This study aims to examine the properties of four major cryptocurrencies and how they can be used as a simpler alternative mode of hedging foreign exchange (FX) risks as compared to existing mainstream financial risk management techniques.
This study uses a combination of visual data representations and the classic Fama and Macbeth (1973) two-pass procedure regressions.
The findings show that cryptocurrencies can be a more effective hedge against FX risks as compared to other common hedging instruments and/or techniques such as gold or a diversified currency portfolio.
The conclusions were arrived at based only on a small group of cryptocurrency, i.e. Bitcoin, Ethereum, Litecoin and Ripple. Other cryptocurrencies such as Dogecoin or ZCash might exhibit different properties.
Cryptocurrencies can be cost-effective and cost-efficient instruments that provide a solid hedge for investors and/or firms that are exposed to global FX volatility. Its ease of trade and virtually zero barriers to entry makes it an easily accessible alternative hedge instrument as compared to more complex items such as derivatives.
If cryptocurrencies are to be accepted into mainstream usage, a detailed examination of its various uses is necessary. In particular, as they are often touted to be the future of currency, its properties and price behavior relative to other mainstream financial instruments need to be well-understood, not only by finance professionals but also by laypersons.
Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited
Cryptocurrencies are here to stay. Its use in network-based, cashless payment systems continue to proliferate to the point where regulatory bodies such as the US Federal Reserve have begun to draft guidelines and legislation for its use. With its widespread popularity, a clear understanding of its uses and the parties involved are crucial. In this regard, Tasca et al. (2018) provided a comprehensive mapping of the parties, who are materially involved with Bitcoin transactions. These include miners, gambling services, black markets and exchanges. While initially a hotbed for black market and gambling activity, there is a rising trend in the use of Bitcoin for legitimate reasons (Tasca et al., 2018). In fact, studies such as Whelan (2013), Yermack (2013), Rogojanu and Badea (2014) and Molnár et al. (2015) have viewed Bitcoin as a possible investment asset, further enhancing its legitimacy as an alternative financial instrument of the future. However, as all “new” instruments are, Bitcoin prices surged from about US$700 in December 2016 to nearly US$20,000 in December 2017 before falling to its current price (as of December 2018) of about US$3,800. Critics attribute the rapid change in prices to bubbles akin to the Dotcom crash in the early 2000s leading studies such as Brandvold et al. (2015), Ciaian et al. (2016) and more recently Karalevicius et al. (2018) to attempt to shed light on how Bitcoin prices are determined. While their findings are robust, the authors acknowledge that limitations do exist including a lack of causality, missing information and unknown factors. In this paper, I argue that to keep in line with the current rising trend of using cryptocurrencies for legitimate financial purposes, it will benefit academics and practitioners alike if further study was conducted to examine the various uses for cryptocurrencies in mainstream financial activities. Specifically, this paper seeks to examine a few cryptocurrencies’ properties as alternative hedging instruments against foreign exchange (FX) risk.
Stavroyiannis (2018) in presenting Bitcoin as a currency, showed how the volatility of its prices consistently violated various measures of value-at-risk, subjecting banks invested in Bitcoin to greater capital requirements. In this instance, one would not be wrong in assuming that Bitcoin’s volatility brings adverse outcomes. However, studies examining FX risks have shown that return volatility may, in fact, be beneficial if used correctly. Menkhoff et al. (2012) first showed how volatile carry trade positions were able to compensate its investors for forex risks due to their negative co-movement. Dobrynskaya (2014) similarly showed how a global downside market factor i.e. a global bear run explains the volatility in carrying trade positions. As an extension to both studies, Cheong et al. (2017) showed that investors were compensated against global forex risks due to an active transfer of wealth between equity markets and foreign currency-denominated assets. Prior studies contend that gold serves as an adequate hedge against the volatility of stocks and bonds (Baur and Lucey, 2010; Beckmann et al., 2015) and foreign currency (Cheong, 2018) owing to its safe-haven properties and relatively stable prices. However, market accessibility to gold is highly restricted. Although there are no international laws governing gold, gold markets within countries are highly regulated by local authorities. Gold mining, similarly, falls under various jurisdictions of law including licensing, environment, health and safety, tax and royalty making it difficult for retail investors or international firms to access its said benefits. Bitcoin may present a viable alternative mode of hedging forex risks despite its volatility as it shares a number of similarities with gold. First, both derive value from scarcity and high extraction costs (i.e. mining). Second, both are used as a medium of exchange and not a national currency. The only differences here are gold has value-in-store while the value of Bitcoin is still undefined, and Bitcoin prices are exponentially more volatile than gold. As a result, Bitcoin may prove to be a more effective hedging instrument as compared to gold, assuming prior findings are true.
It should be said that the nature of Bitcoin and other cryptocurrencies – often deemed speculative (Dowd, 2014; Dale et al., 2005) – is not being contended here. Rather, this paper shows how investors and firms alike may effectively use cryptocurrencies as a tool for risk management despite its properties, especially during periods of market turmoil. Specifically, this is achieved through data visualizations of Bitcoin, Ethereum, Litecoin, Ripple and gold returns against global FX risk factors in the months surrounding key global events that occurred throughout the sample period. Then, 11 portfolios constructed through a combination of Bitcoin, Ethereum, Litecoin, Ripple, gold, and the four major currencies, namely, US Dollar (USD), British Pound (GBP), the euro (EUR) and the Japanese Yen (JPY) are constructed. Using the Fama and Macbeth (1973) two-pass procedure, global forex risk factors are regressed against the returns of each portfolio. The results show that the volatility of cryptocurrency returns moves in tandem with changes to global forex volatility throughout the sample period. Gold returns meanwhile remain flat throughout, true to its safe-haven nature. That is to say, cryptocurrency returns can compensate investors for their exposure to global forex risks through substantial abnormal returns (Menkhoff et al., 2012; Dobrynskaya, 2014). Gold, in contrast, serves to stabilize or flatten the volatilities of portfolios exposed to global forex risk factors. The results also show that gold returns are more sensitive toward global political and economic uncertainties, as investors’ trade-in (when markets are pessimistic) and out (optimistic) of gold in response to their sentiments on global events. Cryptocurrency, on the other hand, do not display such behavior possibly due to its intangible nature, making it unaffected by physical market conditions.
The contributions of this study are manifold. First, it provides further insight into the nature of cryptocurrencies. Specifically, it corrects the generally pessimistic public opinion toward cryptocurrency price volatility. Second, this study shows how cryptocurrencies such as Bitcoin, Ethereum, Litecoin and Ripple, far from being speculative instruments, can be accepted into mainstream finance as a FX risk management tool. Specifically, this study provides evidence to show how each of the cryptocurrency studied may be effectively used as a hedge against forex risk under different market conditions. Third, this study extends the work of Ciner et al. (2013) by examining the safe-haven properties of gold on a global scale. Specifically, the results show how gold may not necessarily be the ideal safe-haven owing to its sensitivity to global market uncertainties. Finally, this study provides practitioners with an efficient and cost-effective method of portfolio construction that may substantially lower their exposure to global forex risk factors.
The rest of this paper is as follows. Section 2 explains the methodology and the data. Section 3 provides the results. Section 4 discusses the findings. Section 5 concludes.
2. Data and methodology
11 portfolios are constructed:
a Bitcoin-only portfolio;
an Ethereum-only portfolio;
a Litecoin-only portfolio;
a Ripple-only portfolio;
a gold-only portfolio;
a portfolio of Bitcoin, Ethereum, Litecoin, and Ripple (CRPT);
a portfolio of USD, GBP, EUR, and JPY (MFX);
a portfolio of the four cryptocurrencies and four major currencies (MFX+CRPT);
a portfolio of the four major currencies and gold (MFX+G);
a portfolio of the four cryptocurrencies and gold (CRPT+G); and
a portfolio of the four cryptocurrencies, four major currencies, and gold (ALL).
Portfolios 6-11 are equally-weighted portfolios. Each portfolio assumes a simple buy-and-hold strategy. Cryptocurrency prices are from Coinmetrics.io, are expressed in US Dollars per unit of Bitcoin and converted to weekly returns. Weekly gold prices are obtained from the World Gold Council. The major currency portfolio comprises of four major currencies: USD, GBP, EUR and JPY, expressed in units of major currency per one unit of IMF special drawing rights (SDR). SDR exchange rates were obtained from the IMF archive. The data sample spans the period January 2012-December 2018.
2.2 Global foreign exchange volatility
The global forex risk factor in this study is global FX volatility. Global FX volatility is the absolute daily log return for each currency k on each day d in the sample (Menkhoff et al., 2012), which is then averaged over all currencies in the sample on any given day and average the daily values up to the weekly frequency. The measure of global FX volatility in week t is, thus, defined as:
The currencies of 37 countries are included to calculate VOL: Australia, Austria, Belgium, Brazil, Bulgaria, Canada, Croatia, Czech Republic, Denmark, Egypt, Finland, Hong Kong, Hungary, Iceland, Kuwait, Malaysia, Mexico, Netherlands, New Zealand, India, Ireland, Israel, Russia, Slovenia, Switzerland, Saudi Arabia, South Africa, Taiwan, Singapore, South Korea, Thailand, Slovakia, Sweden, Ukraine, Norway, the Philippines, and Poland; quoted as units of foreign currency per one unit of USD.
Besides VOL and DOL, coskewness is included in this study to draw additional inferences from the relationship between cryptocurrencies, gold and market volatility. Coskewness (Harvey and Siddique, 1999, 2000; Ang et al., 2006) is a measure of a portfolio’s ability to generate returns during periods of high market volatility. A high coskewness value implies that the portfolio is able to generate positive returns during volatile market conditions and vice versa.
Coskewness is calculated as:
2.4 Empirical model – Fama–Macbeth regression
The first step of the Fama and Macbeth (1973) two-pass procedure is to estimate a time-series regression to obtain in-sample betas for the 11 portfolios. Each portfolio’s return is regressed against VOL to determine how exposed it is to global FX volatility. In equation form, for the seven portfolios and the VOL factor, in the first step, the factor betas (β) are obtained by estimating seven regressions, each one on VOL factors. The equation is expressed as follows:
2.5 Descriptive statistics
Table I reports the descriptive statistics for returns of the 11 portfolio returns in this study. I also include the descriptive statistics for returns of the four major currencies for comparison. It is immediately apparent that average returns for all portfolios are positive, except in the case of Litecoin and MFX. We can also see that Ethereum recorded the highest average return and the highest returns dispersion, as compared to the other portfolios. Additionally, we can see that Bitcoin has the highest median return throughout the sample period. Ethereum also displayed the highest level of kurtosis and skewness, unsurprising given Ethereum prices continued to rise steadily even as Bitcoin prices began to fall in 2018. The first-order autocorrelation coefficient [AC (1)] for all portfolios is also far away from the value one indicating no autocorrelation in portfolio returns.
Table I also provides the coskewness of the 11 portfolios against US stock market returns (USMKT), Japanese stock market returns (JPNMKT), and Chinese stock market returns (CHNMKT). The coskewness values of the portfolios vary in magnitude and sign. The coskewness of all four cryptocurrencies against USMKT and JPNMKT is negative suggesting that they may not be a safe-haven asset for stock market participants. Their negative coskewness against USMKT and JPNMKT persists even when paired with gold (CRPT+G) or the major currencies (MFX+CRPT), reinforcing their hedge properties against FX but not equity risk. Interestingly, the cryptocurrencies recorded a positive coskew against CHNMKT, which is indicative of China’s overwhelmingly positive reception toward cryptocurrencies. Gold’s positive coskewness against USMKT complement studies that found gold to have low or even negative correlation with other assets (Reboredo, 2013; Ciner et al., 2013; Beckmann et al., 2015; Baur and Lucey, 2010). Finally, MFX’s negative coskewness against all equity markets indicate that holding a diversified portfolio of major currencies does not provide a “natural hedge” against FX volatility, contrary to the popular belief held by many textbooks on international finance (Madura, 2013; Eiteman et al., 2013) plotting cryptocurrency and gold returns over the sample period shows how cryptocurrency returns have risen exponentially since 2014 (Ethereum in particular) while gold returns have been on the decline ever since. During the same period, VOL has been particularly high, which is consistent with the earlier inference on coskewness. By 2014, VOL began to taper slightly. Similarly, gold returns began to decline at a greater rate as cryptocurrency returns continued their rise, albeit at a much slower rate. As VOL rose and peaked sometime during Q3 2017, we see that cryptocurrency and gold returns rose as well. The near-parallel movements observed in the cumulative returns in response to VOL movements indicate the strong hedging potential of cryptocurrencies against global FX volatility. That is to say, the returns earned from investing in cryptocurrencies are capable of compensating investors for the risks faced during a period of high market volatility. Unconditional correlation coefficients of Bitcoin, Ethereum, Litecoin and Ripple against VOL were 0.758, 0.793, 0.741 and 0.750, while gold against VOL was 0.221, respectively (Figure 1).
3. Main findings
3.1 A visual representation
This section provides several figures, which delivers further insights on the behavior of cryptocurrencies and gold returns during different periods of financial distress in the market. Figure 2 plots cryptocurrency and gold returns against VOL. The cryptocurrency and gold mean returns are divided into quartiles corresponding to the level of VOL. The first sub-sample consists of the 25 per cent periods that recorded the lowest VOL (“low”) while the last sub-sample consists of the 25 per cent periods that recorded the highest VOL (“high”). We see that Bitcoin returns rise as FX markets become more volatile. Ethereum meanwhile provides substantial returns during periods of moderate and high FX volatility. Ripple returns, in contrast, are positive only during periods of low FX volatility while Litecoin returns are positive only during periods of moderately high FX volatility (“3”). Gold returns remain relatively stable regardless of market volatility levels.
Throughout the sample period, several notable global events took place, namely, the Greek sovereign debt crisis, the Brexit referendum and the 2016 US Presidential Election. I plot cryptocurrency and gold returns against VOL during these periods to show how they behave in response to major political and economic events.
Panel A of Figure 3 plots euro, Bitcoin and gold returns against VOL during the height of the Greek sovereign debt crisis (January 2012 – June 2012). In the early stages of Bitcoin trading, Bitcoin returns were more sensitive toward market volatility. With limited trading volume in its initial years, minor changes in market volatility often resulted in large changes to Bitcoin returns (January 2012 – March 2012). As the Bitcoin market matured, returns began to stabilize. Gold returns were unsurprisingly stable throughout. The downward trend of Bitcoin returns in Panel B of Figure 3 further alludes to the sensitivity of Bitcoin returns in response to the market volatility during its early days.
Panel A of Figure 4 plots cryptocurrency and gold returns against VOL in the months leading up to, and the months after the Brexit referendum, which took place in June 2016. Cryptocurrency returns from January 2016 to August 2016 were mostly positive. Ethereum and to a lesser extent, ripple returns stand out, closely mimicking FX volatility movements throughout the sample period. Bitcoin returns remained mostly positive while Litecoin returns spiked early in the year and remained mostly flat throughout. Gold returns, in contrast, were more sensitive, peaking at about 20 per cent during the month of the Brexit referendum. Post-referendum, cryptocurrencies recorded low double-digit returns while gold returns fell. Ethereum returns, in particular, rose whenever FX volatility rose. Panel B corroborates these observations where Ethereum returns were at their highest when faced with moderate and high levels of FX volatility. Bitcoin returns, in contrast, are positive when faced with low to moderate levels of volatility. Litecoin and Ripple returns meanwhile are positive only during periods of low and moderate volatility, respectively. Gold returns remained relatively stable regardless of FX volatility levels.
Panel A of Figure 5 plots cryptocurrency and gold returns against VOL during the final months of the campaign period of the 2016 US Presidential Elections, and the first few months after the election results. Market volatility has remained relatively low but for two instances: mid-November 2016 and mid-January 2017 both of which coincide with the Presidential administration’s transition of power, and the Presidential Inauguration, respectively. At both points in time, gold returns were at their highest. Cryptocurrency returns meanwhile were highly sensitive to these US-based events, rising and falling in tandem with VOL. Panel B corroborates this argument as we can see cryptocurrency returns fluctuating at different levels of volatility. Bitcoin returns remain consistently positive at all levels of volatility while Ethereum returns were positive only at moderate volatility. Litecoin returns meanwhile were positive except when volatility was high while Ripple returns were positive when volatility was low. Gold returns were mostly negative and did not fluctuate with volatility.
Clear inferences can be made from these figures. First, because cryptocurrencies are non-physical assets, generally, they are less likely to be affected by changing physical market conditions, and thus, the movement of its prices do not seem to correlate with global economic and political uncertainty. They do, however, display sensitivity toward US economic and political uncertainties. Second, gold is still the go-to asset during uncertain times and finally, there is a slight substitution effect between cryptocurrencies and gold during periods of market and political turbulence.
3.2 Fama–Macbeth regression – dollar risk factor and global FX volatility
Table II reports results based on the Fama and Macbeth (1973) two-pass ordinary least squares (OLS) method. Specifically, the Fama and Macbeth regression estimates are reported using the euro, Yen, USD, GBP, Bitcoin, Ethereum, Litecoin, Ripple, Gold, CRPT, MFX, MFX+CRPT, MFX+G, CRPT+G and ALL as test assets. The factor included in the estimation is VOL. In the case of Bitcoin, Ethereum, Litecoin, Ripple and Gold, factor price estimates (λ) for VOL factors are negative, suggesting that these assets provide lower risk premia when its returns co-move positively with global FX volatility. The same negative factor price estimates are noted for other portfolios. More specifically, we see that the Ethereum-only portfolio records the largest negative factor price estimate. In contrast, the MFX portfolio records the smallest negative factor price estimate, implying that relative to the other possible portfolio combinations, a diversified, currency-only portfolio cannot hedge against FX market volatility. λ estimates for MFX+CRPT, MFX+G and ALL portfolios also support this notion. Judging by the magnitude of factor price estimates, one might infer that Ethereum is best at hedging against VOL, followed by Bitcoin, Ripple and Litecoin. As for the combined portfolios, the CRPT+G portfolio is best at hedging against VOL. It is also evident that the major currencies on their own cannot provide any protection from global FX volatility judging by the statistically insignificant and small coefficient magnitudes.
Table III presents the factor betas of the 11 portfolios used in this study. The βVOL estimates (from step one of Fama–Macbeth) for Bitcoin, Ethereum, Litecoin and Ripple suggests that cryptocurrency returns co-move positively with VOL. CRPT, MFX+CRPT, CRPT+G and ALL returns similarly co-move positively with VOL. Gold returns, in contrast, co-move negatively with VOL. MFX returns co-move positively with VOL but at a near-zero magnitude. From these factor-beta estimates, it seems that the cryptocurrencies on their own, provides the best hedge against global FX volatility, given their substantially larger factor betas.
In summary, the factor price and beta estimates provide strong evidence that the four cryptocurrencies can serve as a de facto hedge against exchange rate risks during volatile market conditions. Their difference lies in their sensitivity toward different markets. Bitcoin returns seem to be more stable in nature across markets and global FX volatility. Ethereum provide substantial returns during volatile times but is highly sensitive to US domestic market conditions. Litecoin and Ripple in contrast, are less effective than Bitcoin and Ethereum in hedging against global FX volatility. Gold returns meanwhile moves counter to VOL but remains stable against political and economic uncertainties. The estimates also suggest that combining the cryptocurrencies together or with other assets dilutes their individual hedging capabilities. The factor price and beta estimates for the MFX portfolio also suggests that a portfolio of major global currencies does not provide sufficient returns to negate the effects of exchange rate risks. Finally, the major currencies on their own do not exhibit any observable effects against global FX volatility.
Although the Bai-Perron test statistics do not indicate any structural breaks in the cryptocurrency returns throughout the sample period, the exponential rise and sudden fall in cryptocurrency prices, Bitcoin in particular, from mid-2017 to sometime during the fourth quarter of 2018 raises some concerns as to whether the cryptocurrencies can still serve as a hedge instrument even when its returns are volatile. To ensure robustness of the results, I repeat the earlier estimations with same test assets using the sample period from April 2017 to December 2018. This would give us a better understanding of the test assets’ hedging capabilities even when their prices are volatile.
Panel A of Figure 6 plots cryptocurrency and gold returns against VOL during the months when cryptocurrency returns were particularly volatile (April 2017 to December 2018). Global FX volatility during this period displayed similar trends to those observed earlier. Interestingly, despite their declining prices especially Bitcoin, all cryptocurrencies were able to generate positive weekly returns. Ripple in particular recorded substantially higher returns compared to the others. Gold returns meanwhile remained relatively flat during this period. Panel B corroborates these observations as we can see that despite falling prices, the cryptocurrencies were able to generate positive returns at all levels of global FX volatility, except for one instance where Bitcoin returns were negative when FX volatility was moderate. The returns distribution in Figure 6 is similar to those seen earlier i.e. Bitcoin returns are more consistently positive across all levels of FX volatility, Ethereum returns are substantially higher when FX volatility rises, and Litecoin returns are positive at low to moderate levels of FX volatility. Figure 6 also suggests that Ripple returns may serve as a substitute to the other cryptocurrencies when cryptocurrency prices are volatile.
Table IV reports results based on the Fama and Macbeth (1973) two-pass OLS method for the sample period April 2017 – December 2018. The test assets are as seen earlier. The factor included in the estimation is VOL. In the case of Bitcoin, Ethereum, Litecoin, Ripple and Gold, factor price estimates (λ) for VOL factors are negative, as was seen in Table II, which suggests that despite experiencing volatile prices, these assets continue to provide lower risk premia when its returns co-move positively with global FX volatility. The same negative factor price estimates are noted for other portfolios. In this sample period, however, we see that the CRPT+G portfolio records the largest negative factor price estimate while the MFX portfolio factor price estimate remains the smallest. λ estimates for MFX+CRPT, MFX+G and ALL are also qualitatively similar to the earlier results. Judging by the magnitude of factor price estimates, one might infer that Ripple is best at hedging against VOL, followed by Litecoin, Ethereum, and Bitcoin. As for the combined portfolios, the CRPT+G portfolio is best at hedging against VOL.
Table V presents the factor betas of the 11 portfolios from April 2017 – December 2018. The βVOL estimates are consistent with those factor price estimates in Table IV and qualitatively similar to those seen in Table III for all test assets corroborating the earlier findings i.e. cryptocurrency returns co-move positively with VOL while gold returns co-move negatively with VOL. However, as was seen in Table IV, the CRPT+G records the largest positive factor-beta suggesting that the inclusion of Gold into an equal-weighted portfolio of currencies improves the portfolio’s hedging capabilities.
In summary, the robustness test not only corroborates the earlier findings, it but also presents its own set of unique inferences. During periods where cryptocurrency prices are highly volatile, each of the four cryptocurrencies exhibits a form of substitution effect among one another judging by their returns distribution across different levels of FX volatility. Ripple particularly stands out in this regard. Also, although a portfolio of cryptocurrencies is still able to hedge against global FX volatility despite volatile cryptocurrency prices, including gold into this portfolio improves its hedging capabilities.
4. Discussion and implications
Instead of the oft-studied price behaviors of Bitcoin, this study builds on prior work by examining the ability of Bitcoin and other cryptocurrencies to hedge against global FX risks. Through the construction of several asset portfolios, this study finds cryptocurrency returns to be resilient against global FX volatility during periods of political and economic uncertainty and periods of volatile cryptocurrency prices. Gold returns meanwhile were found to be susceptible to geo-political dynamics despite its safe-haven nature. Nevertheless, under normal market conditions, the findings suggest that all four cryptocurrencies can provide a hedge against global FX risks. However, given the current uncertainties surrounding cryptocurrency prices and that the principle behind hedging is to minimize risk, the best portfolio combination would be an equal-weight of cryptocurrencies and gold (i.e. CRPT+G). Although the returns of the CRPT+G portfolio are not as substantial as the cryptocurrencies alone or together (i.e. CRPT), the CRPT+G portfolio exhibits returns dispersion properties that are more aligned to the principle of hedging. Textbook “natural hedges” in contrast, do not work as the compensatory effects its proponents claim was negligible.
Several implications arise from the findings of this study. First, cryptocurrency returns are resilient against global currency movements. Wijk (2013) argue that exchange rates exert significant influence over Bitcoin price movements. The findings show that this is not the case. Rather, it corroborates those of Ciaian et al. (2016) where macro-financial developments (i.e. exchange rate movements) have no impact on cryptocurrency returns. This may be attributed to the fact that cryptocurrencies (at least those considered here) are not issued by any government or central bank, effectively detaching it from the real economy. As a result, centralized policies have no effect on its price movements. Its detachment from monetary policy insulates it from the whims and fancies of policymakers and the subsequent market overreactions, making it an effective hedging instrument. Gold prices, in contrast, are bound by supply-demand forces besides policy restrictions imposed on the movement of the physical asset making gold returns more volatile especially during periods of political uncertainty.
Second, cryptocurrencies co-move positively with global FX volatility while gold provides a stabilizing effect. The time-series plots reveal the harmony of Bitcoin and gold returns during a period of economic and political uncertainty. Given their complementary nature, the equal-weighted cryptocurrency and gold portfolio provides investors with the best risk-return trade-off against global FX volatility unlike a cryptocurrency- or gold-only portfolio. Finally, while the returns from a single cryptocurrency and/or gold are substantial enough to insulate investors from global FX volatility, holding a cryptocurrency- or gold-only portfolio is a costly affair. One the last day of the sample period, one Bitcoin, was worth about US$3,900 while one ounce of gold was worth about US$1,280. This is not cost-effective for retail investors. They may, however, choose to include equal weights of cryptocurrency and/or gold into their current portfolio of foreign currency-denominated assets. The results show that this alone is enough to provide a hedge against global FX volatility.
There is no denying that cryptocurrencies will be a mainstay of our financial systems in the future. Detractors, however, maintain their pessimism over cryptocurrencies, alleging a lack of understanding to its nature, rife speculation in its pricing, and its lack of practical use beyond facilitating cashless transactions. This paper extends the budding literature on cryptocurrencies by shedding further light on the fundamentals of cryptocurrencies. Specifically, it refines our understanding of the nature of cryptocurrency volatility over time. Additionally, this paper also shows how cryptocurrencies use can be extended beyond a simple medium of exchange into an alternative method of hedging global FX risks. The results provide evidence that the cryptocurrency price volatility critics fear so much serves as an effective hedge against FX risks by compensating traders with substantial returns for the risks they are exposed to, more effective even when compared to gold. In fact, gold may not be as effective as commonly thought owing to its sensitivity to global economic and political uncertainties. Finally, this paper also showed that natural hedges through a diversified holding of currencies are ineffective, contrary to textbook descriptions. Owing to lower observation frequencies for other cryptocurrencies, this study was focused only on Bitcoin, Ethereum, Litecoin and Ripple, which may limit the generalizability of its findings as more and more cryptocurrencies are being developed over time. Future studies should look toward examining the properties of a wider range of cryptocurrencies and cryptocurrency-based derivatives and their uses in risk management.
|Panel A: asset returns|
|Panel B: portfolio returns|
The table reports mean and median returns, standard deviations, kurtosis and skewness of the euro, Japanese Yen, US Dollar, British Pound, Bitcoin, Ethereum, Litecoin, Ripple and Gold in Panel A. Panel B provides the same for the cryptocurrency-only (CRPT); major-currency-only (MFX); cryptocurrency + major currency (MFX+CRPT); major currency + gold (MFX+G); cryptocurrency + gold (CRPT+G); and cryptocurrency + major currency + gold (ALL) portfolios; All portfolios are equally-weighted; Sharpe ratios and the first-order autocorrelation coefficient [AC(1)] are also reported; Coskew(.) is the Harvey and Siddique (2000) measure of coskewness with respect to the excess return of the US stock market (USMKT), Japanese stock market (JPNMKT), and Chinese stock market (CHNMKT)
Cross-sectional asset pricing: factor prices
|Panel A: factor price – assets|
|Panel B: factor price – portfolios|
The table reports cross-sectional pricing results for the linear factor model based on volatility innovations (VOL); The test assets in Panel A are mean returns to the euro, Japanese Yen, US Dollar, British Pound, Bitcoin, Ethereum, Litecoin, Ripple, and Gold; Test assets in Panel B are the cryptocurrency-only (CRPT); major-currency-only (MFX); cryptocurrency + major currency (MFX+CRPT); major currency + gold (MFX+G); cryptocurrency + gold (CRPT+G); and cryptocurrency + major currency + gold (ALL) portfolios for the full sample period; The panels display coefficient estimates of factor risk prices, λ obtained by Fama and Macbeth (1973) cross-sectional regressions; Shanken (1992) adjusted (SH) and Newey and West (1987) standard errors with optimal lag selection (NW) are in parentheses; Bai and Perron (2003) f-statistics for structural breaks are also included;
***, **, and * indicates significance at the 1, 5 and 10% level
Cross-sectional asset pricing: Factor betas
|Panel A: factor-beta – assets|
|Panel B: factor-beta – portfolios|
The table reports cross-sectional pricing results for the linear factor model based on volatility innovations (VOL); In Panel A, the test assets are mean returns to the euro, Japanese Yen, US Dollar, British Pound, Bitcoin, Ethereum, Litecoin, Ripple, and Gold; Test assets in Panel B are the cryptocurrency-only (CRPT); major-currency-only (MFX); cryptocurrency + major currency (MFX+CRPT); major currency + gold (MFX+G); cryptocurrency + gold (CRPT+G); and cryptocurrency + major currency + gold (ALL) portfolios for the full sample period; The panels reports results for time-series regressions of excess returns on a constant (α) and volatility innovations (VOL); Newey-West HAC standard errors (with optimal lag selection) are reported in parentheses; Bai and Perron (2003) f-statistics for structural breaks are also included. The sample period is January 2012 to December 2018;
***, **, and * indicates significance at the 1, 5 and 10% level
Cross-sectional asset pricing: factor prices
|Panel A: factor price – assets|
|Panel B: factor price -portfolios|
The table reports cross-sectional pricing results for the linear factor model based on volatility innovations (VOL); The test assets in Panel A are mean returns to the euro, Japanese Yen, US Dollar, British Pound, Bitcoin, Ethereum, Litecoin, Ripple and Gold; Test assets in Panel B are the cryptocurrency-only (CRPT); major-currency-only (MFX); cryptocurrency + major currency (MFX+CRPT); major currency + gold (MFX+G); cryptocurrency + gold (CRPT+G); and cryptocurrency + major currency + gold (ALL) portfolios for the full sample period; The panels display coefficient estimates of factor risk prices, λ obtained by Fama and Macbeth (1973) cross-sectional regressions. Shanken (1992) adjusted (SH) and Newey and West (1987) standard errors with optimal lag selection (NW) are in parentheses. Bai and Perron (2003) f-statistics for structural breaks are also included.
***, **, and * indicates significance at the 1, 5, and 10% level. The sample period is April 2017 to December 2018
Cross-sectional asset pricing: factor betas
|Panel A: factor-beta – assets|
|Panel B: factor-beta – portfolios|
The table reports cross-sectional pricing results for the linear factor model based on volatility innovations (VOL); In Panel A, the test assets are mean returns to the euro, Japanese Yen, US Dollar, British Pound, Bitcoin, Ethereum, Litecoin, Ripple and Gold; Test assets in Panel B are the cryptocurrency-only (CRPT); major-currency-only (MFX); cryptocurrency + major currency (MFX+CRPT); major currency + gold (MFX+G); cryptocurrency + gold (CRPT+G); and cryptocurrency + major currency + gold (ALL) portfolios for the full sample period; The panels report results for time-series regressions of excess returns on a constant (α) and volatility innovations (VOL); Newey-West HAC standard errors (with optimal lag selection) are reported in parentheses; Bai and Perron (2003) f-statistics for structural breaks are also included
***, **, and * indicates significance at the 1, 5 and 10% level; The sample period is April 2017 to December 2018
The major currencies are expressed as per unit of SDR instead of US Dollar to avoid potential endogeneity and multicollinearity issues as Bitcoin and Gold are also expressed in US Dollars.
See Andersen et al. (2001) and Menkhoff et al. (2012) for a detailed discussion on this matter.
The Austrian Schilling, Belgian Franc, Finnish Markka and Dutch Guilder were in circulation until 2002; the Slovenian Tolar was in circulation until 2007; while the Slovak Koruna was in circulation until 2009; before being replaced by the Euro. These currencies were dropped and Equations 1 and 2 were recalculated when these countries officially adopted the Euro. The removal of these currencies did not alter the VOL and DOL values significantly.
These three markets were chosen as they are the three largest countries by Bitcoin trading volume globally (Bitcoin.com, 2016). The New York Stock Exchange, Nikkei 225, and Shanghai Composite Index’s daily returns data are proxies for US, Japanese and Chinese stock market returns.
Correlation coefficients were significant at the 1 per cent level. Results are not tabulated for brevity.
I include the Euro (and subsequently, USD, GBP and JPY) in the visualizations and analyses to disaggregate and observe the how each of the currencies react toward global FX volatility and the global events. Their reactions may very well differ from that of the equally-weighted MFX portfolio. I would like to thank the anonymous reviewer for pointing this out.
I only include Bitcoin in Figure 3 because Ethereum, Litecoin and Ripple were either non-existent or was not sufficiently traded to register public prices during this period.
I would like to thank the anonymous reviewer for highlighting this concern. The additional testing conducted served to solidify the earlier findings, making this a better paper than the one before.
Assuming an investor buys in at t and closes his position at t + 1.
Andrews, D.W.K. (1991), “Heteroskedasticity and autocorrelation consistent covariance matrix estimation”, Econometrica, Vol. 59 No. 3, pp. 817-858.
Ang, A., Chen, J. and Xing, Y. (2006), “Downside risk”, Review of Financial Studies, Vol. 19 No. 4, pp. 1191-1239.
Bai, J. and Perron, P. (2003), “Computation and analysis of multiple structural change models”, Journal of Applied Econometrics, Vol. 18 No. 1, pp. 1-22.
Baur, D. and Lucey, B.M. (2010), “Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold”, The Financial Review, Vol. 45 No. 2, pp. 217-229.
Beckmann, J., Berger, T. and Czudaj, R. (2015), “Does gold act as a hedge or a safe haven for stocks? A smooth transition approach”, Economic Modelling, Vol. 48, pp. 16-24.
Brandvold, M., Molnár, P., Vagstad, K. and Valstad, O.C.A. (2015), “Price discovery on bitcoin exchanges”, Journal of International Finance Markets, Institution and Money, Vol. 36, pp. 18-35.
Cheong, C.W.H. (2018), “The Islamic gold dinar: a hedge against exchange rate volatility”, Managerial Finance, Vol. 44 No. 6, pp. 722-738.
Cheong, C.W.H., Sinnkkannu, J. and Ramasamy, S. (2017), “On the predictability of carry trade returns: the case of the Chinese yuan”, Research in International Business and Finance, Vol. 39 No. A, pp. 358-376.
Ciaian, P., Rajcaniova, M. and d’Artis, K. (2016), “The economics of bitcoin price formation”, Applied Economics, Vol. 48 No. 19, pp. 1799-1815.
Ciner, C., Gurdgiev, C. and Lucey, B.M. (2013), “Hedges and safe havens: an examination of stocks, bonds, gold, oil, and exchange rates”, International Review of Financial Analysis, Vol. 29, pp. 202-211.
Dale, R.S., Johnson, J.E.V. and Tang, L.L. (2005), “Irrational behaviour during the South sea bubble”, The Economic History Review, Vol. 83, pp. 233-271.
Dobrynskaya, V. (2014), “Downside risk of carry trades”, Review of Finance, Vol. 18 No. 5, pp. 1885-1913.
Dowd, K. (2014), New Private Monies. A Bit-Part Player?, Institute of Economic Affairs, London.
Eiteman, D.K., Stonehill, A.I. and Moffett, M.H. (2013), Multinational Business Finance, 13th ed., Pearson, UK.
Fama, E.F. and Macbeth, J. (1973), “Risk, return and equilibrium: empirical tests”, Journal of Political Economy, Vol. 81 No. 3, pp. 607-636.
Harvey, C.R. and Siddique, A. (1999), “Autoregressive conditional skewness”, The Journal of Financial and Quantitative Analysis, Vol. 34 No. 4, pp. 465-477.
Harvey, C.R. and Siddique, A. (2000), “Conditional skewness in asset pricing tests”, The Journal of Finance, Vol. 55 No. 3, pp. 1263-1295.
Karalevicius, V., Degrande, N. and De Weerdt, J. (2018), “Using sentiment analysis to predict interday bitcoin price movements”, The Journal of Risk Finance, Vol. 19 No. 1, pp. 56-75.
Madura, J. (2013), International Financial Management, Stamford, Cengage.
Menkhoff, L., Sarno, L., Schmeling, M. and Schrimpf, A. (2012), “Carry trades and global foreign exchange volatility”, The Journal of Finance, Vol. 68 No. 2, pp. 681-718.
Molnár, P. Vagstad, K. and Valstad, O.C.A. (2015), “A bit risky? A comparison between bitcoin and other assets using an intraday value at risk approach”, Working Paper, available at: www.diva-portal.org/smash/get/diva2:742882/fulltext01.pdf (accessed May 2017).
Newey, W.K. and West, K.D. (1987), “A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix”, Econometrica, Vol. 55 No. 3, pp. 703-708.
Reboredo, J.C. (2013), “Is gold a safe haven or a hedge against the US dollar? Implications for risk management”, Journal of Banking and Finance, Vol. 37 No. 8, pp. 2665-2676.
Rogojanu, A. and Badea, L. (2014), “The issue of competing currencies. Case study –bitcoin”, Theoretical Applied Economics, Vol. 21 No. 1, pp. 103-114.
Shanken, J. (1992), “On the estimation of beta pricing models”, Review of Financial Studies, Vol. 5 No. 1, pp. 1-34.
Stavroyiannis, S. (2018), “Value-at-risk and related measures for the Bitcoin”, The Journal of Risk Finance, Vol. 19 No. 2, pp. 127-136.
Tasca, P., Hayes, A. and Liu, S. (2018), “The evolution of the Bitcoin economy: extracting and analysing the network of payment relationships”, The Journal of Risk Finance, Vol. 19 No. 2, pp. 94-126.
Whelan, K. (2013), “How is Bitcoin different from the dollar?” Forbes, available at: www.forbes.com/sites/karlwhelan/2013/11/19/how-is-Bitcoin-different-from-the-dollar/ (accessed May 2017).
Wijk, V.D. (2013), “What can be expected from the Bitcoin?”, Unpublished Thesis, Erasmus University Rotterdam, Netherlands.
Yermack, D. (2013), “Is Bitcoin a real currency? An economic appraisal”, National Bureau of Economic Research, Working Paper No. w19747, available at: www.nber.org/papers/w19747.pdf (accessed May 2017).
Bouoiyour, J. and Selmi, R. (2015), “What does Bitcoin look like?”, MPRA Paper No. 58091, University Library of Munich, Germany.
Bouri, E. Gil-Alana, L.A. Gupta, R. and Roubaud, D. (2016), “Modelling long memory volatility in the Bitcoin market: evidence of persistence and structural breaks. Department of economics”, University of Pretoria, Working Paper No. 2016-54, available at: www.up.ac.za/media/shared/61/WP/wp_2016_54.zp92472.pdf
Carhart, M.M. (1997), “On persistence in mutual fund performance”, The Journal of Finance, Vol. 52 No. 1, pp. 57-82.
Lustig, H., Roussanov, N. and Verdelhan, A. (2011), “Common risk factors in currency markets”, Review of Financial Studies, Vol. 24 No. 11, pp. 3731-3777.