A diversifier is an asset that usually has a positive correlation with another asset. In contrast, a hedge is an asset that usually correlates negatively with another asset or is uncorrelated with it. Finally, a safe haven is an uncorrelated or negatively correlated asset with another asset during stressful times. Subsequent studies have attempted to study the role of Bitcoin and other cryptocurrencies based on these categories, but their findings are mixed. Although several works have supported the ability of Bitcoin to serve as a safe haven, others concluded that Bitcoin has a limited ability to improve the risk—return relationship, and some of them even implied that Bitcoin amplifies risk.
Using a multivariate stochastic volatility model and dynamic conditional correlation DCC approach, Kliber et al. The only exception for Bitcoin being a safe haven is in Venezuela when the investment is in bolivar. Mensi et al. Meanwhile, using the cross-quantilogram approach to test the safe-haven properties of Bitcoin, gold, and commodities, Shahzad et al.
Wang et al. They indicated that cryptocurrencies are a safe haven for several international indices but only in certain periods. Moreover, the safe-haven property is more pronounced in developed markets and when using larger in terms of market capitalization and more liquid cryptocurrencies. Moreover, Umar et al. They documented that the cryptocurrency market is less integrated with the technology sector and thus less exposed to systemic shocks.
However, Smales ruled out the potential of Bitcoin to act as a safe-haven asset. He claimed that this is due to more volatility, less liquidity, and higher transaction costs of Bitcoin. Klein et al. They indicated that Bitcoin and gold are entirely different. Specifically, correlations of Bitcoin with conventional assets behave differently from gold. Bitcoin is positively correlated with equity indices in market downturns, suggesting that it is not a safe haven.
Baur et al. Dyhrberg a , b concluded that Bitcoin has several similarities to gold and the dollar, which implies a hedging property. They argued that Bitcoin can serve as a hedge for equities and against the dollar. Corbet et al. They concluded that Bitcoin is not a safe haven. In fact, they argued that it is actually an amplifier of contagion. Moreover, Conlon et al. Tether does demonstrate a safe-haven property, but it is not consistent across time.
They also concluded that cross-currency hedging strategies fail during a systemic event such as the COVID pandemic. Our final contribution is the inclusion of the three components of the yield curve in our analysis. Doing so contributes to the growing field of studies dealing with the relationships among conventional currencies, Bitcoin, macroeconomic information, and monetary systems e.
For example, in their exploration of the response of the major cryptocurrencies Bitcoin, Ethereum, Litecoin, and Ripple to tightening versus easing monetary regimes, Nguyen et al. They found that although Bitcoin responds to FOMC announcements, it does not react to macroeconomic announcements, such as the employment rate and the Consumer Price Index. Two interesting mechanisms may underly the interplay between Bitcoin and the yield curve.
If Bitcoin is indeed disconnected from the monetary systems, it should also be insulated from fluctuations in the yield curve. However, the growing popularity of Bitcoin that has resulted in its integration by firms and financial markets could result in increased connectivity.
To summarize, our study extends and contributes to the existing literature in several aspects. First, we extend the examinations done so far in the context of major currencies and their connectedness with the components of the yield curve and Bitcoin. Although several recent papers have examined the relationship between Bitcoin and conventional currencies, they are still scarce, and we are motivated to extend the empirical evidence in this respect.
In addition, we are not aware of any study that has included the components of the yield curve, major currencies, and Bitcoin in its assessments about their connectedness. Our sample period spans from May 11, , to November 26, , based on the availability of the matched series data.
As the first step, we construct the components of the yield curve. Toward this end, we utilize the zero-coupon sovereign yield for the US with 15 monthly maturities, including 3, 6, 9, 12, 24, 36, 48, 60, 72, 84, 96, , , , and months. The sovereign yield data are retrieved from Bloomberg.
The decomposed components of the yield curve are not stationary; therefore, we take the first difference of each series to ensure stationarity and confirm it with augmented Dickey— Fuller ADF tests. The exchange rates are not stationary, and therefore, we compute the first difference of levels for each currency.
We confirm stationarity by employing the ADF tests. Lastly, we obtain the historical volatility of each fiat currency and Bitcoin from Bloomberg. Here again, the raw volatility series obtained from Bloomberg is non-stationary. Consequently, we take the first differences to ensure stationarity and confirm it with the ADF tests. We note the significantly high average price and standard deviation of Bitcoin compared to the other fiat currencies, which we attribute to the strong demand for Bitcoin in recent years.
Figure 4 a of Appendix depicts the level and the first difference of the exchange rates and the components of the yield curve. Similarly, Fig. Notes : This figure shows the level and first difference of the exchange rate of Bitcoin and the fiat currencies in the top and bottom panels, respectively. The levels and first difference of the components of the yield curve are also reported. Notes : This figure shows the level and first difference of the historical volatility of Bitcoin and the fiat currencies in the top and bottom panels, respectively.
Our empirical framework is composed of two methodological steps. Then, we estimate the three US yield curve components: level, slope, and curvature. Notably, the use of Nelson—Siegel approach for estimating the yield curve components conveys several advantages. Among others, it offers parsimonious estimates for the yield curve components, does not impose arbitrage-free restrictions, and fits any type of yield curve. Several studies in the literature e.
Consequently, this approach became a preferable approach among researchers. Subsequently, we follow the methodology of Diebold and Yilmaz , , for a dynamic track of the connectedness between the desired system variables. In the following subsections, we supply a concise description of each step.
The groundwork of the Nelson—Siegel model inspired Diebold and Li to suggest a dynamic estimation for the level, slope, and curvature of the yield curve. They stated that the three coefficients in the Nelson—Siegel curve are latent level, slope, and curvature factors. The following is the state-space representation of Diebold and Li :.
Estimating and examining the spillover and connectivity dynamics of variables in a certain system e. We adopt the novel method of Diebold and Yilmaz , , that is a well-common approach and enhances the comparability of our results. Diebold and Yilmaz , , approach is based on the well-known VAR model by Sims , which has been a main tool by researchers and economists. In essence, they suggested an interesting arrangement and use of the forecast error variance decomposition FEVD as an interpretation for the connectivity between the variables of a certain system.
Given that the FEVD provides information about the degree to which a counterpart variable explains a future variation in a certain variable in the system, they managed to construct a connectedness off-diagonal matrix for the variables of the system. Equation 3 describes that each variable in the system is explained by its own lagged values and the rest of system variables. Under the assumption of covariance stationarity, the moving average representation of Eq.
The traditional techniques like the Cholesky factorization for estimating orthogonal innovations are sensitive to variable ordering. Therefore, we follow the solution of Koop et al. Sums of forecast error variance contributions i. The off-diagonal entries of D H are the parts of the N forecast error variance decompositions of relevance from a connectedness perspective; they measure the pairwise directional connectedness.
In particular, the gross pairwise directional connectedness from j to i is given as follows:. To obtain the net role of each variable relative to a certain other counterpart variable, that is, whether it functions as a transmitter or a receiver, we compute the net pairwise directional connectedness as follows:.
Similarly, one can be interested in the total connectedness FROM all variables as a whole to variable i. In this case, the connectedness measure is computed as follows:. Alternatively, the total connectedness of a single system variable j TO the system can be measured by. In this respect, the net total directional connectedness between a single system variable i and the system as a whole is defined as.
The total connectedness measure in the system is bounded in the interval of 0 and 1. The lower bound represents a variable system according to which no spillover risk exists, whereas the upper bound of 1 applies to the case of a full connectedness according to which all risks are generated by the system variable interactions. Following Zeng et al. Diebold and Yilmaz pointed that a day look-ahead may be in line with the day value at risk VaR required under the Basel accord.
Naturally, one can use different lengths from day look-ahead, which matches its own risk management preferences. We also use a rolling window of days approximately nine months to evaluate the dynamic connectedness measures. We discuss the connectedness of the exchange rates and the yield curve components first, followed by the connectedness of the exchange rate volatility and the yield curve components. The total connectedness index TCI in the right bottom corner that expresses the overall degree of connectivity in the system is Among the safe-haven currencies, the Euro receives the most spillovers from the system.
Net last row shows the net directional spillover of each variable. TCI bold right bottom corner is the total connectedness index of the system of all variables. A positive value implies that the variable is a net transmitter, whereas a negative value implies that the variable is a net receiver of spillovers.
Note that the level and slope of the yield curve along with the Euro are net transmitters of spillovers, whereas all the other variables are net receivers of spillovers. Interestingly, all three components of the US yield curve or other currencies have negligible influence on Bitcoin.
Most variations in the system are generated by the traditional currencies and yield curve components. Bitcoin contributes only 0. Therefore, we might argue that Bitcoin exhibits risk diversification attributes and may hedge against changes in other safe-haven currencies or fluctuations in the yield curve. However, investors should keep in mind that these results also imply that, as a single asset class, Bitcoin is heavily exposed to its idiosyncratic shocks.
For the other currencies, relatively larger spillover exists TO and FROM the yield curve components and within the currencies themselves. Next, we look at the pairwise spillover. To identify the net transmitters and net receivers of spillovers on a pairwise basis, we resort to network analysis, which enables us to easily analyze the pairwise connections of net transmitters and receivers of spillover.
The left-hand panel of Fig. The source of the arrow indicates the transmitter of the spillover, whereas the edge of the arrow shows the receiver of the spillover for that particular pair. Looking at the left-hand illustration, we notice that the Euro is the dominant transmitter of shocks to other variables in the system red lines. Meanwhile, the Japanese Yen is the most prominent recipient of spillover from all other variables blue lines.
Although Bitcoin seems to be also a main recipient of spillovers, as mentioned earlier, the relative magnitude of spillovers for Bitcoin is negligible. The left-hand right-hand graph shows the network connectedness in terms of exchange rates volatility of exchange rates. Arrows indicate the net directional connectedness between two variables in the system with a one-way direction arrow. The source of the arrow shows the transmitter, and the edge of the arrow shows the receiver of the spillover.
More arrows mean a more influential variable in the connectedness. A red font arrow means that the variable has the largest transmitter of pairwise spillovers, while a blue means largest receiver of spillovers. We extend our analysis and discuss the connectedness of the yield curve components and the volatility of the exchange rates of Bitcoin and the fiat currencies. Overall, the results for volatility connectedness exhibit similar patterns to those for the exchange rate connectedness.
The TCI for the yield curve and volatility series is Here again, we note that the slope and level of the yield curve are the most pronounced transmitters, where the curvature is the highest receiver of shocks. In addition, the Euro and the CAD are the net transmitters. All other currencies are net receivers. The lower magnitude of connectedness may be attributed to their potential diversification benefits. Static connectedness of the volatility of the exchange rates and components of the yield curve.
To analyze the pairwise connectedness of the volatility of the exchange rate series and the yield curve components, we look at the right-hand illustration in Fig. The yield curve level exhibits the largest number of pairwise transmission spillovers to other variables in the system, making it the most influential system variable. Meanwhile, the CHF is the most prominent in terms of the number of pairwise spillovers absorbed, although, as aforementioned, its magnitude is relatively low.
The curvature of the yield curve is the next dominant receiver of pairwise spillovers in the system. Although the number of links with other system variables is small, the magnitude is substantially higher than with the CHF.
Our static analysis results accord with those of previous studies documenting the weak connectedness between Bitcoin and traditional assets e. However, the author also stated that time-conditional effects that remain hidden in the aggregate present a notable temporary transmission of shocks in the system under study. Therefore, the use of the total period may hide certain patterns due to possible structural breaks or changing trends in their connectivity.
Hence, the connectedness portion may be different under a dynamic estimation using a rolling window approach. Our second step in the connectedness examination for the variables of interest involves a dynamic approach. Diebold and Yilmaz , , and many other studies have advocated using a rolling window procedure to address several caveats that might be involved with a static approach. These drawbacks include instability, possible structural breaks, non-stationarity, and the effect of outliers in the variables.
A dynamic approach is crucial, particularly when the system includes volatile asset classes, such as fiat currencies and Bitcoin. Therefore, using dynamic analysis not only allows us to comprehend the evolution of the connectedness but also provides an important robustness test and a more informative picture.
Figure 2 describes the estimated TCI across time. It contains two graphs depicting the index for the exchange rates Fig. We can infer from the graphs that the relationship between the system variables varies across time, justifying the dynamic estimation we conducted. Specifically, one can observe that connectedness is high in some periods. In general, the connectedness index for the exchange rates Fig. We also see an increasing trend in connectedness around the second half of , which might be attributed to the political uncertainty related to BREXIT.
Total Dynamic Connectedness Measures of the System. The upper figure is illustrates the dynamic connectedness of the yield curve components and the exchange rate of Bitcoin and the safe haven currencies, while the bottom depicts the connectedness of the yield curve components and the volatility of Bitcoin and the safe haven currencies. Our general dynamic analysis so far shows that the connectedness of the entire system of variables varies over the sample period. However, analyzing the contribution of each variable in the overall connectedness of the system is also equally important.
Doing so will help us understand the potential role of Bitcoin in terms of risk reduction. Net Dynamic Connectedness Measure of the System. Notes : The figures describe the dynamic net spillover between each variable and the whole system. Positive values indicate a variable X as a net transmitter, while negative values indicate a variable as a net receiver in the system.
The top figure represents the spillover of the system of the yield curve components and the exchange rate of Bitcoin and the safe haven currencies. The bottom figure shows the spillover of the yield curve components and the volatility of Bitcoin and the safe haven currencies.
The rest of notations as in Fig. Figures 3 a, b provide additional insights into the interactions of each variable with the system. Figure 3 a depicts the net contribution of each variable to all other variables in the system of exchange rates. Positive values indicate that the variable is a net transmitter, whereas negative values imply a net receiver in the system.
The results correspond to the general results we observed in the static analysis. The Euro, the level, and the slope of the yield curve are net transmitters. Bitcoin is a net receiver of spillovers during most of the sample period, with the notable exception of during the COVID pandemic.
Furthermore, similar to other currencies, Bitcoin exhibits periods of relatively strong and weak connectedness. Based on these findings, we conclude that its role in terms of risk reduction is also dynamic, varying between a hedge and a diversifier. Figure 3 b. Similar to the previous results, the Euro is the dominant transmitter of spillovers during most of the sampled period, whereas the CHF exhibits alternating patterns.
Most other currencies are net receivers. Here again, we note that the CAD is a major transmitter of spillovers during the COVID period, whereas all other currencies are predominantly net receivers. For the yield curve components, the level and slope are the main transmitters, whereas the curvature is the receiver of spillovers. Lastly, Bitcoin is primarily a receiver of spillovers from the system of the exchange rate volatility and yield curve components.
It also still exhibits dynamic connectedness, which strengthens particularly during the COVID period. These results underscore the importance of observing the connectedness level through the lens of a dynamic approach. Doing so allows us to conclude that Bitcoin fails to be a safe-haven asset, and its ability to reduce risk varies between hedging and diversifier roles over time. Figure 6 a depicts the spillover from each listed variable to all other variables in the system, whereas Fig.
At some points, the spillovers and connectedness are relatively low, but at others, they are considerably higher. An additional spike coincides with the removal of the cap on the Swiss Franc relative to the Euro in The rest of the notations are as in Fig.
Similar to our previous results, although Bitcoin has little connection with the rest of the currencies and the yield curve components in some periods, it seems increasingly connected to the system in stressful times. An important observation from our dynamic analysis reveals that the hedging capabilities of Bitcoin with other currencies and the yield curve components decrease when we shift from a static to a dynamic analysis.
As discussed earlier, the connectedness actually strengthens during turbulent times. These results accord with previous studies arguing against considering Bitcoin a safe-haven asset. For example, Smales ruled out Bitcoin as a safe-haven asset based on its high volatility, low liquidity and high transaction costs. Similarly, Conlon, Corbet and McGee conclude that Bitcoin fails to act as a safe-haven asset against major international equity indices.
The paper estimates the inter-connectedness of the variables in both the exchange rates and the volatility of the exchange rates. We contribute to the literature dealing with major currency interaction with both the yield curve and the dominant cryptocurrency, the Bitcoin. Given the growing popularity of Bitcoin among individuals, investors, and companies, we present an important attempt to track its potential bidirectional interplay with major currencies.
To determine whether Bitcoin has the property of a safe-haven asset, we took a step forward and employed a dynamic connectedness approach, which can distinguish its connectedness during various phases of the economic cycle covered in our sample period. Our dynamic analysis shows that its connectedness actually strengthens during crises and due to policy shocks. These findings from the dynamic analysis support previous studies and confirm that Bitcoin is far from being a safe-haven asset e.
Policymakers, financial market participants, and practitioners e. Consequently, it may help them improve their allocation and risk management decisions. Future works on the topic debated in this paper could be further extended in at least two interesting avenues.
First, it would be valuable to explore the system connectedness with other leading cryptocurrencies apart from Bitcoin or extend the system explored to include precious metals from the commodity market. Second, it would be interesting to consider the varying effects of good and bad news on the connectedness of the system. The authors are thankful to the editor and anonymous referees for their insightful comments.
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