Investigating the nature of interaction between crypto-currency and commodity markets
Introduction
Price volatility and risk contagion have been a persistent feature of global financial and energy markets. However, in recent years, these phenomena have become more frequent and intense due to several factors, such as uncertainties in the real economy, geopolitical conflicts, and economic policy changes. This has resulted in a complex and challenging investment environment, where predicting and managing returns on various assets is difficult.
Bitcoin, the largest cryptocurrency, gained significant attention in recent years after experiencing a massive increase in value from September 2017 to December 2017, followed by a significant decline in early 2018. Despite this setback, Bitcoin gradually recovered and reached new all-time highs in 2021. Ethereum, the second-largest cryptocurrency, also experienced significant growth during this period. Aside from cryptocurrencies, natural gas, crude oil, and gold have also experienced significant price volatilities and risk contagions. Natural gas and crude oil prices rose and fell sharply due to oversupply concerns and the Covid-19 pandemic's impact on demand. Gold, a safe-haven asset during times of economic uncertainty, also experienced significant growth during the same period (Baur & Lucey, 2010; Bekaert, Hoerova, and Lo Duca, 2019).
From September 9th, 2017, to October 24th, 2022, various economic and geopolitical crises impacted the returns of different assets (Baur, Dimpfl, and Jung, 2017; Bouri, Shahzad, & Roubaud, 2019). To mitigate market risks and secure profits, investors usually opt for various hedging assets (Baur & McDermott, 2010). These assets are interconnected, and their price movements can affect one another through direct or indirect mechanisms, which can be described as a transmission channel (Filis et al., 2011). For instance, gold is often seen as a safe-haven asset, and its price movements can influence other markets, such as the energy markets (Baur & McDermott, 2010). Cryptocurrencies, particularly Bitcoin, have gained popularity as an alternative investment asset class, and their prices can be influenced by changes in other markets, including gold and oil (Bouri et al., 2019; Nadarajah et al., 2018).
A plethora of economic, technological, and social factors influence the complicated and ever-evolving relationship between cryptocurrency markets and commodities markets. The relationship between the prices of gold, natural gas, crude oil, Bitcoin, and Ethereum as well as the underlying forces that drive these markets will be examined in this study. For thousands of years, people have used gold, a valuable metal, as a store of value and a means of exchange. In the past, factors such as inflation, interest rates, and geopolitical developments have had an impact on the price of gold. The growth of cryptocurrencies in recent years has opened up new opportunities for investors looking for alternative stores of wealth. Since both assets are regarded as safe havens during periods of economic instability, there has been some link between the prices of gold and Bitcoin. Natural gas, a fossil fuel, is mainly used for powering and heating buildings, and its prices are influenced by various factors, including the dynamics of supply and demand, climatic patterns, and geopolitical events (Asche, Osmundsen, and Tveteras, 2012; Brennan and Bouri, 2020; Neumann and Vitez, 2015). These factors perform a significant role in shaping the natural gas market and determining price fluctuations. Climatic patterns, particularly during winter months when heating demand is high, can affect natural gas prices as well (Leiva, Sanchez-Choliz, and Arto, 2016). Additionally, geopolitical events, such as conflicts in gas-producing regions or disruptions in supply routes, can introduce volatility and influence natural gas prices (Brennan and Bouri, 2020). Although there is no direct correlation between the price of natural gas and cryptocurrencies, blockchain technology is increasingly being used in the energy industry, with an increasing number of firms using cryptocurrencies to ease the trade of energy.
Another fossil fuel utilized for a variety of tasks, including manufacturing and transportation, is crude oil, which also appears to be affected by a plethora of factors, such as supply and demand dynamics, geopolitical developments, and weather patterns (Jensen and Hoel, 2017). The price of crude oil is influenced by the balance between global oil supply and demand. Geopolitical developments, such as conflicts or political instability in major oil-producing regions, can cause volatility and impact crude oil prices. Additionally, weather patterns, such as hurricanes or extreme cold spells, can disrupt oil production and transportation infrastructure, leading to fluctuations in prices (Jensen and Hoel, 2017). Despite the absence of a direct link between crude oil prices and cryptocurrency prices, several studies have mentioned that blockchain technology's advancement may alter the way oil is valued and traded in the future.
The two biggest cryptocurrencies by market capitalization are Bitcoin and Ethereum, with Bitcoin being the first cryptocurrency and Ethereum being a more sophisticated smart contract platform (Nakamoto, 2008). Numerous factors, such as supply and demand dynamics, technological advancements, and legislative changes, have an impact on the pricing of various cryptocurrencies (Cheah and Fry, 2015 and Katsiampa, 2017).
Although there is no direct relationship between the prices of cryptocurrencies and those of traditional commodities such as gold, natural gas, or crude oil, some investors consider cryptocurrencies as a potential digital substitute for gold, with comparable characteristics as a store of value. The interplay between cryptocurrency and commodities markets might be influenced by various factors, including economic, technological, and social dynamics. Although there may not be a direct correlation between the prices of commodities such as gold, natural gas, or crude oil and cryptocurrencies like Bitcoin and Ethereum, the adoption of blockchain technology could have implications for commodity markets.
Multivariate volatility refers to the joint volatility of two or more assets, wherein the volatility of one asset can have an impact on the volatility of other assets (Bollerslev, 1990; Engle, 2002). For example, during turbulent periods when investors become more risk-averse, safe-haven assets like gold experience high volatility but also price increases (Baur & Lucey, 2010; Baur & McDermott, 2010). Understanding the interconnections between gold, natural gas, oil prices, cryptocurrencies, and multivariate volatility is crucial for investors and policymakers to make informed decisions (Bouri, Gupta, Tiwari, and Roubaud, 2017; Dyhrberg, 2016). A substantial part of recent literature investigates the link between those assets (Bouri et al., 2019; Ciaian et al., 2016).
To capture and model the time-varying volatility and covariance of financial assets, in this study, we utilize GARCH and multivariate GARCH models. This approach allows for a more precise modeling of risks, which is crucial for informed investment decision-making. The interdependence between several assets can also be modeled using multivariate GARCH models, providing insight into how shocks to one asset may affect others. While GARCH and multivariate GARCH models are frequently utilized in academic studies on finance and economics, their use in analyzing cryptocurrency markets is relatively recent and provides valuable insights into the unique characteristics of this developing asset class.
In recent years, there has been a significant surge in the popularity of cryptocurrencies, with Bitcoin and Ethereum being the most well-known and widely traded, while commodity markets such as those for gold, natural gas, and crude oil have long been crucial to the world economy. Knowing how these two asset classes interact can provide investors with valuable insights to make informed decisions in the financial markets.
In our study we implement multivariate GARCH models such as Diag-BEKK, CCC, DCC, and GOGARCH in order to investigate the nature of the relationship between cryptocurrency and commodity markets, by analyzing dynamic linkages and volatility spillovers. Our approach takes into account the nature of the interaction between these markets and their transmission mechanisms when analyzing the conditional cross effects and volatility spillovers. Our results indicate significant returns and volatility spillovers between gold, natural gas, crude oil, Bitcoin, and Ethereum prices, with the GO-GARCH (2,2) being the best-fit model for modeling the joint dynamics of various financial assets. Moreover, we empirically show that gold acts as a safe haven during times of economic uncertainty, and it hedges against natural gas and crude oil price fluctuations. We also observe a bidirectional causality between crude oil and natural gas prices, indicating that changes in one commodity price can affect the other. The study further shows that Bitcoin and Ethereum are positively correlated with each other, but negatively correlated with gold and crude oil, suggesting that they could serve as useful diversification tools for investors seeking to reduce their exposure to traditional assets. Our research provides valuable insights for investors and policymakers on asset allocation and risk management, shedding light on the dynamics of financial markets.
We contribute to the literature in several ways. First, we emphasize the interdependencies and offer insightful information about the behavior of the cryptocurrency and commodities markets. Second, we examine the implications of spillovers between those markets and highlight various risk management techniques that investors should use in both positive and negative shock times. Third, we investigate the volatility persistence of various assets, revealing that investors should consider various risk management techniques for both positive and negative shock periods. Fourth, we provide insightful information on the correlations, knock-on effects, and volatility persistence of various assets, which can help investors manage risk and diversify their portfolios. Fifth, due to potential interdependencies among various asset classes, we highlight the importance of adopting prudent and efficient risk management and diversification techniques.
The rest of the paper is organized as follows: in Section 2, we introduce the literature review. In Section 3, we review the methodologies used in this study. In Section 4, we present and discuss the empirical results, and we conclude the article in Section 5.
Section snippets
Literature review
The study of cryptocurrency and commodity market dynamics has gained increasing attention in recent years. A plethora of studies have explored the factors driving the prices of cryptocurrencies, such as Bitcoin, as well as the linkages between cryptocurrencies and traditional financial assets, including commodities. For instance, Bouri, Gupta, Tiwari, and Roubaud (2018) found that Bitcoin can act as a hedge against extreme negative events in the stock market. Similarly, in contrast, some
Methodology
GARCH models, particularly multivariate-GARCH models such as Diag-BEKK, CCC, DCC, and GOGARCH, have become increasingly popular in studying volatility spillover effects in financial markets. These models are chosen as an empirical setting mainly due to their ability to estimate conditional variances and covariances, which are essential for explaining time-varying volatility and correlations in the returns of financial assets. Multivariate-GARCH models are advantageous in that they allow for the
Data
In order to study multivariate-GARCH volatility, we analyze the historical daily return data of Bitcoin, Ethereum, gold, natural gas, and crude oil from September 11th, 2017, to October 24th, 2022. Those data can be examined through different multivariate-GARCH models, such as Diag-BEKK, CCC, DCC, and GOGARCH, to identify the dynamic relationships and volatility spillovers among these assets during past crises.
In this study, multivariate GARCH models are employed to analyze the daily returns
Step 1: PC analysis
Table 9 shows the results of a PCA on the correlation matrix of the returns. The results of the principal components analysis on the correlation matrix of the cryptocurrencies and energy markets returns suggest that the first principal component explains 36.7% of the variation in the data, the second component explains an additional 20.7%, the third component explains 20.1%, the fourth component explains 18.2%, and the fifth component explains only 4.3%. The first principal component is the
Step 2: NLS Estimation of the rotation matrix
Table 12 shows the results of the NLS estimation of a rotation matrix. The rotation matrix is used to rotate the principal components so that they are aligned with the original assets. The estimated Q matrix is symmetric and shows the pairwise correlations between the original assets. The estimated U matrix shows the loadings of each asset on each rotated principal component. The non-singular matrix shows the rotation matrix, which is used to transform the original assets into the rotated
Conclusion
Our study makes several important contributions to the literature on financial markets. Firstly, it provides valuable insights into the behavior of cryptocurrencies, gold, and energy markets by utilizing advanced statistical methods such as GARCH models. Our findings highlight the interdependencies between these markets, which can inform investors' risk management and diversification strategies. In order to measure the level of correlation and volatility spillovers among various assets, we
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