TEL  Vol.9 No.3 , April 2019
Cryptocurrencies and Investment Diversification: Empirical Evidence from Seven Largest Cryptocurrencies
ABSTRACT

The study examines the diversification capability of seven cryptocurrencies with the largest market size against risks from economic factors as oil price, gold price, interest rate, USD strength, and S&P500. Using the weekly data of Bitcoin, Litecoin, Ripple, Stellar, Monero, Dash, and Bytecoin in the period Aug/2014-Jun/2018, the study finds that there are structural breaks and ARCH disturbance in each cryptocurrency, suggesting a systematic risk within the cryptocurrency market. However, the causality between cryptocurrencies and economic factors is undirected. Interestingly, our findings show that cryptocurrencies are insignificant correlations with economic factors. The result implies that cryptocurrencies can not be assumed as financial assets to hedge systematic risks from economic factors.


1. Introduction

The cryptocurrencies with a decentralized and open-source technology have extensively received attention from finance literature in recent years [1] . The fact is true that some financial institutions, public organizations and governments have recognized Bitcoin and other cryptocurrencies as official financial assets [2] . From the original objective as an alternative payment system independent of any central banks, the popularity of cryptocurrencies has tremendously received much attention from the literature due to their increased capitalization values. However, because of lacking foundational theories, linkages between cryptocurrencies and economic factors are still open to debate.

Some studies have focused on the volatility of cryptocurrency prices, especially the Bitcoin [3] - [10] . Other studies have emphasized the relationships between Bitcoin price and economic factors. Su, Li, Tao, and Si [11] showed that there have been four explosive bubbles in China and the U.S. market during the periods of the huge surges of Bitcoin prices and the shocks from foreign or domestic markets. Concerning Bitcoin and other assets [12] [13] [14] [15] found that the fundamental price of Bitcoin is close to zero. About the relation between cryptocurrencies, for instance, Bitcoin and Ethereum [16] [17] unveiled clear bubble behaviours during the time after 2013. Gandal, Hamrick, Moore, and Oberman [18] added that the suspicious trading resulted in an unprecedented spike of the USD-BTC exchange rate in late 2013.

Interestingly, as a hedge instrument against market-specific risk and uncertainty, Bitcoin may be a priority choice in portfolio management for financial markets [11] [19] [20] [21] . Some arguments show that the average monthly volatility of Bitcoin returns is higher than for gold or a set of foreign currencies indexed by dollars [22] . The Bitcoin price is more sensitive to changes in economic and market factors in the short-run, but less sensitive to technological factors in the long-run [23] . As in Al-Yahyaee, Mensi, and Yoon [24] , the Bitcoin market is easy to be broken in comparison to other currencies markets, while Gajardo et al. [2] show that Bitcoin has a greater multifractal spectrum than other assets on its cross-correlation with the WTI, the Gold and the DJIA. Concerning the role of other cryptocurrencies, Ciaian, Rajcaniova, and Kancs [25] revealed that Bitcoin seems to be less affected macro-financial indicators in comparison to the altcoins price formation. On the contrary, Ciaian et al. [25] show that relationships among cryptocurrencies are complex, especially in the context of ICOs leading to a huge of cryptocurrencies available [26] .

This study contributes to the literature by shedding the light on the capability of seven cryptocurrencies with the largest market capitalization in hedging against the systematic risks in line with economic factors. Specifically, the Granger causality tests between each cryptocurrency with economic factors show that the oil price, and the USD index cause most of the selected cryptocurrencies. While only BTC and LTC are among the cryptocurrencies, which cause the oil price, the USD index, the S&P500 index and the gold price, respectively. In addition, there exist structural breaks and ARCH disturbance in the price of each cryptocurrency, suggesting a systematic risk within cryptocurrency markets. Moreover, the USD index has negative effects on all seven cryptocurrencies, while other economic factors have inconsistent effects on all cryptocurrencies. The results imply that the cryptocurrencies are likely impacted by economic factors other than a hedge for economic factors.

Next section presents the methodology and data. The results and discussions are in Section 3. Some conclusions are remarked in the final section.

2. Methodology and Data

The study surveys all cryptocurrency markets and collects the daily closing price of each cryptocurrency and come up to 20 largest cryptocurrencies. Matching each cryptocurrency together with economic factors to find the longest time span possible, the study narrows down to seven cryptocurrencies in terms of largest market capitalization including Bitcoin, Litecoin, Ripple, Stellar, Monero, Dash, and Bytecoin in the period from 8 Aug 2014-7 June 2018. Economic indicators are proxied by WTI Oil price, Gold price, S&P500 index, LIBOR, and USD index. The weekly data of WTI Oil price, S&P500 index, Gold price, LIBOR (one month), and the bid price of USD index are collected from Thomson Reuter and Fred. All variables are taken by logarithm to reduce heteroskedasticity, except for LIBOR. Definitions, sources, and statistical descriptions of variables are presented in Table 1. The data of cryptocurrencies is collected from Coinmarketcap in Aug/2018. The LIBOR is collected from Federal Reserve Economic Data St. Louis Fed (FRED). All remained economic factors are collected from Thomson Reuter.

In this study, we collect the weekly data of all variables to enlarge the time span of the sample. In which, the weekly close values of all variables are used. Table 1 shows the primary data before taking logarithm. Bitcoin has highest average price then Dash, Monero, and Litecoin in the followings. To examine linkages between cryptocurrencies and world economic indicators, the study conducts Granger causality tests for each of pair variables. To detect the associations of cryptocurrencies with systematic risks, the study uses the GARCH (1, 1) based on the existence of ARCH disturbance. GARCH (1, 1) is formed as followings.

Y t = β 0 + β i X t + ϵ t (1)

Table 1. Data description (primary data).

Note: Time period: 8 Aug 2014-7 June 2018 due to the availability of Cryptocurrency prices from Coinmarketcap [from Aug 2014]. Source: Coinmarketcap, Fred, Thomson Reuters.

ϵ t | φ t 1 ~ N ( 0 , t 2 ) (2)

t 2 = γ + α 1 ϵ t 1 2 + δ 1 t 1 2 (3)

where: Y is each cryptocurrency; X is a set of economic factors including oil price, SP500, gold price, USD index, and LIBOR. β is coefficient. ϵ is conditional error term. 2 is GARCH term. ϵ 2 is ARCH term. To check robustness, the study employs dynamic conditional correlation Multivariate GARCH model (Multivariate Autoregressive Conditionally Heteroskedastic―MGARCH). Due to the existence of ARCH disturbance and structural breaks in variables, MGARCH is more flexible than the conditional correlation MGARCH model, and more parsimonious than the diagonal vech MGARCH model [27] [28] [29] . The estimated results of conditional correlations from DCC MGARCH [30] [31] between each cryptocurrency and economic factors are helpful in detecting the associations between cryptocurrencies and economic factors.

The DCC GARCH model is given:

Y t = α X t + ϵ t (4)

ϵ t = H t 1 / 2 γ t (5)

where: Y is cryptocurrency; X is a set of economic factors; H is the Cholesky factor of the time-varying conditional covariance matrix; γ t is the vector of normal, independent, and identically distributed innovations.

3. Results and Discussions

3.1. Basic Results

Figure 1 shows that Bitcoin has the highest price with the peak at the end of 2017. All six other cryptocurrencies are the same patterns in this period. The US stock market (S&P500) has a stable trend, while gold price and USD index show a small fluctuation during this time. Oil price steadily decreases from 2014 until 2015 before increasing until 2018. LIBOR shows a steadily increasing trend, especially from 2015 (Figures 1-4).

Figure 1. Price of 7 largest cryptocurrencies.

Figure 2. Economic factors in the period Aug/2014-Jun/2018 (SP500 and Gold are left axis; Old, LIBOR (1M) × 100 and USD index are right axis).

Figure 3. Cusum test for all variables (in log forms).

Figure 4. Cusum test for all variables (in 1st difference of log forms).

Tables 2-4 reports the characteristics of variables examined by the Dicky-Fuller unit root test, Johansen Cointegration test, and Granger causality test, respectively.

Results in Table 2 show that all variables (excluding USD index) have stationary at the 1st difference. For testing cointegration, results in Table 3 show that the LIBOR and the USD index have cointegration with all cryptocurrencies. The S&P500 index has cointegration with BTC, XRP, DAS and BCN, while the oil price has cointegration with XMR and DAS. Interestingly, the gold price has no cointegration with all cryptocurrencies. These results suggest that there are strong relationships between the USD index and LIBOR with cryptocurrencies.

The results of Granger causality test for each pair of cryptocurrency and economic indicators are presented in Table 4. The causal relations between these variables are asymmetric. There exists bidirectional causality between the oil price and most of the cryptocurrencies, except for BTC. The USD index causes all cryptocurrencies. However, only BTC, LTC, DAS and BCN, respectively, causes the USD index. The S&P500 index causes BTC, LTC, and DAS, respectively; and only XRP, XLM, and BCN, respectively causes S&P500 index. The goldprice causes BTC, XRP, XMR, and DAS, respectively; and only LTC causes the gold price. In summary, the oil price, and the USD index cause most of the selected cryptocurrencies. Conversely, only BTC and LTC are among the cryptocurrencies, which cause the oil price, the USD index, the S&P500 index and the gold price, respectively.

As in Table 2, all variables are stationary at the 1st difference (excluding USD index. Taking the 1st difference of all variables, we examine the structural breaks and ARCH disturbance for each variable. Results of Table 5 show evidence that there are structural breaks in economic factors (e.g., oil price, LIBOR, USD index). In addition, there is an ARCH disturbance in case of XRP, XLM, XMR and BCN, respectively. We then run GARCH (1, 1) for each cryptocurrency with economic factors and results are reported in Table 6.

The results in Table 6 show that there exist structural breaks and ARCH disturbance in the price of each cryptocurrency, suggesting a systematic risk within

Table 2. Correlation matrix.

Note: *, **, *** denote significant levels at 10%, 5%, 1% respectively. P-values are in parenthesis. All variables are examined in log forms (exclude LIBOR).

Table 3. Dickey Fuller test for stationary for level and first different data.

Note: *, **, *** denote significant levels at 10%, 5%, 1% respectively.

Table 4. Johansen Cointegration test.

Note: *, **, *** denote significant levels at 10%, 5%, 1% respectively. All of pair asset are tested to obtain suitable lag-order selection statistics.

Table 5. Granger causality tests for each of pair assets.

Note: *, **, *** denote significant levels at 10%, 5%, 1% respectively. All of pair asset are tested to obtain suitable lag-order selection statistics.

Table 6. Cumulative sum test and Structural Break test for 1st Difference Data.

Note: *, **, *** denote significant levels at 10%, 5%, 1% respectively.

cryptocurrency markets. Concerning economic factors, observations show that the USD index has negative effects on all seven cryptocurrencies, while other economic factors have inconsistent effects on all cryptocurrencies. The implication drawn from these results is that cryptocurrencies are considered as a financial asset to hedge systematic risk from economic factors.

3.2. Check Robustness

The inconsistent results of economic factors in line with the existence of structural breaks and ARCH disturbance among variables leading to an ideal condition for DCC MGARCH model in which the conditional correlation matrix from estimation is robust to analyse the relationship among variables [30] [31] . All results from DCC MGARCH are reported in Tables 7-13 for each cryptocurrency.

For BTC, as in Table 7 the oil price, the S&P500 index, and LIBOR have significantly negative correlations with BTC. The results suggest that BTC seems to not be a tool for hedging the risk of USD index and gold price. Our finding is different from the studies [20] [21] that Bitcoin can hedge against USD or any currency.

For XRP, the results of Table 8 show that XRP has a significant negative correlation with the oil price. Moreover, as in Table 4, the oil price causes XRP. These results suggest that the increased oil price reduces the price of XRP.

For other cryptocurrencies, as in Tables 9-14 DAS has a positive correlation with LIBOR, but negative correlation with USD index. XLM has a positive correlation with SP500 index. Our findings show that the correlations between cryptocurrencies and economic factors are inconsistent, suggesting that cryptocurrencies may be not tools or financial assets to hedge systematic risks, which are caused by economic factors.

Table 7. GARCH (1, 1) for each cryptocurrency.

Note: *, **, *** denote significant levels at 10%, 5%, 1% respectively. Standard errors are in bracket.

Table 8. Dynamic conditional correlation MGARCH model of Bitcoin.

Note: *, ** and *** denote the significance level at 10%, 5% and 1%. Standard errors are in bracket.

Table 9. Dynamic conditional correlation MGARCH model of Ripple.

Note: *, ** and *** denote the significance level at 10%, 5% and 1%. Standard errors are in bracket.

Table 10. Dynamic conditional correlation MGARCH model of Litecoin.

Note: *, ** and *** denote the significance level at 10%, 5% and 1%. Standard errors are in bracket.

Table 11. Dynamic conditional correlation MGARCH model of Stellar.

Note: *, ** and *** denote the significance level at 10%, 5% and 1%. Standard errors are in bracket.

Table 12. Dynamic conditional correlation MGARCH model of Monero.

Note: *, ** and *** denote the significance level at 10%, 5% and 1%. Standard errors are in bracket.

Table 13. Dynamic conditional correlation MGARCH model of DASH.

Note: *, ** and *** denote the significance level at 10%, 5% and 1%. Standard errors are in bracket.

Table 14. Dynamic conditional correlation MGARCH model of Bytecoin.

Note: *, ** and *** denote the significance levels at 10%, 5% and 1%. Standard errors are in bracket.

4. Conclusions

With the assumption as financial assets, the question on the capability of cryptocurrencies in hedging to systematic risk is quite worthy to investigate. Selecting seven cryptocurrencies with largest capitalization level, the study investigates correlations between the selected cryptocurrencies and economic factors that are proxied by oil price, gold price, interest rate, USD strength, and S&P500. Some main findings are noticeable.

First, there are strong correlations between cryptocurrencies. Moreover, there are also structural breaks and ARCH disturbance in each cryptocurrency. We suggest a systematic risk within the cryptocurrency market. Second, the Granger causality tests show that the relationship between cryptocurrencies and economic factors are undirected. Third, GARCH (1, 1) tests provide evidence that cryptocurrencies are insignificant correlations with economic factors with the implication that cryptocurrencies are not assumed as financial assets to hedge systematic risks. The results are robust by DCC MGARCH tests. The results are significant for financial investors on the perspective of the diversification. That is, the financial investor must be more careful in using cryptocurrencies as financial assets, especially in diversifying their portfolio since they have low capability in diversification within cryptocurrency market and also with economic risks.

Acknowledgements

This study is funded by the University of Economics Ho Chi Minh city.

Cite this paper
Canh, N. , Binh, N. and Thanh, S. (2019) Cryptocurrencies and Investment Diversification: Empirical Evidence from Seven Largest Cryptocurrencies. Theoretical Economics Letters, 9, 431-452. doi: 10.4236/tel.2019.93031.
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