The growing importance of oil prices in the economy has prompted financial contagion studies to analyze the correlations between energy and stock markets (Wen et al., 2012). According to Kilian and Vigfusson (2011), financial contagion from energy prices can have a significant impact on the world economy, and it is particularly relevant in emerging markets. Usually, these countries have less diversified economies and are highly dependent on energy exports or imports, which makes them particularly vulnerable to fluctuations in energy prices and the effects of volatility transmission.

Originally, energy contagion studies focused on oil as an energy indicator and co-movement dynamics. Some remarkable works in this area were published by Ghorbel and Boujelbene (2013), Pan et al. (2015), Guesmi et al. (2018), Mahadeo et al. (2019), and Zhao et al. (2021). They analyzed the contagion from oil to stock market indices in Europe, North America, and Asia, as well as emerging countries. Later, several authors (Li et al., 2019; Algieri & Leccadito, 2017; Hergety, 2012) addressed other commodities (e.g., ethanol, coal, copper, and other metals) as energy indicators to measure contagion.

Subsequent works applied wavelet analysis as a method to measure the contagion from oil to stock markets. This line of research includes Reboredo and Rivera-Castro (2014), Boubaker and Raza (2017), Lin et al. (2019), and Hamdi et al. (2019), who examined contagion in Europe, North America, Asia, and emerging countries. It should be highlighted that no study in the published literature has addressed the relationship between energy indicators and stock markets using wavelet analysis combined with co-movement dynamics. These research gaps indicate promising areas for future studies that can provide a more detailed and constantly evolving picture of the interaction between energy and stock markets. In this context, this study aims to develop a new perspective focused on the comprehensive understanding of the propagation of disturbances between energy indicators and electricity spot prices (Díaz et al., 2019). Such perspective combines co-movement dynamics and wavelet analysis, which is a novel contribution to financial and energy studies.

As financial contagion can have a significant impact on emerging markets, it is important to understand the factors involved in this phenomenon. Therefore, this study makes a contribution to the literature in this area because it introduces a new perspective on energy contagion in Latin American countries using several energy indicators: the futures contracts prices of Brent and West Texas Intermediate (WTI) crude oil, Natural Gas (NG), and coal. First, crisis and non-crisis periods were identified using two methods taken from rule-based algorithms: one employs the criterion of duration (Pagan & Sossounov, 2003); and the other one, the magnitude of price change (Lunde & Timmermann, 2004). Second, the effects of the fundamental factors of electricity spot prices were decomposed. Third, tests were conducted to measure contagion adopting the perspective of co-movement dynamics and wavelet analysis. Finally, the results of these two approaches are discussed.

In the energy market, it is essential to consider a diversity of investment horizons, which produce flows of dynamic information in different time frames and also contribute to the variability observed in said market. A detailed understanding of the dynamic relationship between different energy assets can be achieved by decomposing market prices at different time scales. As a result, co-movement dynamics and wavelet analysis emerge as appropriate tools to decompose these data at several time scales without imposing restrictions on the structure of the Underlying Asset Return (Roy et al., 2023).

The rest of this paper is structured as follows. Section 2 presents a general overview of the financial literature in this area. Section 3 describes the data used in this study. Section 4 details the methodology. It shows the modeling of the electricity spot prices and international energy indicators; explains the econometric tools used to measure the degree of interdependency between the Colombian energy market and the international fuel markets of interest; and discusses the strategies employed to identify crisis and non-crisis market conditions. Section 5 reports the results. Finally, Section 6 draws the conclusions.

Globalization has enabled communication and interdependence between markets in different countries (Centeno et al., 2015; Briones Pinargote, 2023). This has produced linkages that have been studied to establish if they show evidence of financial contagion. Pericoli and Sbracia (2003) reviewed five definitions of contagion that have been adopted in the literature and the corresponding measurements used in empirical studies. They suggest that, during a crisis, there is a change in the transmission mechanism. Along the same lines, Dornbusch et al. (2000) and Kaminsky and Reinhart (2000) made a distinction between two types of contagion: fundamentals-based (indirect) contagion and pure (direct) contagion. Calvo et al. (1996) defined fundamentals-based contagion as the transmission of a shock between countries and/or markets as a result of real linkages and the integration of stock markets in crisis and non-crisis periods. Eichengreen et al. (1999) and Bae et al. (2003) defined pure contagion as the excess transmission of a shock between countries beyond what should be expected after the fundamentals have been controlled. Hence, the definition of contagion works well as long as a financial crisis can be identified and excess transmission can be estimated. Pericoli and Sbracia (2003) hold that a financial crisis is a source of falls in the stock market index or increases in the price volatility of financial assets. Samarakoon (2011) indicates that an increase in cross-market correlation in a crisis period (as opposed to in a non-crisis one) is interpreted as contagion. However, Forbes and Rigobon (2002) claim that these correlations are biased by the increase in market volatility during crisis periods, generating heteroskedasticity. Thus, they proposed an adjusted correlation coefficient and defined financial contagion as a significant increase in cross-market correlations after a shock.

The first contagion studies were conducted by Sharpe (1964) and Grubel and Fadner (1971), and a variety of methodologies can be used to evaluate evidence of contagion. Based on the adjusted correlation model proposed by Forbes and Rigobon, (2002), Fry-McKibbin et al. (2014) proposed a contagion test using adjusted linear correlation, assessing the significance of the adjusted correlation in crisis compared to non-crisis periods. Fry et al. (2010) introduced a coskewness-based test of contagion that evaluates the differences between market correlations in crisis and non-crisis periods as a function of changes in coskewness. There is evidence of contagion in two situations: (1)when the average behavior of a market affects another’s volatility and (2)when a market’s volatility affects another’s average behavior. Fry-McKibbin and Hsiao (2018) developed a test for co-volatility contagion, evaluating the differences between market correlations in crisis and non-crisis periods as a function of changes in co-volatility. There is evidence of contagion when the volatility of a market affects that of another.

Recent studies on financial contagion have turned their attention to analyzing the correlations between energy indicators and stock markets. Wen et al. (2012) mentioned the growing importance of oil price in the economy, which has become an energy indicator. They applied time-varying copulas to analyze the effect of the energy contagion between the WTI oil price and Chinese stock market indices. Fang and Egan (2018) studied the contagion effects between Brent crude oil and Chinese stock market sectors. Zhao et al. (2021) investigated the energy contagion effect and bubbles between (international and Chinese) oil prices and Chinese stock markets. Other authors, such as Pan et al. (2015), have extended Hong et al.’s (2007) model-free test to analyze contagion between (Brent and WTI) crude oil y and the S&P 500 index (US), FTS 100 (UK), and the DAX index (Germany). Guesmi et al. (2018) tested for the existence of energy contagion between oil price fluctuations and stock markets in the European Monetary Union (EMU), Asia-Pacific (AP), the Non-European Monetary Union (NEMU), and North America (NA).

In this research area (i.e., energy contagion), some studies have investigated emerging markets. Ghorbel and Boujelbene (2013) employed GARCH-class models to test for the existence of energy contagion between the volatility of crude oil prices and Brazil, Russia, India, and China (BRIC) stock markets. Mahadeo et al. (2019) analyzed the effects of energy contagion from Brent crude oil prices to the Trinidad and Tobago Stock Exchange using contagion tests with linear correlation, co-skewness, and co-volatility. Nevertheless, energy contagion tests are not limited to analyzing the correlations between oil prices (as an energy indicator) and stock markets. As mentioned by Li et al. (2019), coal has dominated the energy supply in China as a substitute for crude oil, and it could be related to other markets. Other studies have evaluated the presence of contagion between other energy indicators and stock markets. Algieri and Leccadito (2017) analyzed the contagion between WTI crude oil futures; ethanol as a proxy for biofuels; metal and food markets; indices; and the S&P 500 index (US). Hergety (2012) studied contagion between the prices of raw materials (i.e., oil, copper, and coffee); macroeconomic variables; external factors; and the Brazil, Mexico, Chile, and Peru stock markets.

The literature on financial contagion has not been limited to analyzing significant increases in the correlations between markets after a shock by testing for co-movement. For instance, Ghosh et al. (2020) and Shrestha et al. (2018) delved into the evolutionary dynamics inherent in financial and energy markets by means of wavelet analysis to estimate the conditional correlation between markets at different time scales. Wavelet analysis is a quantitative modeling tool that can be used to conduct multiresolution analysis of non-stationary time series and separate new relationships from financial time series taking into account the time and frequency dimensions (Ranta, 2013). On this regard, Ftiti and Hadhri (2019) provided solid evidence that using the wavelet approach produces significantly more robust results than conventional time-scale techniques.

Among the pioneering studies that first examined the effects of contagion between stock markets using wavelet analysis, Gallegati’s (2012) stands out, proving the existence of contagion between the market indices of G7 countries plus Brazil and Hong Kong. Benhmad (2013) showed the contagion dynamics between the S&P 500 index (US) and the stock market indices of G7 countries (without Italy) plus other nations, such as Switzerland, Australia, India, South Korea, Russia, and China. Both studies focused on the 2007 subprime mortgage crisis. More recently, Cărăuşu et al. (2018) investigated how and when contagion occurs between ten stock markets in central and eastern Europe and financial markets in western Europe and the US. Zhou et al. (2018) analyzed the contagion effect between stock markets in countries in Asia, Europe, and America. Dash and Maitra (2019) studied the sentiment contagion effect between developed and emerging markets.

Wavelet analysis is also employed to study energy contagion. Reboredo and Rivera-Castro (2013) analyzed the relationship between crude oil prices and the US Dollar exchange rate. Later, Reboredo and Rivera-Castro (2014) examined the relationship between crude oil and stock markets in Europe and the US. Both studies analyzed the global financial crisis. Boubaker and Raza (2017) examined the secondary effects and shocks between crude oil prices and stock markets in BRICS countries during the 2007–2009 financial crisis. Lin et al. (2019) investigated contagion between Brent crude oil, the London gold market, and stock markets in China and European countries. Hamdi et al. (2019) examined the extent of volatility between oil price and sectoral indices in the Gulf Cooperation Council (GCC) countries by using wavelet nonlinear denoised based quantile and Granger-causality analysis. Multiple studies have investigated the relationship between other energy indicators and stock markets employing wavelet analysis. Lahmiri et al. (2017) examined the relationship between 14 stock exchanges, two kinds of exchange rates, and twelve commodity markets before and after the financial crisis. Although they did not focus on energy contagion, they indirectly analyzed contagion between crude oil, natural gas, metals, food commodities, and financial markets in the US, Europe, and Asia. In a more recent study, Ghosh et al. (2020) analyzed the co-movement dynamics and the dynamic correlation between the Bombay Stock Exchange (BSE) Energy Index; Crude Oil; Natural Gas; and the DJIA and NIFTY indices. They used fractal modeling, wavelet analysis, and DCC-GARCH. However, their study did not evaluate contagion in particular either.

Other relevant studies have examined the interaction between oil prices and stock markets using wavelet analysis. Belhassine and Karamti (2021) studied the interdependence between oil and stock markets, highlighting long-term asymmetries and hedging opportunities in India and China. Zhu et al. (2022) focused on the asymmetric effects of oil prices on BRICS markets, emphasizing the short term and the influence of financial volatility. Mensi et al. (2023) addressed global co-movements, supporting the idea of recoupling during a crisis, with persistent dependency and greater portfolio risk. This diversity of approaches enriches our understanding of how these factors impact markets, which is relevant for investors and policymakers in the area of risk management and strategic financial decisions.

Based on the above, it is clear that, nowadays, there is a growing interest in researching how energy indicators impact equity markets (Boako et al., 2020) using different methodologies. However, there is a lack of studies that apply wavelet analysis to examine energy contagion, especially beyond crude oil. In addition, Latin America has been little explored in this regard, and no studies so far have investigated contagion between energy indicators and electricity prices in this region. To address this gap, this study analyzes energy contagion from Brent and WTI crude oil, natural gas, and coal to electricity prices in Colombia using co-movement tests and wavelet analysis. Two crisis periods were identified using two cutting-edge empirical strategies.

This study employed daily data from February 23, 2011, to December 31, 2018. This period was selected for two reasons. First, the big 2008 financial crisis was deliberately avoided because this paper focuses on another object of analysis and said crisis has been thoroughly addressed in previous research. Second, the period of the COVID-19 pandemic was left out because it was characterized by a series of extreme movements in a wide range of variables, which involved not only financial but also economic, social, and health-related aspects—this is beyond the scope of the financial contagion that this study aims to analyze. The data were sourced from two platforms: Refinitiv Financial Solutions and XM. Refinitiv is a leading platform thanks to its thorough coverage of financial data in real time. It is recognized by its outstanding accuracy and advanced analysis tools that put investors in a privileged position with excellent information search and powerful analytical capabilities. In turn, XM provides access to essential data on the management of the Colombian wholesale electricity market. This broad range of information is highly attractive for those who seek to conduct transactions in real time and manage, in an effective manner, their exposure to risk in stock and energy markets.

The wavelet analysis applied in this study is a widely employed technique in signal processing and data analytics to research the relationship between two variables, particularly when the data have complex structures in the time and frequency domains.

The so-called wavelet functions bear some resemblance to base functions employed in techniques such as Principal Component Analysis (PCA) or the Fourier transform. However, they are especially designed for analyzing non-stationary signals with local (rather than global) characteristics. These mathematical functions are characterized by their localization capacity in the time as well as the frequency domain, which makes them ideal tools to detect local changes in a signal.

As a complement, this methodology employed cross-correlation analysis, which can be used to identify relationship patterns between two signals at different time and frequency scales. This approach is particularly relevant in the context of the analysis proposed in this study because the phenomenon of financial contagion is inherently local, manifesting itself at specific moments of financial crises and involving different scales or frequencies. As demonstrated in the Results section, significant correlations were observed at small as well as larger scales.

Several time series were employed: indicators of the Colombian energy market, type of exchange, foreign economic activity, and energy market performance. In addition, variables were used to decompose the effects of fundamental factors of the electricity spot prices. The indicator of energy market performance was based on three variables: the closing futures prices in dollars of (Brent and WTI) crude oil, natural gas, and coal. The indicator of type of of exchange was the Real Effective Exchange Rate (REER) in Colombia. The measure of foreign economic activity was the short-term shadow rate of the US stock market (SSR_US). The indicator of energy market performance was the national electricity spot price (P_spot). The variables employed to decompose the effects of the fundamental factors of electricity spot prices were excess demand measured using the total predicted demand for day t divided by the installed capacity of hydropower plants (river contribution in kw/h). In addition, an indicator was used to show if there was El Niño phenomenon on day t. Finally, missing data (due to holidays or other reasons) were replaced with the immediately previous value.

As stated above, when authors address contagion, they refer to the emergence of transmission channels (that did not exist previously), which strengthen the relationship between a couple of markets in moments of crisis compared to a non-crisis scenario. As a result, traditional transmission channels (i.e., those that are predominant in non-crisis scenarios) should be left out. In econometrics, this is achieved by modeling the temporal evolution of each one of the variables of interest using a well-specified regression and, immediately after, extracting the residual (which is the part not explained by the model). These residuals (called pseudoreturns in this study) are expected to reflect non-traditional channels, if there are any.

The following subsection explains the econometric tools used here to measure the degree of interdependency between the Colombian energy market and the international markets of fuels of interest. It also provides evidence in favor of the contagion hypothesis if these measures of interdependency increase significantly in moments of crisis. The third subsection explains the way crisis and non-crisis periods were identified in this study. Considering all this, contagion is summarized in a graph showing a time series that reflects the degree of interdependency over time and, in addition, crisis and non-crisis periods. The existence of contagion is determined when, in crisis periods, the time series mentioned above reaches local maxima.

In contrast with more conventional methods in the literature—such as copulas (Uribe, 2011) or correlation coefficients (Mahadeo et al., 2019), which have a tendency to analyze relationships in a more general manner—the wavelet technique applied in this study could simultaneously address the problem of financial contagion in its local dimension and at different scales. This was especially adequate to understand the multifaceted nature of volatility transmission in the stock markets studied here.

The results of this study—which used wavelet analysis to investigate energy contagion—match those reported by Reboredo and Rivera-Castro (2013), who analyzed the relationship between crude oil prices and the US Dollar exchange rate. They found that, before the crisis, there was no significant relationship between oil prices and exchange rates. However, during the financial crisis, they detected a negative correlation between them, without lead or lag effects before the crisis. Subsequently, during the crisis, oil prices started to influence exchange rates and vice versa. This could mean that unexpected events in the oil market (e.g., sharp price falls) may trigger significant movements in exchange rates, which, in turn, would affect economies and companies that depend on international transactions.

Additionally, the findings of this study are in line with previous research. For instance, Hergety (2012) and Boubaker and Raza (2017) examined secondary effects and shocks in crude oil prices and the BRICS stock markets during the 2007–2009 global financial crisis. Their studies have stressed the idea that countries in Latin America have currencies that are highly correlated with basic products, and, as a result, their foreign exchange markets show greater vulnerability to external influences. From a financial perspective, this suggests that investors and financial institutions who operate in Latin America should be especially aware of changes in basic product prices and global economic events because these factors can have a significant impact on foreign exchange markets and, ultimately, on their investment and risk management strategies.

The main contribution of this study is that it demonstrated that there is a significant and long-term relationship between energy indicators and electricity spot prices, as well as contagion from natural gas and Brent oil to electricity spot prices in crisis periods. However, there is no clear evidence of contagion from coal. These results are relevant to understand how changes in the energy market, and the economy in general, can affect electricity prices in the long term in an emerging economy. Furthermore, this study consolidates a theoretical, empirical, and informative body of knowledge of the industrial sector of the economy—objectively encouraging the formulation of financial and economic policy in the Colombian energy sector, as well as in its capital market.

Energy contagion has a significant impact on the global economy. And it is particularly relevant in emerging markets that usually have less diversified economies and are highly dependent on energy exports or imports, which makes them particularly vulnerable to fluctuations in energy prices and the effects of volatility transmission. This kind of studies consolidate a body of knowledge so that firms, investors, finance policymakers, and other market agents can develop strategies and actions to mitigate risks and strengthen their response capacity in crisis situations related to the international energy market.

This study analyzed the effect of the international prices of fossil fuels on electricity prices in the Colombian market. It employed time series of energy indicators, type of exchange rate, foreign economic activity, and energy market performance. It also implemented two cutting-edge econometric methods: wavelet analysis and pseudoreturn analysis (which reflects non-traditional contagion channels). Likewise, it included contagion tests using different methods to determine crisis and non-crisis periods. It was found that the method by Pagan and Sossounov (2003), as well as that by Lunde and Timmermann (2004), can be used to establish crisis periods more effectively and produce more consistent results regarding the contagion between the global market of Brent oil and Colombian stock and foreign exchange markets.

In general, the results show that there are significant long-term correlations between energy indicators and electricity spot prices during crisis periods. There is presence of contagion from Brent oil and natural gas to electricity spot prices. Regarding Brent oil, there is conclusive evidence of contagion using the two approaches. In contrast, in the case of natural gas, evidence of contagion was only observed during the oil price plunge (2014–2015) and Brexit in 2016. In relation to coal, contagion was not clearly identified employing any of the approaches analyzed in this study. However, significant long-term correlations were observed by means of wavelet analysis. These findings are relevant to understand, in a holistic way, how changes in the global energy market, and the economy in general, can affect electricity prices in the long term in emerging economies. This is specially interesting because these markets are more susceptible in terms of macroeconomic stability and the quality of life of their populations.

Future studies can expand this database so that it includes more periods of time and, thus, other important crises such as those that occurred in 2008 or 2019. Likewise, more research should be conducted on Latin American markets, where there are several economies that mainly depend on energy commodities. In addition, the efficacy of the econometric analysis of contagion largely depends on the identification of moments of stability and disturbance. Therefore, future research should include more identification methods of this kind to confirm the robustness of the results.