncertainty indices. WTC analysis is found on the Fourier transform that expresses the information related only to the frequency (Sun et al., 2020). Unlike this one WTC analysis examines the dependence between two variables in both time and frequency. In order to perform the WTC, the time series have to assume the structure of a continuous wavelet transform (CWT). CWT of time series x t is obtained by transferring the basic wavelet function to an original time series x t . The CWT function is:
W x a , b = + x ( t ) 1 a ψ t b a d t
where a captures the information of the wavelet dilation and b that about its position. The Wavelet coherence (WTC) equation as formulated by Torrence and Compo (1998) is as follows:
R 2 a , b = | S ( s 1 W x y a , b ) | 2 S s 1 W x a , b | 2 S s 1 W y a , b | 2
where S is the smoothing parameter that refer to both time and frequency, R 2 a , b can take values between 0 and 1, and the more the value tends to 1, the greater the interdependence between the two variables.
The phase difference indicates the direction of the dependence between two time series and it is represented by the following formula:
φ x y a , b = t a n H S s 1 W x y a , b R S s 1 W x y a , b
where H and R are the imaginary and real part, respectively. The phase difference allows to obtain the relationships of lead and lag of the variables. It is indicated by the black arrows on the wavelet choerence graphs. Concretely, a phase difference equal to zero indicates the same trend of the examined time-series. The arrows point to the right (or to the left) where the two variables are in phase (or anti-phase) or are positively (or negatively) correlated. If the arrows point to the top right, the first variable leads the second. Otherwise, if they point to the bottom left the second variable is ahead. Instead, when they point vertically upwards, they indicate that the first variable is ahead and, on the contrary, when they point vertically downwards, they indicate that the first variable is behind.

3. Results

This study aims to analyze the dynamic connection of returns between energy markets. It is based on a dataset consisting of returns related to oil and natural gas futures prices, to which the S&P Carbon Credit EUA index (Eua) is added and used as a benchmark of the performance of carbon emission markets. Indeed, although it is designed to measure the performance of the European Union’s carbon emission trading system, this EU ETS is the world’s first large experiment for carbon pricing (Ellerman & Buchner, 2007).The indices related to this last market have become increasingly important following the adoption of measures aimed at limiting CO2 emissions and, hence, the use of fossil energy sources. Consequently, we collect the daily futures prices quotes of the main oil and natural gas market indices such as WTI, Brent, MCX Crude oil index (MCX Oil), Henry Hub natural gas (NG), ICE Dutch TTF natural gas (TTF), and MCX natural gas index (MCX Gas). As can be seen, the analyzed indices refer to different geopolitical areas. In particular the three main energy markets are considered: the US, European, and Indian. In fact, these are among the main countries that consume energy raw materials and therefore represent a significant sample of the relevant market. To these, two indices that measure uncertainty are added, such as the geopolitical risk index (GPR) constructed by Caldara and Iacoviello (2022) and the volatility index (VIX). The GPR allows the quantification of the economic effects caused by the manifestation of geopolitical risks, while the VIX measures the sentiment of American stock market and therefore is usually used as a measure of volatility expectations. The data cover the period from 3 November 2014, to 31 October 2024. It is a fairly long period of time during which numerous events of different type and magnitude occurred. To analyze the connection between energy markets and S&P Eua index, we utilize the first differentiated series of logarithms:
y i , t = ln p i , t ln ( p i , t 1 )
Figure 1 illustrates the trend of the historical series. Notably, it is observed how the oil and gas markets follow similar trends. Regarding the former, after the peak detected during the pandemic, a strong subsequent co-movement is noted, and in particular during the war between Russia and Ukraine. Regard the gas market, plot show a certain synergy between the NG and MCX Gas. Instead TTF shows heterogeneous movements that diverge significantly until the 2022 energy crisis, when they anticipate and exacerbate the price peaks that occur during the year. A separate discussion deserves the S&P Carbon Credit EUA index which shows continuous growth starting from 2018 and exacerbated before the energy crisis of 2022.
In turn, as can be seen in Table 1 which presents the statistical description of the returns, all means and medians have values around 0. Furthermore, the standard deviation is higher for gas markets than for the others, with the TTF showing the highest value. All oil markets and EUA index show a negative skewness, they are left-skewed distributions. Instead, all gas markets are right-skewed distributions. Moreover, all series are significantly leptokurtic; the kurtosis are greater than 3. The Jarque–Bera test significantly rejects the hypothesis of normality as all variables exhibit a clear leptokurtosis and fat tails. The results of the unit-root test, ADF test, and Phillips–Perron stationarity test confirm that all returns are stationary at the 1% significance level. In conclusion, as shown in Table 2, the unconditional correlations between oil markets are highest (with values above 0.7), while the correlation between gas markets and between gas, oil, and Eua markets is moderately high (with values around 0.2).
We start our discussion by analyzing the total dynamic connectedness (TCI) and net return (NET) between energy commodity markets and the Eua index. Table 3 shows that the main shock transmitter is the WTI followed by Brent, MCX Oil, and NG, while the net shock recipients are the Eua index followed by TTF and MCX Gas. This is confirmed by the pairwise net directional transmission dominance value which for these last three markets shows negative values equal to −7.57, −6.12, and −0.81, respectively. Furthermore, the TCI explains that on average the co-movement of energy markets, and therefore the risk equality of the entire network, is 56.68%, which in turn means that on average 48.58% (= 56.68% · 67) of the variance of the forecast error of a market return can be explained by the influence of the returns of all other markets. The results suggest that the Eua index is the largest recipient of the system, i.e., it is dominated by other energy markets, as is logical given the type of linkage that it has with traditional energy markets. This indicates that price changes in the Eua index have a limited role and lower propensity to transmit shocks to the other markets in the system. Instead, oil markets emerge as the main sources of shocks, (Chen et al., 2024). Then, dynamic total connectedness captures the temporal variation of the TCI for the entire period of study.
At this point, we focus our attention on the analysis of the total dynamic connectedness (TCI), which provides the interconnectedness of the network over time. Figure 2 shows how the total dynamic return connectedness is variable over time, but always above 40%, indicating, thus, a solid interconnectedness between energy markets and Eua index. Significantly, the total dynamic return connectedness reaches values greater than 60% in 2014—at the beginning of the analysis period—in 2020, and in 2022. This adapts with periods of tension in the commodity markets, pandemic and Russia–Ukraine war. These very strong connections denote how in times of significant crisis and uncertainty the interconnectedness between energy markets is significantly consolidated. This may be because in the presence of unlooked-for and uncertain events investors are more cautions in diversifying their portfolios and less willing to take risk, this causes a greater interconnectedness between markets (Adekoya et al., 2021).
Figure 3 represents the dynamic connectedness of the net directional returns among energy markets. A positive value indicates that the information spillover is positive and, thus the related market behaves as a net transmitter. On the other hand, a negative value signals a negative information spillover which indicates that the market is a net receiver. The oil markets, markedly WTI and Brent, are the main transmitters of information to other markets. This can be explained by the fact that crude oil is the one of world’s most important commodities, and its price impacts the overall economy. Furthermore, trading on the oil market involves numerous participants with different motivations. In particular, investors use energy markets, mainly Wti and Brent, the most liquid, as alternatives to stock and bond investments to diversify their portfolios and hedge against inflation risks. While the gas markets, to a greater extent TTF, and Eua index receive information from other markets for the entire period analyzed. The MCX Oil market, the NG and MCX Gas alternate positive net spillover values with negative, indicating, in this way, how the role played by these markets is not always that of transmitter or receiver. Analyzing in more detail the Indian oil markets, MCX Oil, this one with a prevalence of positive values tends to follow the footsteps of the US and European ones, functioning mainly as a transmitter.
In summary, in terms of yield connectedness, the WTI and Brent crude oil markets act as a net transmitter of spillover, with very high percentage in late 2014 and after 2020. Instead, TTF gas market and Eua credit index function as a net receiver. Furthermore, the WTI market has the toughest dominant role in the connectedness among oil markets, indicating that US crude oil market has the utmost influence on the other markets. While TTF gas market emerges as the most significant information receiver, suggesting its limited influence on other markets. Additionally, as partly already highlighted, unexpected and significant events, such as pandemic, can amplify the performance connectedness among energy commodity markets.
Following the analysis of the dynamic connectedness of the return among energy markets, we analyze the way in which the uncertainty indices, the geopolitical risk index (GPR) and the VIX index, influence the dynamic connectedness systems. In this regard, the analysis of the wavelet coherence is used in order to verify the presence of dependence among the dynamic total connectedness and the already mentioned uncertainty indices. The dependence is measured in terms of time and frequency. Figure 4 exhibit the results of the wavelet coherence among GPR and VIX and the dynamic total connectedness. The horizontal axis indicates the time scale, and the vertical axis the different frequency bands. A strong co-movement among the dynamic total connectedness and the uncertainty indices is represented by the red regions.
First of all, it emerges how there is a lower dependence among dynamic total connectedness and GPR. In fact, the relationship between GPR-TCI shows the presence of few red regions from 2022 onwards, and these are concentrated in the medium-long-term, with a frequency of 64 and over 256 days. Of greater interest appears the relationship VIX-TCI, in which the red region with strong coherence is larger, but always concentrated mainly in the medium—16 to 256 days—and in the long-term—over 256 days.
A further consideration concerns the fact that significant events that radically change the previous situation, such as pandemic and Russia–Ukraine war, determine an increase in intensity of the dependence between TCI and uncertainty indices. In fact, the dependence among TCI and VIX is greater in the medium-term during pandemic and in the long-term during Russia–Ukraine conflict. Instead, there is a long-term dependence among TCI and GPR only after 2022. These results may be due to the fact that during the two periods uncertainty and geopolitical risk increased. In fact, during pandemic with the lockdown of economic activities, energy prices recorded a drastic decrease. Instead, during the war the tension recorded on these markets, with particular emphasis on the gas markets, produced sudden increases in its prices. Furthermore, the two events, albeit with different modalities, affected global supply chains and particularly in energy markets (Li et al., 2023), with interruptions in supply or the presence of higher costs.
In summary, the GPR-TCI relationship shows how in the medium-term the two variables are negatively correlated. A different scenario emerges from the analysis of the relationship between dynamic total connectedness and VIX, which shows a positive dependence in the medium and long-term with the VIX which anticipates the dynamic total connectedness. This indicates that the return spillovers within the energy markets tend to increase when the VIX increases in the medium-long-term.
So far, the analysis carried out indicates the way in which the uncertainty indices interact with the dynamic total return connectedness of the energy markets. At this point, to have a more in-depth understanding of the individual markets, we analyze the dependence among the uncertainty indices and the dynamic connectedness of the net total return that each market has with them. Figure 5 indicates the changing characteristics of this relationship. A first distinction concerns the dissimilar behaviors of the GPR and VIX indices. In fact, if we consider the three oil markets analyzed, it emerges that the positive relationship between these and the GPR is limited to the long-term and to a few areas of the medium-term but after 2022. In the opposite order, the relationship between the three oil markets and the VIX index is greater than the GPR. In the VIX-WTI graph, large red areas can be seen throughout the long-term with extensive spills over in the medium-term in the aftermath of the pandemic. Furthermore, if in the long-term the VIX be ahead the WTI, in the medium-term the arrows pointing to the left indicate a negative correlation. Similar conclusions, although the red areas are smaller, can be drawn for the VIX-Brent relationship. Also, in this case we note red areas in the long-term and a large negative correlation in the medium-term between pandemic and the war years. Analyzing the VIX-MCX Oil relationship we note a presence of small red areas in the short-term and a marked correlation in the medium-long-term but only during pandemic with the VIX ahead of the MCX Oil. Moving on to the analysis of the GPR and the gas markets, we note how for all three markets lone occasionally red areas appear in the short-term. Moreover, these are concentrated after 2020. If we analyze the relationship with the VIX the conclusions change. In the VIX-NG we note a relationship in the medium-term during the pandemic and a negative correlation at the beginning of the period and at the end. More marked relationships with occasional red areas in the short-term are found in the VIX-TTF graphs. In this, a large red area emerges in the medium-term between 2019 and 2021 with the TTF ahead of the VIX. In the long-term, it is noted that until 2018 the TTF is ahead of the VIX, it goes back during the pandemic, and then moves forward again during the war. Looking at the connectedness VIX-MCX Gas we note an intense connection for almost the entire long-term that also spills over into the medium-term during pandemic with the MCX Gas ahead the VIX. Furthermore, there is a large red area in the medium-term between 2023 and 2024. Finally in the analysis of GPR-Eua we note the presence of connections in the long-term with the GPR ahead. In the medium-term up to 2017 it sees the GPR ahead, and the EUA ahead after 2020. The VIX-Eua graph indicates more occasional red areas. The connectedness it is not consistent in the various time scales since the arrows point in different directions in the diagram, with the presence of positive correlations alternating with negative ones.
From the considerations carried out it can be concluded that the strong coherence among uncertainty indices and return connectedness for each markets occurs mostly in turbulent periods, such as pandemic and Russia–Ukraine war. These findings are consistent with Tran and Vo (2023), who documented that following increases in the VIX index, investors, having pessimistic expectations and overreacting, tend to increase selling. This leads to lower returns and increased market volatility. Unlike the VIX, GPR involve conflict and more complex relationships modifying various aspects of global economic interactions and producing long-term consequences such as disruption in supplies.

4. Conclusions

In this study, we examined the energy markets of different types of crude oil and natural gas. To have a better understanding of the energy market, in addition to those traditional markets we added the carbon market. This market is important not only for the growing attention paid to the environmental policies aimed at reducing carbon emissions but above all for the close relationship and integration between CO2 emissions and the use of traditional energy sources such as crude oil and natural gas. The aim of this study was to examine over and above the integration and the potential contagion risk of energy markets. The variable investigated are: WTI, Brent, MCX crude oil, Henry Hub Natural Gas, Ice Dutch TTF gas, MCX gas and S&P Carbon Allowances EUA index. Finally, we seek to determine whether net connectedness among energy markets is driven by political uncertainty and risk. It has been widely demonstrated that external uncertainty influences the fluctuations of financial and commodity markets (Bahloul et al., 2018; Gozgor et al., 2016). On the other hand, Papathanasiou and Koutsokostas (2024) observe how, following the pandemic, a strong cohesion has occurred between the VIX and the total dynamic connectedness of various financial and commodity markets. This has caused a significant attraction of capital flows on the VIX, which has been influenced by other markets, including crude oil. In this study, we provide new evidence of how the geopolitical risk (GPR) and uncertainty index (VIX) interact with dynamic connectedness between energy commodity markets. To this end, by applying the dynamic connectedness approach based on a TVP-VAR model in the spirit of Antonakakis et al. (2020) we compute the dynamic return connectedness among energy markets. After that, we employ the wavelet coherence methodology to investigate and measure the existence and dependent relationships between uncertainty indices (GPR and VIX) and the dynamic connectedness within energy markets. Compared to previous studies, our results provide new and valuable insights.
First, the time domain analysis indicates a strong interaction within energy markets with a total spillover index exceeding 50% throughout the period and reaching significantly high levels of around 60–80% on some occasions.
Second, the main crude oil markets, (WTI and Brent), are net transmitters of spillover while the TTF gas and Eua markets function as net receivers. The other markets, MCX Oil, NG and MCX Gas, alternate periods in which they are net receivers and periods in which they are net transmitters of spillover. The WTI oil market has the most powerful dominant role in connectedness among energy markets, signaling its strong effect on the other markets, while the Eua index stands out as the most significant receiver of information, suggesting its limited influence. Furthermore, the results suggest that dynamic connectedness is highly dependent on exogenous shocks and is very sensitive to global events, like pandemic and Russia–Ukraine war. Then, we analyzed the intensity of co-movement among the examined variables and different uncertainty indices (GPR and VIX) to identify actual interaction. In this context, the most influential uncertainty index on connectedness is VIX that shows a positive dependence on dynamic total connectedness in the medium and in the long-term, while GPR mainly has a negative relationship in the long-term.
Third, the connectedness among VIX and each market is mainly seen in the mid-long-term with notable spillovers into the mid-term in the aftermath of the pandemic. While, for the GPR the connectedness is more limited and concerns only the long-term. The only exception is S&P Carbon Allowances Eua index where it is more marked and concerns also the mid-term. Furthermore, over time, the uncertainty indices have changed roles and differing from one market to another. In fact, it can be noted how, within the same market, the correlation among connectedness and uncertainty index is both positive and negative at different time scales (the arrows point in different directions). Isolated events could help explain these changes over time. In particular, amplified uncertainty about the potential effects of pandemic and the Russia–Ukraine war on economic activity potentially helps explain the prevalence of long-term connectedness.
Finally, the results of our analysis support policy makers and investors. Given that the energy market connectedness shows differentiated interactions across both the different uncertainty indices (GPR and VIX) and different time scales, it is useful for policy makers to be able to distinguish the sources from which shocks originate and the different time horizons. The transmission of spillovers between different oil and gas markets and EUA index poses risk to energy stability. Furthermore, their interconnections respond differently to international events that generate political uncertainty and have the potential to disrupt markets, accentuating or limiting the push toward clean energy. This requires energy market regulators and climate policymakers to adopt specific responses to various type and origin of uncertainty, balancing fiscal, monetary and trade policies. This is so that responses strategies stabilize the market and avoid increasing risks between different markets. Furthermore, since information spillovers interact across different markets, investors should incorporate the transmission mechanism and the dependency relationship between uncertainty and energy market connectedness. This is to understand how geopolitical risk impacts prices in order to be able to forecast prices based on changes in geopolitical risk and adapt their asset allocation and hedging strategies accordingly in light of specific geopolitical events.

Author Contributions

Conceptualization, M.L.; methodology, M.L.; software, M.L.; validation, M.L.; formal analysis, M.L.; investigation, M.L. and R.P.; data curation, M.L.; writing—original draft preparation, M.L. and R.P.; writing—review and editing, M.L.; visualization, M.L.; supervision, M.L., R.P. and A.M.; funding acquisition, R.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data will be made available on request.

Acknowledgments

The authors thank the participants of the Conference on Climate Risk Management for their suggestions in improving the article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Abdel-Latif, H., & El-Gamal, M. (2020). Financial liquidity, geopolitics, and oil prices. Energy Economics, 87, 104482. [Google Scholar] [CrossRef]
  2. Abid, I., Dhaoui, A., Kaabia, O., & Tarchella, S. (2023). Geopolitical risk on energy, agriculture, livestock, precious and industrial metals: New insights from a Markov Switching model. Resources Policy, 85, 103925. [Google Scholar] [CrossRef]
  3. Adekoya, O. B., Oliyide, J. A., & Noman, A. (2021). The volatility connectedness of the EU carbon market with commodity and financial markets in time- and frequency-domain: The role of the U.S. economic policy uncertainty. Resources Policy, 74, 102252. [Google Scholar] [CrossRef]
  4. Alqahtani, A., & Klein, T. (2021). Oil price changes, uncertainty, and geopolitical risks: On the resilience of GCC countries to global tensions. Energy, 236, 121541. [Google Scholar] [CrossRef]
  5. Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13, 84. [Google Scholar] [CrossRef]
  6. Bahloul, W., Balcilar, M., Cunado, J., & Gupta, R. (2018). The role of economic and financial uncertainties in predicting commodity futures returns and volatility: Evidence from a nonparametric causality-in-quantiles test. Journal of Multinational Financial Management, 45, 52–71. [Google Scholar] [CrossRef]
  7. Balcilar, M., Gabauer, D., & Umar, Z. (2021). Crude oil futures contracts and commodity markets: New evidence from a TVP-VAR extended joint connectedness approach. Resources Policy, 73, 102219. [Google Scholar] [CrossRef]
  8. Balli, F., Balli, H. O., Dang, T. H. N., & Gabauer, D. (2023). Contemporaneous and lagged R2 decomposed connectedness approach: New evidence from the energy futures market. Finance Research Letters, 57, 104168. [Google Scholar] [CrossRef]
  9. Bompard, E., Carpignano, A., Erriquez, M., Grosso, D., Pession, M., & Profumo, F. (2017). National energy security assessment in a geopolitical perspective. Energy, 130, 144–154. [Google Scholar] [CrossRef]
  10. Bouras, C., Christou, C., Gupta, R., & Suleman, T. (2019). Geopolitical risks, returns, and volatility in emerging stock markets: Evidence from a panel GARCH model. Emerging Markets Finance and Trade, 55(8), 1841–1856. [Google Scholar] [CrossRef]
  11. Caldara, D., & Iacoviello, M. (2022). Measuring geopolitical risk. American Economic Review, 112, 1194–1225. [Google Scholar] [CrossRef]
  12. Chen, X., Yao, Y., Wang, L., & Huang, S. (2024). How EPU, VIX, and GPR interact with the dynamic connectedness among commodity and financial markets: Evidence from wavelet analysis. The North American Journal of Economics and Finance, 74, 102217. [Google Scholar] [CrossRef]
  13. Choi, S.-Y. (2024). Sectoral responses to economic policy uncertainty and geopolitical risk in the US stock market. Journal of Multinational Financial Management, 76, 100874. [Google Scholar] [CrossRef]
  14. Diebold, F. X., & Yılmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182, 119–134. [Google Scholar] [CrossRef]
  15. Ellerman, D., & Buchner, B. K. (2007). The European Union emissions trading scheme: Origins, allocation, and early results. Review of Environmental Economics and Policy, 1, 1. [Google Scholar] [CrossRef]
  16. Forbes, K. J., & Warnock, F. E. (2021). Capital flow waves—Or ripples? Extreme capital flow movements since the crisis. Journal of International Money and Finance, 116, 102394. [Google Scholar] [CrossRef]
  17. Gong, X., & Xu, J. (2022). Geopolitical risk and dynamic connectedness between commodity markets. Energy Economics, 110, 106028. [Google Scholar] [CrossRef]
  18. Gozgor, G., Lau, C. K. M., & Bilgin, M. H. (2016). Commodity markets volatility transmission: Roles of risk perceptions and uncertainty in financial markets. Journal of International Financial Markets, Institutions and Money, 44, 35–45. [Google Scholar] [CrossRef]
  19. Hu, G., Liu, S., Wu, G., Hu, P., Li, R., & Chen, L. (2023). Economic policy uncertainty, geopolitical risks, and the heterogeneity of commodity price fluctuations in China—An empirical study based on TVP-SV-VAR model. Resources Policy, 85, 104009. [Google Scholar] [CrossRef]
  20. Jiang, W., Zhang, Y., & Wang, K.-H. (2024). Analyzing the connectedness among geopolitical risk, traditional energy and carbon markets. Energy, 298, 131411. [Google Scholar] [CrossRef]
  21. Jiao, J.-W., Yin, J.-P., Xu, P.-F., Zhang, J., & Liu, Y. (2023). Transmission mechanisms of geopolitical risks to the crude oil market—A pioneering two-stage geopolitical risk analysis approach. Energy, 283, 128449. [Google Scholar] [CrossRef]
  22. Khan, N., Mejri, S., & Hammoudeh, S. (2024). How do global commodities react to increasing geopolitical risks? New insights into the Russia-Ukraine and Palestine-Israel conflicts. Energy Economics, 138, 107812. [Google Scholar] [CrossRef]
  23. Koop, G., & Korobilis, D. (2014). A new index of financial conditions. European Economic Review, 71, 101–116. [Google Scholar] [CrossRef]
  24. Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74, 119–147. [Google Scholar] [CrossRef]
  25. Kostaridou, E., Siatis, N., & Zafeiriou, E. (2024). Resource price interconnections and the impact of geopolitical shocks using Granger causality: A case study of Ukraine-Russia unrest. Journal of Risk and Financial Management, 17(6), 240. [Google Scholar] [CrossRef]
  26. Leone, M., Manelli, A., & Pace, R. (2025). Energy, metals, cereals and G7 indices: Russia-Ukraine conflict and risk spillovers. Finance Research Letters, 82, 107557. [Google Scholar] [CrossRef]
  27. Li, X., Umar, M., Zhu, C.-B., & Oprean-Stan, C. (2023). Can geopolitical risk stably predict crude oil prices? A multi-dimensional perspective. Resources Policy, 85, 103785. [Google Scholar] [CrossRef]
  28. Liang, C., Wei, Y., Li, X., Zhang, X., & Zhang, Y. (2020). Uncertainty and crude oil market volatility: New evidence. Applied Economics, 52, 2945–2959. [Google Scholar] [CrossRef]
  29. Manelli, A., Pace, R., & Leone, M. (2024). Russia–Ukraine conflict, commodities and stock market: A quantile VAR analysis. Journal of Risk and Financial Management, 17(1), 29. [Google Scholar] [CrossRef]
  30. Meng, L., & Li, J. (2024). Natural resources volatility and geopolitical risk: A novel perspective of oil and mineral rents using quantile-quantile regression for China. Resources Policy, 88, 104499. [Google Scholar] [CrossRef]
  31. Micallef, J., Grima, S., Spiteri, J., & Rupeika-Apoga, R. (2023). Assessing the causality relationship between the geopolitical risk index and the agricultural commodity markets. Risks, 11(5), 84. [Google Scholar] [CrossRef]
  32. Mignon, V., & Saadaoui, J. (2024). How do political tensions and geopolitical risks impact oil prices? Energy Economics, 129, 107219. [Google Scholar] [CrossRef]
  33. Mo, B., Nie, H., & Zhao, R. (2024). Dynamic nonlinear effects of geopolitical risks on commodities: Fresh evidence from quantile methods. Energy, 288, 129759. [Google Scholar] [CrossRef]
  34. Ozdemir, L., Vurur, N. S., Ozen, E., & Grima, S. (2025). Volatility modelling of the impact of geopolitical risk on commodity markets. Economies, 13(4), 88. [Google Scholar] [CrossRef]
  35. Papathanasiou, S., & Koutsokostas, D. (2024). A trade-off between sustainability ratings and volatility in portfolio hedging strategies. International Journal of Banking, Accounting and Finance, 14, 370–406. [Google Scholar] [CrossRef]
  36. Pesaran, H. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58, 17–29. [Google Scholar] [CrossRef]
  37. Su, C.-W., Khan, K., Tao, R., & Nicoleta-Claudia, M. (2019). Does geopolitical risk strengthen or depress oil prices and financial liquidity? Evidence from Saudi Arabia. Energy, 187, 116003. [Google Scholar] [CrossRef]
  38. Sun, X., Chen, X., Wang, J., & Li, J. (2020). Multi-scale interactions between economic policy uncertainty and oil prices in time-frequency domains. The North American Journal of Economics and Finance, 51, 100854. [Google Scholar] [CrossRef]
  39. Tiwari, A. K., Boachie, M. K., Suleman, M. T., & Gupta, R. (2021). Structure dependence between oil and agricultural commodities returns: The role of geopolitical risks. Energy, 219, 119584. [Google Scholar] [CrossRef]
  40. Torrence, C., & Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79, 61–78. [Google Scholar] [CrossRef]
  41. Tran, M. P.-B., & Vo, D. H. (2023). Correction: Asia-Pacific stock market return and volatility in the uncertain world: Evidence from the nonlinear autoregressive distributed lag approach. PLoS ONE, 18, e0285279. [Google Scholar] [CrossRef]
  42. Wang, Y., Bouri, E., Fareed, Z., & Dai, Y. (2022). Geopolitical risk and the systemic risk in the commodity markets under the war in Ukraine. Finance Research Letters, 49, 103066. [Google Scholar] [CrossRef]
  43. Zheng, J., Wen, B., Jiang, Y., Wang, X., & Shen, Y. (2023). Risk spillovers across geopolitical risk and global financial markets. Energy Economics, 127, 107051. [Google Scholar] [CrossRef]
Figure 1. Daily closing prices. Note: The above figure represents the daily closing prices of WTI, Brent, MCX oil, Henry Hub natural gas, ICE Dutch TTF natural gas, MCX natural gas, and S&P Carbon Allowances EUA Index.
Figure 1. Daily closing prices. Note: The above figure represents the daily closing prices of WTI, Brent, MCX oil, Henry Hub natural gas, ICE Dutch TTF natural gas, MCX natural gas, and S&P Carbon Allowances EUA Index.
Jrfm 18 00462 g001
Figure 2. Dynamic total return spillover. Note: Results are based on TVP-VAR model with lag length of order 3 (BIC), 200 days window size, and a 10-step-ahead forecast.
Figure 2. Dynamic total return spillover. Note: Results are based on TVP-VAR model with lag length of order 3 (BIC), 200 days window size, and a 10-step-ahead forecast.
Jrfm 18 00462 g002
Figure 3. Dynamic net directional return connectedness. Note: Results are based on TVP-VAR model with lag length of order 3 (BIC), 200 days window size, and a 10-step-ahead forecast.
Figure 3. Dynamic net directional return connectedness. Note: Results are based on TVP-VAR model with lag length of order 3 (BIC), 200 days window size, and a 10-step-ahead forecast.
Jrfm 18 00462 g003
Figure 4. Dynamic total return connectedness and uncertainty indices (GPR, VIX). Note: The horizontal axis represents the time scale, while the vertical axis indicates different frequency bands. The bold black outline denotes the 5% significance level, determined through Monte Carlo simulations. The presence of a light black contour around the cone of influence is attributed to edge effects.
Figure 4. Dynamic total return connectedness and uncertainty indices (GPR, VIX). Note: The horizontal axis represents the time scale, while the vertical axis indicates different frequency bands. The bold black outline denotes the 5% significance level, determined through Monte Carlo simulations. The presence of a light black contour around the cone of influence is attributed to edge effects.
Jrfm 18 00462 g004
Figure 5. Dynamic net return connectedness for each market and uncertainty indices (GPR, VIX). Note: The horizontal axis represents the time scale, while the vertical axis indicates different frequency bands. The bold black outline denotes the 5% significance level, determined through Monte Carlo simulations. The presence of a light black contour around the cone of influence is attributed to edge effects.
Figure 5. Dynamic net return connectedness for each market and uncertainty indices (GPR, VIX). Note: The horizontal axis represents the time scale, while the vertical axis indicates different frequency bands. The bold black outline denotes the 5% significance level, determined through Monte Carlo simulations. The presence of a light black contour around the cone of influence is attributed to edge effects.
Jrfm 18 00462 g005
Table 1. Descriptive statistics.
Table 1. Descriptive statistics.
WtiBrentMCX OilNatural GasTTFMCX GasEua
Min−0.6016−0.3118−0.3457−0.1944−0.3524−0.2091−0.1923
Median0.00150.00150.0000−0.0003−0.0005−0.00050.0008
Mean−0.0006−0.00010.0000−0.00010.0002−0.00010.0008
Max0.33310.23530.31890.22730.41270.22260.1516
Std. Dev.0.03290.02560.02920.03690.04710.03730.0298
Skewness−2.2121−0.9550−0.53750.23260.37690.2122−0.4343
Kurtosis64.230018.538624.72874.35919.60393.30224.2731
ADF test−12.567 *−12.564 *−12.801 *−12.458 *−12.963 *−12.648 *−13.858 *
JB test407,748 ***34,154 ***60,259 ***1888.5 ***9125.1 ***1088.9 ***1868.7 ***
PP test−2486.7 *−2433.7 *−2450.5 *−2589.5 *−2242.7 *−2443.7 *−2500.6 *
Note: This table presents the descriptive statistics and stationary properties of the sample. The sample consists of the daily returns of WTI, Brent, MCX oil, Henry Hub natural gas, ICE Dutch TTF natural gas, MCX natural gas and S&P Carbon Allowances EUA Index. The robustness standard errors *, *** indicate significance at 10%, 5%, and 1%, respectively.
Table 2. Correlation.
Table 2. Correlation.
WtiBrentMCX OilNatural GasTTFMCX GasEua
Wti1
Brent0.9011 ***1
MCX Oil0.7334 ***0.7842 ***1
Natural Gas0.0831 ***0.1017 ***0.0747 ***1
TTF0.1077 ***0.1370 ***0.1128 ***0.1107 ***1
MCX Gas0.0583 ***0.0932 ***0.1138 ***0.7617 ***0.1247 ***1
Eua0.1712 ***0.1817 ***0.1643 ***0.0677 ***0.1811 ***0.0624 **1
Note: This table presents the correlation matrix of the sample. The sample consists of the daily returns of WTI, Brent, MCX oil, Henry Hub natural gas, ICE Dutch TTF natural gas, MCX natural gas and S&P Carbon Allowances EUA Index. **, *** indicate significance at 10%, 5%, and 1%, respectively.
Table 3. Averaged dynamic connectedness table.
Table 3. Averaged dynamic connectedness table.
WtiBrentMCX OilNatural GasTTFMCX GasEuaFROM
Wti34.8630.6428.311.321.491.132.2565.14
Brent30.9035.1327.161.481.831.192.3164.87
MCX Oil30.1428.1035.291.301.721.312.1464.71
NG1.771.831.9052.132.4238.141.8047.87
TTF3.313.373.283.3675.772.468.4524.23
MCX Gas1.551.561.8638.402.1552.871.6247.13
Eua4.544.424.152.438.502.1073.8626.14
TO72.2169.9266.6748.2918.1146.3318.57340.09
Inc.Own107.07105.04101.96100.4293.8899.1992.43cTCI/TCI
NET7.075.041.960.42−6.12−0.81−7.5756.68/48.58
NPDC6.005.004.003.001.002.000.00
Notes: The table presents the average dynamic connectedness among variables and total connectedness index (TCI) total directional connectedness TO others, total directional connectedness FROM others, the NET total directional connectedness, and the net pairwise directional connectedness (NPDC). Results are based on a TVP-VAR (0.99, 0.99) model with lag length of order 3 (BIC), 200 days window size, and a 10-step-ahead forecast.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Leone, M.; Manelli, A.; Pace, R. Dynamic Connectedness Among Energy Markets and EUA Climate Credit: The Role of GPR and VIX. J. Risk Financial Manag. 2025, 18, 462. https://doi.org/10.3390/jrfm18080462

AMA Style

Leone M, Manelli A, Pace R. Dynamic Connectedness Among Energy Markets and EUA Climate Credit: The Role of GPR and VIX. Journal of Risk and Financial Management. 2025; 18(8):462. https://doi.org/10.3390/jrfm18080462

Chicago/Turabian Style

Leone, Maria, Alberto Manelli, and Roberta Pace. 2025. "Dynamic Connectedness Among Energy Markets and EUA Climate Credit: The Role of GPR and VIX" Journal of Risk and Financial Management 18, no. 8: 462. https://doi.org/10.3390/jrfm18080462

APA Style

Leone, M., Manelli, A., & Pace, R. (2025). Dynamic Connectedness Among Energy Markets and EUA Climate Credit: The Role of GPR and VIX. Journal of Risk and Financial Management, 18(8), 462. https://doi.org/10.3390/jrfm18080462

Article Metrics

Back to TopTop