key: cord-0741560-tf14ko7p
authors: Melki, Abir; Nefzi, Nourhaine
title: Tracking safe haven properties of cryptocurrencies during the COVID-19 pandemic: A smooth transition approach
date: 2021-06-17
journal: Financ Res Lett
DOI: 10.1016/j.frl.2021.102243
sha: 5ad2dd57d1c82e7e7f7347b7d810baa71fcd55d1
doc_id: 741560
cord_uid: tf14ko7p

The study aims to examine the hedge and safe-haven properties of three heavyweight cryptocurrencies—Bitcoin, Ripple, and Ethereum—against the stock, commodity, and foreign exchange markets. The study sample covers the period of August 2011 to September 2020 and therefore includes the current coronavirus disease-2019 (COVID-19) crisis. Using a logistic smooth transition regression model (LSTR2), the study findings indicate the ability of monitored cryptocurrencies to act as safe-haven assets, but such behavior differs across markets. Interestingly, during the pandemic period, Ethereum provides the strongest safe haven function for the commodity market. According to our findings, we are mindful of that the COVID-19 outbreak provides an exciting opportunity to advance our knowledge of the prominence of new coins such as Ethereum that are gradually gaining supremacy in the cryptocurrency market to the detriment of traditional cryptocurrencies like Bitcoin.

The coronavirus pandemic, discovered in December 2019 in Wuhan, China, has affected more than 200 countries. Until September 2020, it has infected almost 28 million people and caused more than 900 000 deaths worldwide . 1 The implementation of lockdown measures during the disease has severely shaken the stability of the international financial market. Baig et al. (2020) argued that rises in confirmed cases and deaths due to coronavirus are associated with a significant increase in market illiquidity and volatility. Zaremba et al. (2020) found a strong relationship between government interventions due to coronavirus and higher stock market volatility. The impact on commodity markets, such as gold and oil, has made these assets inefficient compared to the period before the pandemic (Mensi et al., 2020) . A decline in the Foreign Exchange (FX) market efficiency has also been indicated in many studies, such as Aslam et al. (2020) , Njindan Iyke (2020) , and Okorie and Lin (2020) . Studies have revealed that mainstream assets are affected by the pandemic and digital currencies have suffered important losses. An analysis conducted by Lahmiri and Bekiros (2020) suggests an upward trend in volatility and a decline in the level of stability and irregularity of cryptocurrencies during the COVID-19 crisis.

Within this environment of great loss and uncertainty caused by the ongoing COVID-19 pandemic, deep research on safe-haven

We consider the following three largest cryptocurrencies (in terms of market capitalization): Bitcoin, Ethereum, and Ripple. MSCI world, Gold Bullion LBM, and EUR/USD returns are selected as proxies for the stock, commodity, and foreign exchange (FX) markets, respectively. The sample covers the period of August 18, 2011, to September 4, 2020, except for Ethereum and Ripple, where the data started on August 10, 2015. The daily return series are defined as follows 3 :

With R t is the return series at date t, and P t andP t − 1 are the variable prices at t and t-1, respectively. 4

We start by illustrating cryptocurrency movements during the outbreak of COVID-19, which would provide some initial guidance regarding the safe-haven features of these currencies. 5 As observed in Fig. 1 , all currencies have experienced significant downward pressure on February 2020 and have been moving at a very similar trend during this period, before starting escalating again in March 2020. Table 1 displays the summary statistics of return series during the pandemic. All cryptocurrencies exhibit higher returns and larger variability than financial markets, highlighting the influence of the COVID-19 period on the latter. The return distribution for all series is negatively skewed, except for the FX market, while all variables are heavy tailed. Jarque-Bera (JB) test confirms the leptokurtic . Melki and N. Nefzi Finance Research Letters 46 (2022) 102243 behavior of return series. 6 Overall, Table 1 supports the typical conclusion that asset returns are asymmetric and non-Gaussian.

To examine the behavior of cryptocurrencies during stable and volatile periods, we employ a smooth transition regression (STR) model, which has the following expression:

where R C,t denotes cryptocurrency return; R M,t is the market return; γ measures the transition speed between regimes; c is a threshold parameter; and G (.) is the transition function that depends on the transition variable z t , as well as the parameters γ and c. In this study, z t is chosen as the lagged market return. 7 Terasvirta and Anderson (1992) distinguish between two main forms of the STR model, namely, the logistic smooth transition regression (LSTR) and the exponential smooth transition regression (ESTR). The LSTR model incorporates two main functions: the first order logistic function (LSTR1) and the second-order logistic function (LSTR2). Both LSTR2 and ESTR meet our objective, i.e., the detection of the behavior of each currency in stable and volatile regimes. However, we limit our empirical investigation to the second-order logistic function that allows a slower re-switching between regimes compared to the exponential function. This provides a more realistic description of market behavior given that investors do not respond simultaneously to news, and thus, it takes time for the market to react. G (.) in the LSTR2 model is defined as follow:

is bounded between zero and 1. It takes a value of zero if it is in the lower regime, i.e., stable period (where α 1 and β 1 belong) and unity if it is in the upper regime, i.e., volatile period (where α 1 +α 2 and β 1 +β 2 are estimated). The function is symmetric about the point c1+c2 2 , while its minimal value lies between 0 and 0.5 (stable regime). The volatile regime is observed when lim zt →±∞ G = 1 .

As mentioned in Beckmann et al. (2015) , hedging and safe-haven properties can be tested by examining β 1 and β 1 +β 2 , respectively. Precisely, a cryptocurrency is identified as a strong (weak) hedging asset for a specific market if β 1 is significantly negative (not significantly different from zero), while it is considered a strong (weak) safe-haven asset if β 1 +β 2 is significantly negative (not significantly different from zero).

To test the non-linearity of the relationship, we employ the Luukkonen, Saikkonen, and Teräsvirta (1988) test procedure which consists of replacing the transition function by the third-order Taylor approximation:

where the parameter vectors φ 1 , φ 2 , φ 3 are multiples of γ.

The linear model is included in Eq. (1) for G (zt, γ, c) = 0 and the null hypothesis for which the linear model is adequate is tested under the hypothesis H0: φi = 0 with i = 1, 2, 3 against H1: at least one φi ∕ = 0 (Teräsvirta 1998) . The test results can also identify the appropriate transition variable, i.e., the one showing the smallest p-value.

Once the null hypothesis of linearity is rejected, the choice between LSTR1 and LSTR2 is made by applying the following hypothesis test sequence:

We use a grid search over γ and c to find the appropriate initial estimates, 8 and thereafter, we proceed with the estimation of γ, c and the remaining coefficients α i and β i using the Newton-Raphson algorithm. Luukkonen et al. (1988) . The test is executed for j lag orders, where j = 1, 2, … 10, and the lagged variable with the strongest test rejection (the smallest p-value) is selected as the appropriate transition variable. (1) and (2). Numbers in parentheses are standard errors. '***', '**', and '*'denote significance at 1%, 5%, and 10%, respectively Note: This table presents the estimated results of LSTR2 model during the COVID-19 period after rejecting the linearity hypothesis. Z t lags is the lagged transition variable with the strongest test rejection (the smallest p-value). Numbers in parentheses are standard errors. '***', '**', and '*'denote significance at 1%, 5%, and 10%, respectively.

We start our analysis by conducting the linearity test on a set of transition variables delayed from one to 10. The transition variable is selected as the one showing the smallest p-value. Table 2 displays the linearity test results during the sample period and the appropriate model for each case. As mentioned previously, only the second-order logistic function can describe the behavior of cryptocurrencies in two different states of the market. Given the estimated results, we investigate, on the one hand, the hedge and safehaven properties of Bitcoin in the FX market and, on the other hand, Ripple and Ethereum behaviors in stock and commodity markets. Table 3 presents results for the LSTR2 model during the entire period. Notably, the estimated coefficients indicate significant differences among the three monitored cryptocurrencies regarding the hedge and safe-haven properties. Indeed, Ethereum exhibits a strong safe-haven function for the commodity market since (β 1+ β 2 ) are significantly negative. Ripple does not exhibit a hedge/safehaven functions in the stock and commodity markets given the positive estimated values of β 1 and β 2 . Bitcoin also appears as neither a hedge nor a safe-haven for all the three markets, confirming the conclusions of , , and Corbet, Larkin, et al. (2020) . The estimated values of γ differ across markets highlighting the usefulness of the selected approach (Beckmann et al., 2015) .

In an attempt to assess whether the behavior of the three monitored cryptocurrencies has changed during the COVID-19 pandemic, we re-estimate the model by splitting the sample into two sub-periods: before COVID-19 and during COVID-19. As seen in Table. 4, Note: This table presents the estimated results of the LSTR2 model for Ethereum-commodity market and Ripple-stock market during the pre-crisis period. Z t lags is the lagged transition variable with the strongest test rejection (the smallest p-value). Numbers in parentheses are standard errors. '***', '**', and '*'denote significance at 1%, 5%, and 10%, respectively.

Ripple is found to be a weak safe-haven asset for the FX market given that β1 + β2 ≈ 0, i.e., the two markets are almost uncorrelated during the pandemic period. We did not find a hedge or safe-haven functions provided by Bitcoin before the onset of the coronavirus. However, this coin shows a strong safe-haven behavior toward the commodity market during the pandemic scenario (β1 + β2 = -1.14). Interestingly, Ethereum is found to be a strong safe-haven for the commodity market during the pre-crisis and the This rather result could be due to considerable price fluctuations of the commodity market observed after the financial crisis (Wu et al., 2020) . By comparing the behavior of Ethereum and Bitcoin against the commodity market during the COVID-19 pandemic, it is clear that Ethereum outperforms Bitcoin as a safe-haven since β1 + β2 = -1.91. Fig. 2 illustrates how the transition function of the commodity market is switching between stable and volatile regimes during the entire period. The role of Ethereum as a safe-haven asset can be observed in regimes such as the COVID-19 period where the commodity market exhibits negative trends, but where the Ethereum performance is nevertheless positive.

Results presented so far are in line with Naeem et al. (2020) who find that Ethereum constitutes one of the strongest safe-haven assets for commodities. However, our findings have extended our knowledge about cryptocurrencies' ability to outperform Bitcoin and act as a safe-haven asset during the COVID-19 period.

Two main attributes of Ethereum may explain our findings. First, Bitcoin is gradually losing its supremacy in the cryptocurrency market to the detriment of new rival cryptocurrencies (Bouri, Hussain Shahzad, and Roubaud, 2020) . Second, the dramatic decline experienced by Bitcoin during COVID-19 has encouraged investors to transfer their funds to more attractive havens like Ethereum characterized by a low transaction fee and an advanced blockchain technology. Indeed, Ethereum is the second most decentralized cryptocurrency in the world and has low dependence on Bitcoin. Thus, investors who are seeking diversification and minimizing risk may have recourse to this currency as a store value and an effective tool that minimizes risk.

This study set out to assess the hedge and safe-haven properties of the three heavyweight cryptocurrencies, namely, Bitcoin, Ethereum, and Ripple, against the stock, foreign exchange, and commodity markets. The investigation accounts for the COVID-19 period that presents an initial testing ground for the safe-haven properties of cryptocurrencies. Using a second-order LSTR model, our results highlight the ability of Ripple to act as a weak safe-haven asset for the forex market during the pandemic crisis. Moreover, while Bitcoin and Ripple show neither a safe-haven nor a hedge for the three markets, they provide, respectively, safe-haven functions for the commodity and FX markets during the pandemic period. One unanticipated finding was the behavior shown by Ethereum. Indeed, this currency outperformed Bitcoin by providing a stronger safe-haven for the commodity market during both the pre-crisis and COVID-19 periods. These findings are consistent with those of Mariana, Ekaputra, and Husodo (2020) who argue that although both cryptocurrencies exhibit safe-haven features, Ethereum appears to be a better safe-haven than Bitcoin.

In summary, our work sheds light on the useful role of cryptocurrencies other than Bitcoin, such as Ripple and specifically Ethereum, to exert their safe-haven function during extreme downward situations. Our results also support Corbet, Hou, et al.'s (2020) findings arguing that using cryptocurrencies as safe-haven investments during stress periods presents a significant alert for policymakers to focus on cryptocurrencies and adjust their monetary policy decisions.

Finally, one important limitation that needs to be mentioned is the non-consideration of the heteroscedasticity problem presented in cryptocurrency behavior. Thus, as an interesting path for further research, we would suggest an empirical analysis that accounts for the GARCH effect. Further studies also need to be conducted to investigate the safe-haven qualities of stablecoins during the disease period.

Conception and design of study: Abir Melki (A.Melki) , Nourhaine Nefzi (N.Nefzi), Acquisition of data: Nourhaine Nefzi (N.Nefzi) Analysis and/or interpretation of data: Nourhaine Nefzi (N.Nefzi)

Drafting the manuscript: Abir Melki (A.Melki), Nourhaine Nefzi (N.Nefzi), revising the manuscript critically for important intellectual content: Abir Melki (A.Melki) ; Nourhaine Nefzi (N.Nefzi),

Approval of the version of the manuscript to be published (the names of all authors must be listed) : Abir Melki (A.Melki); Nourhaine Nefzi (N.Nefzi), Urquhart, An.drew, Zhang, Hanx.iong, 2019 . Is Bitcoin a Hedge or safe haven for currencies? An intraday analysis. Int. Rev. Financ. Anal. 63, 49-57. Wu, F.ei, Zhao, Wa.n-Li L., Ji, Qi.ang, Zhang, Day.ong, 2020 . Dependency, centrality and dynamic networks for international commodity futures prices. Int. Rev.

Econ. Finance 67, 118-132. Zaremba, Ad.am, Kizys, Ren.atas, Aharon, Da.vid Y., Demir, End.er, 2020. Infected markets: novel coronavirus, government interventions, and stock return volatility around the globe. Finance Res. Lett., 101597

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We would like to thank Professor Samuel A. Vigne, Editor-in-Chief, and the anonymous reviewer for helpful and constructive comments which have greatly improved the paper.

The decision rule for choosing between the LSTR1 and LSTR2 model is given as follows:• The rejection of H 03 : φ3 = 0 implies the selection of the LSTR1 model. If H 03 is not rejected, then we proceed to test H 02 :