BitCoin: A new basket for eggs?

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Economic Modelling journal homepage: www.journals.elsevier.com/economic-modelling

BitCoin: A new basket for eggs? Meng Qin a, Chi-Wei Su b, *, Ran Tao c a

Graduate Academy, Party School of the Central Committee of the Communist Party of China (National Academy of Governance), China School of Economics, Qingdao University, China c Qingdao Municipal Center for Disease Control & Preventation, China b

A R T I C L E I N F O

A B S T R A C T

JEL classification: C32 G12 E66

This paper explores the role of Bitcoin in diversifying investment risks during periods of high global economic policy uncertainty. It employs the bootstrap full- and sub-sample rolling-window Granger causality tests to investigate the mutual causal influence between global economic policy uncertainty and Bitcoin returns. It finds that global economic policy uncertainty has both positive and negative causal impacts on Bitcoin returns, suggesting that Bitcoin cannot always be viewed as a new basket for eggs since it can not always be considered to hedge policy uncertainty. The paper finds a positive reverse causal impact of Bitcoin returns on global economic policy uncertainty, indicating that the Bitcoin market contains useful information to forecast global economic policy uncertainty. Additional analyses suggest that global economic policy uncertainty also contains valuable information to improve the prediction of returns and volatility in the Bitcoin market. Under the recent global trade tensions and complex economic environment, these analyses imply that investors and countries can exploit the Bitcoin market to optimize their investment.

Keywords: Bitcoin price Global economic policy uncertainty Causal relationship Time-varying

1. Introduction The primary object of the study is to investigate whether Bitcoin is a new basket for eggs from the perspective of global economic policy uncertainty (GEPU). Satoshi Nakamoto, the Bitcoin founder (Harvey, 2014), has established a peer-to-peer electronic cash system, which is not controlled by the central bank and any financial institutions (Nakamoto, 2008). On January 3, 2009, the first batch of 50 Bitcoins marks the official birth of the Bitcoin financial system (Elwell et al., 2013). Since then, the Bitcoin market has a close relationship with the economic situation (Parino et al., 2018), also with GEPU. Bitcoin has certain similarities to gold and the dollar, which underlines its hedging ability as a medium of exchange (Dyhrberg, 2016). The rise in Bitcoin price (BP) during the periods with high GEPU evidence that it can be considered as a new basket for eggs (Bouri et al., 2017a). Also, Bitcoin is proved to be a hedge or safe haven to avoid risks of the exchange rate (Dyhrberg, 2016), inflation (Kub at, 2015), the stock market (Bouoiyour and Selmi, 2017) and energy commodities (Bouri et al., 2017b). However, research conducted by the World Gold Council highlights that gold has outperformed cryptocurrencies in the fight for the best hedging assets. Thus, the idea that Bitcoin is a new basket for eggs can not always be supported

(Yermack, 2013; Kristoufek and Scalas, 2015). Conversely, the soar of BP may trigger a Bitcoin bubble, and once it bursts, global investors and economy will face great turmoil (Li et al., 2018; Su et al., 2018). The large fluctuations in BP affect the public confidence (Bradbury, 2015), as well as national wealth which in turn will increase uncertainty. In addition, the security issues of Bitcoin (Bradbury, 2013; Mauro et al., 2018; Zaghloul et al., 2019) are also the main reason for the high GEPU. The mutual influence between GEPU and BP benefits the investors to reduce the losses from GEPU by diversifying their investment risks. Also, it inspires governments to reduce uncertainty by preventing the potential risks of Bitcoin. Unlike the legal tender, Bitcoin is not issued by a specific currency institution, but by the calculation of a network node (Elwell et al., 2013). Anyone can dig, buy, sell or charge Bitcoin, and it can be circulated around the world (Beikverdi and Song, 2015). A report released by the Infiniti Research1 announces that Bitcoin is an effective alternative currency in countries with economic instability. In 2013, the Cyprus debt crisis causes GEPU to increase (Davis, 2016). In order to reduce the losses brought by uncertainty, investors turn to the Bitcoin market, which leads BP to soar (Luther and Salter, 2017). Also, Bitcoin can be considered as a hedge or safe haven for countries with high inflation rates (Kubat, 2015),

* Corresponding author. 308, Ningxa Rd., Qingdao, Shandong, China. E-mail address: [email protected] (C.-W. Su). 1 Infiniti Research is a global market research company that provides quality, large-scale survey and its services. https://doi.org/10.1016/j.econmod.2020.02.031 Received 14 November 2019; Received in revised form 16 February 2020; Accepted 16 February 2020 Available online xxxx 0264-9993/© 2020 Elsevier B.V. All rights reserved.

Please cite this article as: Qin, M. et al., BitCoin: A new basket for eggs?, Economic Modelling, https://doi.org/10.1016/j.econmod.2020.02.031

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test.3 We choose monthly data from 2010:M7 to 2019:M6 to conduct the empirical analysis. The outcomes are supported by the predicted interaction mechanism, underlining the mutual influence between GEPU and BP. We point out that GEPU has both positive and negative causal impacts on Bitcoin returns, suggesting that Bitcoin can not always be viewed as a new basket for eggs since it can not always be used to hedge policy uncertainty. We further notice a positive reverse causal impact of Bitcoin returns on GEPU, indicating that the Bitcoin market contains useful information to forecast GEPU. We extend our analysis and demonstrate that GEPU also contains useful information to improve the prediction of returns and volatility in the Bitcoin market. Under the recent global trade tensions and complex economic environment, our findings imply that investors and countries exploit the Bitcoin market to optimize their investment. The rest of the study is arranged as follows: Section 2 shows the review of related papers. Section 3 reveals the interaction mechanism between GEPU and BP. Section 4 introduces the empirical methods. Section 5 introduces the data. Section 6 reveals the empirical analyses. Section 7 summarizes the study of this paper.

especially in Venezuela, Argentina and Iran. Among them, Venezuela even issues Petro2 to help this country weather the economic crisis. The large fluctuations in China’s stock market have not only boosted GEPU but also pushed BP to the highest level in three years. The withdrawal of Indian high-denomination banknotes also increases the trading volume of Bitcoin (Aggarwal et al., 2018), which drives BP to rise. In addition, the Turkish lira exchange rate has fallen sharply, and Bitcoin becomes more popular in this country, which highlights the advantages of cryptocurrency in value storage. Therefore, the fluctuations in BP are affected by the economic policy uncertainty around the world, which in turn may lead to greater disorder in the global economy (Vigna, 2015). Since the security issues (Bradbury, 2013; Mauro et al., 2018; Zaghloul et al., 2019) and bubble risks (Li et al., 2018; Su et al., 2018) of Bitcoin, countries formulate policies to control their transactions, which will increase GEPU. China advocates that the financial and payment service institutions can not participate in Bitcoin transactions, which is considered as a threat to the Bitcoin market (Kaiser et al., 2018). The Jordanian Central Bank prohibits banks, currency exchanges, financial and payment service firms from involving Bitcoin and other cryptocurrencies. Bitcoin transactions are considered illegal in several countries, such as Indonesia and Pakistan (Amrina and Kusuma, 2018). Furthermore, Russia’s attitude towards Bitcoin experiences the biggest change among the world, from illegal which is declared by the Russian Attorney General in 2014 to a digital asset which announced by the central bank governor in 2017 (Kakushadze and Liew, 2018). Therefore, BP has a relationship with economic policy uncertainty at a global level. This paper can be classified into the literature on Bitcoin investment and global policy uncertainty and relates to studies on optimal investment decisions under uncertainty. The paper contributes to the literature in several ways. To begin with, the existing studies mainly investigate the relationship between BP and macroeconomic factors (such as inflation, exchange rate, and stock market) of one or several countries (Cheng and Yen, 2019; Hu et al., 2019; Wang et al., 2019). The Bitcoin market can be affected by global economic uncertainty (Beikverdi and Song, 2015; Bouri et al., 2017a). Similarly, global economic uncertainty may be influenced by the Bitcoin market. In other words, there may be a mutual influence between GEPU and BP. However, prior studies do not investigate this mutual influence. The study can be considered a pioneering effort to investigate whether Bitcoin is a new basket for eggs and to explore the role of the Bitcoin market in the GEPU variation. Secondly, we contribute to the literature by examining the timevarying relation between volatility in the Bitcoin market (which reflects the risks of investing in the Bitcoin market) and GEPU. That aside, we examine whether the information content of GEPU can improve the predictions of returns and volatility in the Bitcoin market. This is important because it provides the mean investor additional information to enhance his/her portfolio optimization decisions. This approach distinguishes our work from previous studies since they do not examine the Bitcoin market from this perspective (i.e. they do not test the validity of the causal relation between Bitcoin returns and GEPU, as well as the predictive power of GEPU over the Bitcoin market). Thirdly, the causality between GEPU and BP could be non-constant. That is, the relationship may be weaker or stronger, and also could be positive and negative over time, which are ignored by the existing studies (Fang et al., 2019; Wu et al., 2019). For example, during certain periods, the causality between GEPU and BP may be insignificant, which means the statement that Bitcoin is a new basket for eggs does not always hold true. Thus, we fill this research gap by ensuring the reliability and accuracy of our results. Our analysis focuses on the non-constant interaction between GEPU and BP, by applying the bootstrap sub-sample rolling-window causality

2. Literature review The question of whether Bitcoin is a new basket for eggs has attracted attention over the past decade. We can answer it from the perspective of global economic policy uncertainty by examining the causality between GEPU and BP. Dyhrberg (2016) suggests that Bitcoin plays an important role in the financial market and risk management, and it is useful for risk-averse investors when they face in anticipation of negative shocks to the market. Bouri et al. (2017a) evidence that Bitcoin can be viewed as a hedge to avoid global economic uncertainty. Bouoiyour and Selmi (2017) indicate that the surge in BP after the U.S. presidential elections in 2016 highlights Bitcoin is a modern safe haven, and this property is time-varying. Bouri et al. (2017b) prove that Bitcoin can be considered as a strong hedge and a safe-haven to avoid the risks of the energy commodity indices movements, which is one of the main sources for GEPU. From the evidence of India, Aggarwal et al. (2018) find that the portfolios with Bitcoin have better risk-adjusted returns than without it (Chuen et al., 2017), which underlines Bitcoin can be viewed as an investment alternative with huge potential. By applying a least absolute shrinkage and selection operator (LASSO) model, Panagiotidis et al. (2018) reveal that among the determinants of BP, policy uncertainty is one of the most important factors. Parino et al. (2018) identify the socio-economic factors, such as per capita Gross Domestic Product (GDP) and trade freedom, which affect not only GEPU but also the adoption degree of Bitcoin blockchain by users. Fang et al. (2019) indicate that Bitcoin can also be acted as a hedge under specific economic policy uncertainty conditions (Demir et al., 2018; Wu et al., 2019; Su et al., 2019a,b). Wang et al. (2019) suggest that Bitcoin can be acted as a safe-haven or a diversifier under the U.S. economic policy uncertainty shocks. However, the idea that Bitcoin is a new basket for eggs is not always supported. Yermack (2013) argues that Bitcoin plays no role in risk management and hedging ability, since its high short-term risks and the great likelihood of daily hacking and theft. Although Bitcoin has the advantage to stop inflations caused by governments, Kubat (2015) highlights that the storage of Bitcoin is riskier than other assets (i.e. currencies, gold and stocks), which also means the store of value money function is poor. Kristoufek and Scalas (2015) choose financial stress index (FSI) which measures the uncertainty in financial market and gold price which denominated in Swiss francs, and find that there are no signs that Bitcoin can be used as a safe haven. Baek and Elbeck (2015) reveal that the Bitcoin market is highly speculative at present, rather than an investment vehicle. Bouri et al. (2017c) evidence that the hedging ability of Bitcoin is poor, it can only be viewed as a strong safe haven to avoid risks of weekly extreme fall in the Asian stock market. Cheng and Yen (2019) indicate that economic policy uncertainty of U.S. or other Asian countries (except for China) can not predict BP, while China economic

2 Petro is a digital cryptocurrency issued by Venezuela. Each coin has a barrel of crude oil as a physical mortgage. 3 See, also, Balcilar et al. (2010) and Su et al. (2019a,b) for this application.

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dBPjt ¼ ðg þ ut Þdt þ χ dZt þ χ 1 dZ jt

policy uncertainty has no predictive power for the other main cryptocurrencies. Hu et al. (2019) point out that the U.S. economic policy uncertainty has no effect on predicting BP. In turn, the security issues (Bradbury, 2013; Mauro et al., 2018; Zaghloul et al., 2019), which may decrease the public confidence, and bubble risks (Cheah and Fry, 2015; Su et al., 2017, 2018; Li et al., 2018), which may raise profound economic and societal issues, lead countries to implement relevant policies. The changes in the Bitcoin market may affect the level of GEPU. Elwell et al. (2013) suggest that the illegal use of Bitcoin has influences on the development objectives, such as stable prices and financial stability which also mean the low GEPU. Vigna (2015) indicates that Bitcoin and digital money are challenges for the global economic situation, which also have effects on GEPU. However, the view that GEPU can be affected by the Bitcoin market does not reach an agreement. Baur et al., 2018 point out that relative to the other assets, the size of Bitcoin is “small”, which also means it can not have immediate risks for the monetary, financial and economic stability. The previous studies mainly explore a one-way causality from economic situation to the Bitcoin market or vice versa. However, there are few studies that investigate the mutual influences between these two variables. Bouri et al. (2018b) employ a copula-based approach to dependence and causality in the quantiles, and evidence that the global financial stress index (GFSI), which related to GEPU, is strongly Granger cause BP. Also, they highlight that Bitcoin can be viewed as a safe-haven to avoid policy uncertainty. In summary, the interaction mechanism between GEPU and BP has not been elaborated clearly, and there is no solution for the issue of whether Bitcoin can be considered as a new basket for eggs from the perspective of GEPU. Furthermore, the existing studies ignore the structural changes in the time series and the non-stable parameters in Granger causality test methods, which can not analyze the time-varying causal relationship and the direction between GEPU and BP. Therefore, in order to ensure the robustness, we apply the sub-sample test in this paper. Through this method, we can investigate the effect of GEPU on BP, in order to answer the question of whether or not Bitcoin is a new basket for eggs. Also, we can evidence the influence from BP to GEPU, in order to analyze the condition of the global economy by considering the Bitcoin market.

t 2 ½0; T

(1)

where g is a constant, χ and χ 1 are the observable coefficients.Zt is a Brownian motion, and Z jt is an independent Brownian motion of investor j. ut indicates that government economic policy affects the average of the profitability process of each investor j. uold refers to the effects of current economic policy. Suppose the policy can be adjusted at the time τ (0 < τ < T), the government should decide whether to change it. If the government changes the current economic policy, the effects replace uold with unew . The values ut can be expressed as follow: 8 old < u for t  τ ut ¼ uold for t  τ if government does not change the policy : new u for t  τ if government changes the policy

(2)

When t ¼ 0, both uold and unew have normal prior distributions with mean zero and known variance σ 2u , which can be written as ueNð0; σ 2u Þ. Then, GEPU can be expressed by σ u , that is the standard deviation of ut . We can only observe that uold and unew have a huge difference, but the size between these two values can not be determined. Hence, the general equilibrium model states that GEPU has certain impacts on BP, but the direction can not be identified. In terms of supply, the influence of GEPU to BP also has two channels. On the one hand, high GEPU may hinder technological development in several countries (Baker et al., 2012), which impedes the innovations of Bitcoin’s mining technologies. Then, the supply of Bitcoin will reduce, which causes BP to increase. On the other hand, GEPU may indicate the changes in national policies on the digital currency market. If the country authorizes the Bitcoin transactions, the public enthusiasm for mining it will rise, which leads to an increase in its supply and a decrease in BP. If BP can be positively affected by GEPU, it indicates that Bitcoin can be considered as a new basket for eggs, since its price will increase during high policy uncertainty periods and vice versa.

3.2. The mechanism of BP influences on GEPU As the value of Bitcoin rises, which means high BP, and Bitcoin market does not have strict regulatory authorities, more security issues (i.e. theft) take place around the world (Bradbury, 2013; Mauro et al., 2018; Zaghloul et al., 2019). For instance, Mt. Gox was stolen by about 800,000 Bitcoins in February 2014, and investors panic when such unsafe problems arise. Relevant departments must take measures or policies to solve these problems, which will cause GEPU to rise. In addition, Bitcoin speculation will trigger bubbles (Cheah and Fry, 2015; Li et al., 2018; Su et al., 2018), and the most significant one is in 2017, the crazy investment of Bitcoin by the world (especially by China, Japan and South Korea) make BP skyrocket. These bubbles will not only increase investors’ risks but also hinder the healthy development of the national capital market. In order to prevent the bubble from bursting or make up for its losses, related departments have to implement policies to control investors’ transactions and maintain financial stability. In short, a sharp increase in BP will drive GEPU to increase. To sum up the above analyses, Hypotheses I and II can be put forward as follows:

3. The interaction mechanism between GEPU and BP 3.1. The mechanism of GEPU influences on BP In terms of demand, we can explain this mechanism from three perspectives. Firstly, Bitcoin can be viewed as an asset to avoid risks of some economic cases (Bouri et al., 2017a; Demir et al., 2018; Fang et al., 2019; Wu et al., 2019). High GEPU rises the Bitcoin demand for hedging uncertainty, causing BP to increase. For instance, the Cyprus debt crisis in 2013 drives GEPU to increase, and also makes the investors be interested in digital currencies, which causes the demand for Bitcoin and BP to soar. Secondly, BP may be related to the values of other assets. GEPU may cause the values of other assets (i.e. gold and U.S. dollar) to change, which affects the demand for Bitcoin and also BP (Zhu et al., 2017; Bouri et al., 2018a; Obryan, 2019). The Federal Reserve cuts the interest rates, not only causing GEPU to increase, but also depreciating the U.S. dollar. The lower value of the U.S. dollar decreases its demand and increases the demand for other assets, such as Bitcoin. Higher demand will lead BP to increase, and vice versa. Thirdly, the transmission mechanism can also be obtained from the general equilibrium model (Pastor and Veronesi, 2012). Suppose that there is an economy with a continuum of Bitcoin investor j (j 2 ½0; 1) and a finite horizon ½0; T. All investors continue to linearly invest their whole capital, which return of Bitcoin investment

Hypothesis I. GEPU has both positive and negative influences on BP, meaning that BP can be certainly affected by GEPU. Hypothesis II. The soar in BP results in high GEPU, that is BP has positive influences on GEPU. Although the existing studies have explored the impacts of policy uncertainty on the Bitcoin market or the influences from BP to GEPU, the interaction mechanism between them is hardly summarized. The interaction mechanism between GEPU and BP is revealed in Fig. 1. Then, we can investigate the mutual influence between GEPU and BP from a theoretical perspective. In addition, this paper applies the bootstrap sub-

(BPjt ) is arbitrary. The equation of capital accumulation of investor j is dBjt ¼ Bjt dBPjt , where Bjt indicates the capital stock of investor j when the time is t. Then, we can construct the equilibrium model as Equation (1):

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4.2. Parameter stability test

sample rolling-window causality test, in order to explore the timevarying Granger causality between these two variables and evidence Hypotheses I and II.

The suppose of the full-sample test is that there are no structural changes in the parameters in VAR models, which is usually unrealistic. Hence, if the parameters are time-varying, it is not reliable to employ the full-sample test. Then, we perform the Sup-F, Ave-F and Exp-F tests, which are developed by Andrews (1993) and Andrews and Ploberger (1994), to conduct the parameter stability tests. The sudden structural change can be examined by Sup-F, the parameters have a gradual change over time can be tested by Ave-F and Exp-F. In addition, we apply the Lc statistics test, which is developed by Nyblom (1989) and Hansen (1992), to examine whether the parameters follow a random walk process or not. Through the above stability tests, if the parameters are non-stable, there must be a time-varying interaction between GEPU and BP. Therefore, we should employ the sub-sample test to explore the Granger causal relationship between GEPU and BP.

4. Methodology 4.1. Bootstrap full-sample causality test The Granger causality test statistics based on the traditional vector autoregression (VAR) model, which can not be submitted to the standard asymptotic distributions. Shukur and Mantalos (1997) develop the critical values of the residual-based bootstrap (RB) method to avoid biased results and enhance the accuracy of the Granger causal relationship test. Moreover, they suggest that this method is suitable for the tests of standard asymptotic distributions, and even in small samples. The likelihood ratio (LR) tests (Shukur and Mantalos, 2000), can be corrected by the characteristics of power and size. In order to test the Granger causal relationship between GEPU and BP, we apply the RB-based modified-LR statistic in this paper. The bivariate VAR (p) process is constructed as Equation (3): Yt ¼ α0 þ α1 Yt1 þ …… þ αp Ytp þ υt

t ¼ 1; 2; ::::::; T

4.3. Bootstrap sub-sample rolling-window causality test This method, developed by Balcilar et al. (2010), is to separate the entire time series into small samples based on the rolling-window width. The separated small samples are scrolled from the start to the end of the entire time series. The detail method is as follows: Let the extent of the whole sample is T and the rolling-window width is l. The terminal of every separated small sample is l, lþ1, …..., T and we can obtain T-lþ1 sub-samples. Each sub-sample can achieve a Granger causality result by employing the RB-based modified-LR test. Then we can come to the results of the bootstrap sub-sample rolling-window test, through considering whole p-values and LR statistics of sub-samples in the chronological Pp 1 Pp b* b* order. N 1 b k¼1 α 12;k and N b k¼1 α 21;k are the mean values of a large number of estimations, which indicate the effect of BP on GEPU and the influence from GEPU to BP, respectively. And Nb is the frequency of

(3)

where p is selected based on the Schwarz Information Criterion (SIC), which means an optimal lag order. The VAR (p) process with two variables can split Yinto GEPU and BP, that is Yt ¼ ðGEPUt ; BPt Þ’ . Then, Equation (3) can be rewritten as follow: 

    α α ðLÞ GEPUt ¼ 10 þ 11 BPt α20 α21 ðLÞ

α12 ðLÞ α22 ðLÞ



   υ GEPUt þ 1t BPt υ2t

where υt ¼ ðυ1t ; υ2t Þ’ is a white-noise process. αij ðLÞ ¼

(4)

Pp

k k¼1 αij;k L ,

i, j¼1,

2 and L is a lag operator, and we have L Yt ¼ Ytk . The null hypothesis that BP has no effects on GEPU, that is α12;k ¼ 0 for k ¼ 1, 2, …..., p, can be examined according to Equation (4). This null hypothesis can be rejected if BP is a Granger cause of GEPU, and vice versa. Similarly, the null hypothesis that GEPU does not Granger cause BP (α21;k ¼ 0 for k ¼ 1, 2, …..., p) can also be rejected. k

b *21;k are parameters from Equation (4). α *12;k and α bootstrap repetitions. b We use 90% confidence interval in this paper, also with the corresponding lower and upper bounds, which refers to the 5th and 95th quantiles of b α *12;k and b α *12;k , respectively (Balcilar et al., 2010). The selection of the rolling-window width is complicated, a small one can not

Fig. 1. The interaction mechanism between GEPU and BP.

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In general, the mutual influences between GEPU and BP are complex and time-varying. Table 1 reports descriptive statistics. The averages of GEPU and BP show that they are centred at the 154.502 and 1763.816, respectively. The skewness is positive in GEPU and BP. The kurtosis is greater than 3, which means these two variables satisfy the leptokurtic distributions. In addition, the Jarque-Bera index indicates that GEPU and BP are obviously non-normally distributed at 1% level. Hence, the traditional Granger causality test is not reasonable to apply. Then, we employ the RB method to solve the problem of the potentially non-normal distributions in GEPU and BP. This paper also uses the bootstrap sub-sample rollingwindow test to investigate the time-varying Granger causal relationship between these two variables. GEPU and BP are transformed by taking natural logarithms to avoid potential heteroscedasticity. Then, we use the first differences of BP, which refers to the returns of Bitcoin, to ensure the stationary of time series.

make the test results robustness. And a large one can improve the accuracy of the test results, but it may reduce the times of scrolls. Pesaran and Timmermann (2005) reveal that the width could not be less than 20 if the stability does not hold. 5. Data In this paper, we consider monthly data from 2010:M7 to 2019:M6 to investigate the Granger causality between global economic policy uncertainty and Bitcoin price, and further explore whether Bitcoin can be considered as a new basket for eggs. Bitcoin is first reported by the technology media Slashdot on July 11, 2010, which brings a large number of users to it. On July 17, 2010, the price of Bitcoin has soared 10 times to $0.08. And in the same month, MT. Gox, the first Bitcoin trading platform, is founded, which provides convenience for the public to conduct Bitcoin transactions. We choose Bitcoin price (BP) which is denominated in U.S. dollars,4 to represent the international digital currency market. Since then, BP rises sharply and reaches its highest point in December 2017. However, the fluctuation of BP is quite dramatic, with a drop of 80% in just one year. In 2019, the U.S. President Donald J. Trump has launched trade wars at a global level, which causes a rise in global economic policy uncertainty (GEPU). During this period, BP has rebounded and broken through $10,000 in June 2019. We can observe that the fluctuations in BP may be related to the changes in GEPU,5 which is developed by Davis (2016). GEPU is a GDP-weighted average of economic policy uncertainty (EPU) for 20 countries.6 The higher GEPU means the uncertainty of global economic policy is greater, and vice versa. Therefore, there may be mutual influences between the global economic situation and the digital currency market. Fig. 2 shows the trends of GEPU and BP. From Fig. 2, it can be noticed that BP does not increase in all periods with high GEPU. At the beginning of the issuance of Bitcoin, the investors are few and there is no formal exchange, which causes its low price. Urquhart, 2018, investors are generally aware of the epoch-making significance of the decentralized nature and limited market size of Bitcoin. The Cyprus debt crisis not only drives GEPU to increase in 2013 but also makes the investors be interested in digital currencies. The rise in demand for Bitcoin to invest and hedge risks causes BP to soar during this time. In the same year, most countries in Europe compete to introduce Bitcoin issuance policies, which is highly valued in these countries, then BP starts to increase rapidly. Since 2014, BP has declined, and the global economic environment has been relatively stable. Although the European immigration crisis in 2015 causes GEPU to rise, BP is still in a downturn. However, uncertain events, such as the Brexit, Brazil economic crisis and U.S. election, cause GEPU to rise sharply in 2016, which also lead BP to increase to avoid loses and maintain investment value. During the history of Bitcoin development, the most significant year is in 2017, which price has increased by nearly 2000% in the whole year. The reason behinds this price skyrocketing is the crazy investment of Bitcoin by the world (especially by China, Japan and South Korea), but GEPU is at a low level during this period. In 2018, the strong dollar as the Federal Reserve (Fed) interest rate hikes, which leads the investment value of the Bitcoin market to reduce. Then, the sluggish demand of investors causes BP to fall nearly 80%, but GEPU is increasing due to the Turkey debt crisis and Trump’s uncertain policies. The intense trade wars launched by the U.S. cause GEPU to rise in 2019, which also make the public hold assets with hedging ability, such as Bitcoin, then the rise in demand drives BP to soar.

6. Empirical results The bivariate VAR models, based on Equation (4), are applied to test the full-sample Granger causality between GEPU and BP. According to SIC, the optimal lag order we selected is 3. The full-sample test results are highlights in Table 2. The p-values reveal that the causality between these two variables is not significant, indicating that GEPU has no influence on BP and vice versa. These are not supported by the previous studies (Vigna, 2015; Parino et al., 2018) and also with the conclusion of the Hypotheses I and II, which highlights that BP is affected by GEPU. We employ a full sample estimate in the bivariate VAR models, which assumptions are that the parameters are stable, and only one causal relationship exists in the overall sample period. However, if the time series and bivariate VAR models have structural changes, the Granger causality between GEPU and BP could be non-constant (Balcilar and Ozdemir, 2013). Sup-F, Ave-F and Exp-F tests (Andrews, 1993; Andrews and Ploberger, 1994) are applied to examine the stability of parameters in the VAR models with GEPU and BP. Moreover, we also employ the Lc statistics test, developed by Nyblom (1989) and Hansen (1992), in order to ensure the reliability of the Granger causality test. Table 3 shows the results of parameter stability tests. From Table 3, the Sup-F test suggests that both GEPU and BP have sudden structural changes at 1% level and the VAR models at 5% level. And the Ave-F and Exp-F tests reveal that parameters can gradually change over time in GEPU at 10% and 1% levels, respectively. However, BP and the VAR system can not accept the alternative hypothesis of evolution along the time trajectory through Ave-F test, which is significant in the Exp-F test at 1% and 5% levels, respectively. Moreover, the null hypothesis of Lc statistics test is that the parameters in the VAR system follow a random walk process, which can be rejected at 10% level. Then, we can conclude that the parameters in the overall VAR process are time-varying. In conclusion, through the parameter stability tests, it can be observed that the Granger causality between GEPU and BP is nonconstant, so the bootstrap full-sample method is not applicable. Then, we employ the bootstrap sub-sample rolling-window causality test, in order to explore the time-varying causal relationship between these two variables. The rolling-window width we selected is 24-months,7 which can ensure the robustness of our Granger causality test. We can examine whether the null hypothesis GEPU does not Granger cause BP (or BP does not Granger cause GEPU) is true or not. In addition, the direction of the effects of GEPU on BP (or the influences from BP to GEPU) can also be obtained. Figs. 3 and 4 reveal the bootstrap p-value and the orientation of the effects from GEPU to BP, respectively. GEPU Granger cause BP during the

4 The price of Bitcoin in U.S. dollars is taken from the Yahoo Finance (https ://finance.yahoo.com/quote/BTC-USD?p¼BTC-USD&.tsrc¼fin-srch). 5 The GEPU index is taken from the Economic Policy Uncertainty (http://www.policyuncertainty.com/global_mon thly.html). 6 The 20 countries are Australia, Brazil, Canada, Chile, China, France, Germany, Greece, India, Ireland, Italy, Japan, Mexico, the Netherlands, Russia, South Korea, Spain, Sweden, the U.K. and the U.S.

7 To evidence the robustness of the empirical analysis, the study also uses the widths of 20-, 28- and 32- months to investigate the causal relationship, and the outcomes support the 24-months rolling-window.

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Fig. 2. The trends of GEPU and BP.

2013:M11-2013:M12, GEPU has slightly increased mainly due to the U.S. issues a statement on December 19, 2013. This statement announces that the monthly purchase of Treasury bonds will be reduced from $45 billion to $40 billion since January 2014, which is a sign that the U.S. is exiting from quantitative easing policy gradually (Ogawa and Wang, 2016). Combined with other events, such as the first phase agreement of the Iranian nuclear issue (Katzman and Kerr, 2015), the Doha round negotiations8 (Bonciu and Moldoveanu, 2014) and the Thai ruling crisis (On December 9, 2013, Thai Prime Minister Yingluck is under pressure from the opposition to dissolve the parliament and advance election), all of these result in a slight rebound in GEPU during the period of low uncertainty. The higher policy uncertainty makes investors pessimistic to the global economic environment, and they are willing to invest in relatively stable assets (Bird et al., 2011; Abberger et al., 2014; Donadelli, 2015; Debata and Mahakud, 2018), so the digital currency market has received attention. Then, the demand for Bitcoin to reduce the losses from GEPU has increased, which also drives BP to rise. Therefore, we can evidence that GEPU has a positive influence on BP during this period. Since the Argentine exchange rate and Turkish debt crises, GEPU has increased in 2018. In the same year, the U.S. government shutdown occurs, which has the longest-running (35 days) from December 22, 2018, to January 25, 2019. The government shutdown not only causes the rise in U.S. partisan conflicts but also GEPU. However, when the crises subside and the government shutdown ends, as well as China and the U.S. agree not to impose new tariffs, all of these cause GEPU to decrease during the periods of 2019:M1-2019:M2. There are two ways to explain the fall in BP. On the one hand, the decline in GEPU improves the public confidence in the current world economic situation (Bird et al., 2011; Abberger et al., 2014; Donadelli, 2015; Debata and Mahakud, 2018), which reduces the demand for hedging or safe-haven assets (i.e. Bitcoin), and leads to a decrease in BP. On the other hand, the U.S. dollar has an appreciation trend at the beginning of 2019, which means that the investment in the dollar is more profitable than Bitcoin. Then, the demand for Bitcoin has reduced, which makes BP decrease (Bouri et al., 2016). In addition, since the international price of Bitcoin is denominated in U.S. dollars (Bouri et al., 2016), and BP usually has an inverse relationship with the value of the dollar. Thus, the reduced demand for Bitcoin and the strength of the U.S. dollar make BP fall during this period. Then, we

Table 1 Descriptive statistics for GEPU and BP.

Observations Mean Median Maximum Minimum Standard Deviation Skewness Kurtosis Jarque-Bera

GEPU

BP

108 154.502 141.405 311.855 81.081 52.028 1.043 3.522 20.795***

108 1763.816 384.797 15034.530 0.062 3056.556 2.126 7.168 159.494***

Note: *** denotes significance at the 1% level. Table 2 Full-sample Granger causality tests. Tests

H0: GEPU does not Granger cause BP

H0: BP does not Granger cause GEPU

Statistics

p-values

Statistics

p-values

Bootstrap LR test

1.598

0.660

2.802

0.430

Notes: To calculate p-values using 10,000 bootstrap repetitions. Table 3 The results of parameter stability test. Tests

GEPU

BP

VAR system

Statistics

p-value

Statistics

p-value

Statistics

p-value

Sup-F Ave-F Exp-F Lc

45.199*** 7.674* 18.358***

0.000 0.100 0.000

25.537*** 5.123 8.445***

0.003 0.404 0.006

30.219** 11.356 10.883** 2.416*

0.018 0.284 0.031 0.071

Notes: To calculate p-values using 10,000 bootstrap repetitions. ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively.

periods of 2013:M11-2013:M12, 2015:M7-2015:M8, 2017:M92017:M11 and 2019:M1-2019:M2 at the 10% significance level. And during these periods, both positive effects (2013:M11-2013:M12 and 2019:M1-2019:M2) and negative effects (2015:M7-2015:M8 and 2017:M9-2017:M11) exist from GEPU to BP. The positive effects of GEPU on BP can prove that Bitcoin can be considered as a new basket for eggs. With the subsiding of the global financial, European debt sovereign and Cyprus debt crises, GEPU is at a relatively low level (Davis, 2016). However, during the period of

8 The purpose of the Doha Round negotiations is to promote the World Trade Organization (WTO) members to reduce the trade barriers and promote the economic development in the world, especially in poor countries.

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p p

Fig. 3. Bootstrap p-values of rolling test statistic testing the null hypothesis that GEPU does not Granger cause BP.

Fig. 4. Bootstrap estimates of the sum of the rolling-window coefficients for the impact of GEPU on BP.

et al., 2018; Zaghloul et al., 2019). The theft of Bitcoin trading platforms (i.e. Bitstamp’s $5.1 million Bitcoin is looted) causes the public confidence to reduce, which leads to a large-scale escape from the Bitcoin market (Bradbury, 2015). Therefore, the decrease in demand for Bitcoin causes BP to fall, which indicates that Bitcoin can not be a new basket for eggs. The influences of the rise in uncertainty, which caused by the events of Brexit (Davis, 2016) and the U.S. presidential election (Donald J. Trump versus Hillary D. R. Clinton), have gradually subsided. During the period of 2017:M9-2017:M11, the global economic situation is stable, which means GEPU is at a low level. However, BP has experienced an unprecedented skyrocket during this time (Su et al., 2018). We can explain it from three sides, which has no relationship with the macroeconomic cycle. First, the Bitcoin futures contract is launched, which provides investment institutions with an opportunity to participate in the cryptocurrency market. This futures contract indicates the outbreak of

can conclude that Bitcoin is a new basket for eggs, as it can be considered to avoid GEPU. However, this idea can not always be supported, which can be seen in the negative effects of GEPU on BP. The European immigration crisis has occurred, that is more than 1 million refugees flood into Europe in 2015. The crash of the stock market in China leads to a downturn in emerging markets, such as Brazil, South Africa and Turkish, and the currencies in these three countries all reach their low level of the record. There are 43 authorities implement the relax monetary policies, except for the Fed. All these events cause GEPU to rise during the period of 2015:M7-2015:M8 (Davis, 2016). This should have increased the Bitcoin demand for avoiding risks, but the Fed’s interest rate hike has been nailed. Then, the public will be more confident to invest in the U.S. dollar (So, 2001), in order to reduce the losses from GEPU and maintain wealth. Hence, the reduced demand for Bitcoin causes BP to fall. In addition, the issue of Bitcoin security is also the main reason for reducing its demand (Mauro

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Although Bitcoin can be used as an asset in a portfolio, its risk will increase during a few periods of high GEPU. When BV increases, investors need to reconsider whether to reduce the proportion of Bitcoin in their portfolios or sell their Bitcoin assets, in order to reduce the losses from volatility. Therefore, these results further affirm that Bitcoin can not always be viewed as a new basket for eggs. Since the Bitcoin market is affected by the global economic situation, BP and BV may be predicted more accurately by considering GEPU. Then, we explore the predictability of BP and BV based on GEPU by performing historical average (HA), GJR-GARCH and GJR-GARCH-GEPU (GJRGARCH model with GEPU) models. We use 50%–50% in-sample and outof-sample periods (Narayan and Bannigidadmath, 2015), that is setting 2010:M7 to 2014:M12 as in-sample data and 2015:M1 to 2019:M6 as out-of-sample data. Also, we choose the forecasting horizon is 1, denoting that h ¼ 1. In order to measure the predictive effects, we employ root mean squared error (RMSE), mean absolute error (MAE) and Theil inequality coefficient (TIC) indexes. Lower RMSE, MAE and TIC indicate the smaller predictive errors, that is more accurate predictions, and these results are shown in Table 4. It is obvious that whether it is in-sample or out-of-sample prediction, the three indexes (RMSE, MAE and TIC) with GEPU are generally smaller than those without GEPU. When GEPU is taken into account, the out-of-sample prediction accuracy of BP and BV have improved by approximately 5.58% and 2.95%10, respectively. Then, in order to ensure the robustness of the above results, we also change the forecasting horizons (h¼2) or use different proportions of in-sample and out-of-sample periods (60%–40%). From Table 4, we can clearly observe that the results of 50%–50%, h ¼ 2, 60%–40%, h ¼ 1 and 60%–40%, h ¼ 2 are consistent with 50%–50%, h ¼ 1.11 Therefore, BP and BV can be predicted more accurately by considering the global economic situation (Phan et al., 2015, 2018; Narayan et al., 2016). Also, a Bitcoin trading strategy (i.e. a portfolio includes Bitcoin) that incorporates GEPU information is superior to ones that do not, this information is useful for investors to decide whether to invest in Bitcoin market. We consider a simple portfolio which only includes Bitcoin and gold, and set the initial ratio is 0.5: 0.5. The initial values of BP and gold return are 1, thus the total return is 1. If BP increases 1%, then we can change the initial ratio as 0.505:0.495, the total return is risen by 0.505%. Hence, investors can make asset allocation decision through considering the predictions. In conclusion, traditional causality test can identify only one causal relationship between GEPU and BP, which is not robust and accurate. Also, the empirical results present that the parameters in the VAR system are not stable. Therefore, we apply the bootstrap sub-sample rollingwindow causality test in this paper, in order to analyze the time-varying mutual influences between these two variables. This paper can not reach a consistent conclusion of the main issue that whether Bitcoin can be viewed as a new basket for eggs. BP will be positively affected by GEPU during some periods, which shows the hedging ability of Bitcoin. While in other periods, this view cannot be supported, mainly due to the security issues may reduce the public confidence in the Bitcoin market, while the potential bubbles may prompt investors to further invest. Thus, Bitcoin can not always be a new basket for eggs since its instability. In turn, the fluctuations in BP will make GEPU change in the same direction, and it reveals that the Bitcoin market can reflect the situation of the global economy in advance. These results can also be supported through the interaction between GEPU and BV, as well as the Hypotheses I and II. Also, based on GEPU, the predictive effects of BP and BV will be significantly improved.

the Bitcoin market (Pichet, 2018). Second, Google statistics show that the search volume of “Bitcoin” ranks second in the “Global News” category in 2017. Thus, the global attention to Bitcoin has increased dramatically (Urquhart, 2018), and with the effect of the sheep flock,9 then its demand is led to soaring. Third, with the development of blockchain technology (Guadamuz and Marsden, 2015), countries around the world are crazy to invest in Bitcoin, especially in China, Japan and South Korea. As its continuing increase in BP, investors are more willing to hold Bitcoin. Moreover, most of the investors pursue short-term returns, so it is easy to manipulate BP by speculators, which also means high risks (Li et al., 2018). Therefore, BP skyrockets during the period of low GEPU, and the negative influences from GEPU to BP can be proved. The Hypothesis I highlights that BP is affected by GEPU, but the direction can not be identified, which supports the above analyses. Figs. 5 and 6 highlight the bootstrap p-value and the orientation of the effect from BP to GEPU, respectively. BP Granger causes GEPU during the period of 2016:M12-2017:M1 at the 10% significance level, which shows a positive influence during this period. BP continues to rise due to the internal (the annual production begins to shrink, which causes the decline in supply) and external (Asian investors invest Bitcoin frantically, especially in China, Japan and South Korea) factors. With BP continuing to rise, a Bitcoin bubble may occur (Li et al., 2018). In order to avoid the Bitcoin bubble burst, and also take the security of its market into consideration, governments constantly adjust their regulatory policies (Yeoh, 2017; Lu, 2018). For instance, Europe has established a special group to monitor cryptocurrencies against money laundering and terrorist financing. Nigeria has prohibited the inter-bank transactions in cryptocurrencies (i.e. Bitcoin). China has stopped all kinds of financing activities with cryptocurrency issuance and relevant trading platform (Kaiser et al., 2018). The adjustment of Bitcoin regulatory policies may lead to an increase in GEPU. In addition, high GEPU can also be explained by the huge change in Trump’s policies (Yang, 2017) instead of the Bitcoin market. For instance, the abolition of Obama’s medical reform, the withdrawal of the Trans-Pacific Partnership (TPP) Agreement (Gleeson et al., 2018) and the new immigration policies (build walls and prohibit Muslims Act). Therefore, the positive influence from BP to GEPU can be proved, which also indicates that the Bitcoin bubble may worsen the global economic environment, then leading to higher policy uncertainty. This result is consistent with Hypothesis II, which states that BP can positively affect GEPU. In addition, investors are also concerned about whether an asset is risky relative to others, and we examine the risks of investing Bitcoin by investigating the relationship between GEPU and Bitcoin volatility (BV). From the autoregressive conditional heteroskedasticity-Lagrange multiplier (ARCH-LM) test, the null hypothesis that the residuals do not have an ARCH effect can be rejected. Also, there may be a skewed generalized error distribution, hence, this paper employs Glosten-JagannathanRunkle generalized ARCH (GJR-GARCH) model (Glosten et al., 1993), which is a volatility model that takes into account asymmetry of returns. Then, we can obtain the monthly BV, which covers the period from 2010:M7 to 2019:M6. Based on Equation (4), we develop a VAR system with GEPU and BV. Also, through employing the parameter stability test, we show that there are time-varying relationships between GEPU and BV. Thus, we perform the sub-sample test, and the bootstrap p-values and the orientations of the effects are revealed in Fig. 7. Obviously, it can be observed that GEPU has positive influences on BV during the periods of 2016:M9-2016:M11 and 2019:M1-2019:M2. That means high GEPU may lead to more volatile in the Bitcoin market during a few periods, which increases the risks of investing in Bitcoin. In turn, BV has a positive effect on GEPU during the period of 2015:M9-2015:M11. We can also conclude that the Bitcoin market plays a role in analyzing the global economic environment, which is consistent with the previous empirical analysis.

10 5.58% and 2.95% are calculated according to the prediction error of the GJR-GARCH-GEPU model relative to the GJR-GARCH model, which are the averages of the improvement in the prediction accuracy of RMSE, MAE, and TIC. 11 Also, we change the forecasting horizons (h ¼ 3, 4) or use different proportions of in-sample and out-of-sample periods (70%–30%, 80%–20%, 90%– 10%), all the results support the 50%–50%, h ¼ 1.

9 The effect of sheep flock refers to the conformity behaviour of economic individual.

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p p

Fig. 5. Bootstrap p-values of rolling test statistic testing the null hypothesis that BP does not Granger cause GEPU.

Fig. 6. Bootstrap estimates of the sum of the rolling-window coefficients for the impact of BP on GEPU.

7. Conclusion

Conversely, there is a positive influence from BP to GEPU, which indicates that the Bitcoin market is a useful tool to grasp the uncertainty of global economic policy more comprehensively. Through the mutual influences between GEPU and BP, we can conclude that Bitcoin should not always be viewed as a new basket for eggs, as its price is also determined by other internal (i.e. security issues and bubbles) and external (i.e. the value of other assets) factors. These results can generally be concluded from the mutual influence between GEPU and BV, as well as the interaction mechanism. Also, the predictive effects of BP and BV can be better if considering GEPU. Understanding the need for Bitcoin to avoid policy uncertainty and the interaction mechanism between GEPU and BP can give revelations for both investors and countries. On the one hand, GEPU has certain influences on BP and BV, also the returns and volatility in the Bitcoin market can be predicted more accurately by considering GEPU. Then, investors can grasp the trend of BP and the risks of holding Bitcoin

This paper examines the causal relationship between the Bitcoin market and global economic policy uncertainty, in order to prove whether or not Bitcoin can be viewed as a new basket for eggs. We apply the sub-sample causality test to identify the time-varying causal relationship between GEPU and BP. The empirical results show that GEPU has both positive and negative effects on BP. The positive impacts indicate that Bitcoin can be used as a hedge or safe haven to avoid GEPU, which also evidence that Bitcoin is a new basket for eggs. However, this view can not be held during periods with negative impacts, which can be explained in two ways. First, the security issues may reduce the public confidence in the Bitcoin market, though GEPU is at a high level. Then, the potential bubbles may prompt investors to further invest, even during the stable economic environment. Thus, with the characteristics of instability, Bitcoin is not always used to hedge policy uncertainty.

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p

p

p

p

Fig. 7. The results of the sub-sample bootstrap rolling-window Granger causality test between GEPU and BV. Table 4 The results of predictive effects. 50%–50%, h ¼ 1

BP

In

Out

BV

In

Out

HA GJR-GARCH GJR-GARCH -GEPU HA GJR-GARCH GJR-GARCH -GEPU HA GJR-GARCH GJR-GARCH -GEPU HA GJR-GARCH GJR-GARCH -GEPU

50%–50%, h ¼ 2

60%–40%, h ¼ 1

60%–40%, h ¼ 2

RMSE

MAE

TIC

RMSE

MAE

TIC

RMSE

MAE

TIC

RMSE

MAE

TIC

0.268 0.200 0.194

0.214 0.149 0.146

0.680 0.551 0.558

0.290 0.226 0.224

0.227 0.189 0.187

0.727 0.601 0.601

0.275 0.201 0.194

0.215 0.150 0.144

0.701 0.544 0.546

0.303 0.212 0.212

0.235 0.173 0.173

0.756 0.601 0.601

0.400 0.386 0.366

0.307 0.302 0.296

0.574 0.544 0.492

0.440 0.432 0.409

0.329 0.323 0.327

0.693 0.691 0.603

0.371 0.359 0.342

0.274 0.270 0.269

0.580 0.554 0.506

0.406 0.401 0.381

0.293 0.289 0.293

0.720 0.719 0.621

0.030 0.030 0.024

0.027 0.027 0.019

0.306 0.301 0.280

0.032 0.032 0.024

0.029 0.029 0.020

0.320 0.315 0.282

0.025 0.024 0.019

0.022 0.021 0.015

0.263 0.257 0.234

0.027 0.026 0.018

0.023 0.022 0.015

0.276 0.269 0.230

0.059 0.057 0.056

0.040 0.038 0.036

0.387 0.380 0.373

0.060 0.057 0.056

0.040 0.038 0.036

0.393 0.387 0.382

0.056 0.053 0.052

0.037 0.035 0.033

0.395 0.385 0.380

0.057 0.054 0.053

0.037 0.035 0.034

0.403 0.391 0.392

Notes: In and Out indicate the in-sample or out-of-sample prediction, respectively. HA, GJR-GARCH and GJR-GARCH-GEPU mean that the predictions based on these three models. x%-y%, h ¼ z reveals that forecasting horizon is z and the proportions of in-sample and out-of-sample periods are x% and y%.

conformity behaviour, since GEPU may bring more risks to the Bitcoin market. National governments can also predict the Bitcoin market according to GEPU, in order to formulate relevant policies to prevent the security issues and bubble risks of Bitcoin. Then, they can ensure the stable development of the Bitcoin market. On the other hand, the rise in BP and BV has positive effects on GEPU during a few periods, which means countries should analyze the global economic environment by considering the Bitcoin market. Related departments need to increase public confidence and stabilize investor sentiments when there may be a bubble or theft in the Bitcoin market. Additionally, they must adopt measures to reduce the national economic uncertainty generated by Bitcoin shocks. In a future study, we will compare the abilities of Bitcoin

according to GEPU, and then decide whether investing in the Bitcoin market to diversify the risks and maintain their wealth. If the expected returns increase, they can view Bitcoin as a new basket for eggs, that is as an asset in portfolio investment. At the same time, they also need to pay attention to the changes in volatility. Thus, a trading strategy can be developed as follow: Investors can use Bitcoin as an asset in portfolios, but its share must adjust to the global economic situation. During periods of high returns and low volatility, investors can increase the share of Bitcoin in the portfolios, especially with high GEPU, Bitcoin can be viewed as a new basket for eggs. However, during periods of high returns and volatility, investors should appropriately reduce their share and guard against the risks of theft and bubble to avoid huge losses caused by

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and other assets (i.e. gold and the U.S. dollar) to reduce the losses from economic policy uncertainty. Moreover, whether Bitcoin is a hedge asset, diversifier or safe haven it should be further explored, and its interactions with other assets should also be given significant attention.

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