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Cryptocurrencies: Hedging Opportunities From Domestic Perspectives in Southeast Asia Emerging Markets:

Cryptocurrencies: Hedging Opportunities From Domestic Perspectives in Southeast Asia Emerging... Previous studies have shown that cryptocurrencies could hedge equities. However, most of those studies did not take into account the recent cryptocurrencies bubbles in 2018 and domestic currencies. Therefore, this research aimed to study whether the hedge effectiveness of cryptocurrencies still exists. This research used five cryptocurrencies (bitcoin, ethereum, monero, ripple, and litecoin), equity indices (Indonesia, Malaysia, Vietnam, Thailand, and the Philippines), and iShares ETF MSCI World (developed world). Commodities-based hedging using iShares S&P GSCI Commodity-Indexed Trust was also analyzed as a comparison. The asymmetric generalized dynamic conditional correlation (AG-DCC) GARCH showed that one cryptocurrency could not significantly and consistently hedge equities while five equally weighted cryptocurrencies could marginally hedge equities. Meanwhile, the classical minimum variance model also showed that the hedge effectiveness of cryptocurrencies was insignificantly positive. Equity traders could add cryptocurrencies into portfolios when the purpose was to maximize the Sharpe ratio instead of hedging. Overall, commodities were the better hedge for Southeast Asia emerging markets. Keywords cryptocurrency, AG-DCC-GARCH, hedge ratio, mean-variance portfolio, Southeast Asia emerging markets 2019). However, most of those studies did not incorporate Introduction the cryptocurrency bubbles in 2018 and domestic currency. Since the work of Markowitz (1952), many researchers have In this research, there are at least six different geographi- focused on the advantages of diversification and many stud- cal portfolios that lead to cryptocurrencies’ effects on risk- ies have analyzed the optimal combination of assets that adjusted returns and hedging abilities. Therefore, there are could maximize returns and also minimize volatility. The three major purposes of this research: to analyze whether popularity of cryptocurrencies has begun to attract investors cryptocurrencies can increase the equity portfolio’s perfor- who wish to add cryptocurrencies into their portfolio. Few mance, to explore those effects on the domestic currency, studies have analyzed the correlation between cryptocurren- and to compare cryptocurrency-hedged portfolios with com- cies and other assets (Corbet et al., 2018). Also, few types of modity-hedged portfolios. research have concentrated on hedging and diversification This research fits in the category of cryptocurrency– capabilities (Baur et al., 2018; Bouri, Molnárb, et al., 2017; equity relationship (Bouri et al., 2019; Corbet et al., 2018; Brière et al., 2015; Dyhrberg, 2016a; Kajtazi & Moro, 2019; Kajtazi & Moro, 2019; Shahzad et al., 2019). Nevertheless, Robiyanto et al., 2019; Shahzad et al., 2019; Urquhart & this study broadens previous studies in three areas. First, this Zhang, 2019). Few studies have analyzed whether bitcoin has similar Universitas Diponegoro, Semarang, Indonesia financial properties with gold (Dyhrberg, 2016a; Klein et al., Perbanas Institute, South Jakarta, Indonesia 2018). Moreover, few academics have examined cryptocur- Satya Wacana Christian University, Salatiga, Indonesia rencies’ volatility (Charfeddine & Maouchi, 2019) and their Corresponding Author: connection with global instability (Bouri, Gupta, et al., 2017; Robiyanto Robiyanto, Department of Management, Faculty of Economics Demir et al., 2018; Fang et al., 2019). Besides, the profi- and Business, Satya Wacana Christian University, Jl. Diponegoro ciency of Bitcoin to minimize market risk has been pub- No. 52-60, Salatiga 50711, Indonesia. lished, regardless of the speculative nature (Bouri et al., Email: robiyanto@staff.uksw.edu Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 SAGE Open research used five cryptocurrencies and commodities as a Furthermore, Bitcoin is a hedge for equities (Dyhrberg, comparison. Second, this research explored the time vari- 2016b). Bitcoin is regarded to have some of the same hedging ability in diversification analyses of cryptocurrencies against abilities as gold and may be utilized to hedge market risk. major Southeast Asia emerging markets and developed mar- Meanwhile, Bitcoin can be regarded as a poor hedge for other kets as a comparison. Third, the performance analyses were non-digital assets such as gold and currencies (Shahzad et al., based on domestic currencies of Indonesia, Malaysia, 2019). Furthermore, Bitcoin can be utilized as a shield against Vietnam, the Philippines, Thailand, and developed markets. uncertainty such as implied volatilities (Bouri, Gupta, et al., The remainder of this research is organized as follows. 2017). Similarly, Demir et al. (2018) showed that uncertainty Section “Portfolio of Different Asset Classes” gives the in economic policy can be hedged by using Bitcoin. description of the portfolio of different asset classes. Section “Method” describes the methodology used in this research. Method Section “Empirical Results” explores the results. Section “Managerial Implications” is the managerial implications. The Datasets Finally, section “Conclusion” concludes the paper. This research utilized five cryptocurrencies (bitcoin, ethe- reum, litecoin, monero, and ripple), obtained from https:// Portfolio of Different Asset Classes coinmarketcap.com/ followed Bouri et al. (2019). The daily prices of equity indices (Indonesia, Malaysia, Vietnam, Generally, there are three types of financial classes: shares, Thailand, and the Philippines) were obtained from fixed income, and cash (Baur & Lucey, 2010; Robiyanto, Bloomberg. The equities indices were LQ45 (Indonesia), 2018). Shares have the ownership element and dividend pay- FTSE Malaysia (Malaysia), VN (Vietnam), SET (Thailand), ment that can change over time, while bonds are debt ele- and PSEI (the Philippines). The Singapore market was ments with an interest payment. Moreover, Corbet et al. excluded since Singapore is not considered as an emerging (2018) imply that Bitcoin and Ethereum are homogeneous market in Southeast Asia. Furthermore, this research also (digital) asset classes. Bitcoin and Ethereum are peer-to-peer used the return of iShare ETF MSCI World, as the represen- electronic cash systems that allow trading to be conducted tative of the developed world (Chunhachinda et al., 2018). without using financial intermediaries. Hence, unlike other Commodity-based hedging using iShares S&P GSCI financial asset classes, they have no physical forms and they Commodity-Indexed Trust was also analyzed as a compari- are divisible. The value of Bitcoin and Ethereum is based on son (Chunhachinda et al., 2018). Based on the data availabil- algorithm security. ity of the cryptocurrencies, the sample starts from August 8, Previous studies have shown that different asset classes, 2015, to July 2, 2019, consisting of 1,425 observations. such as gold and fixed income assets, might enhance or mini- Moreover, the sample included weekends since trading in mize portfolio performance and risk (Robiyanto et al., 2019; cryptocurrency is not constricted to business days. Following Shakil et al., 2018). Fixed income securities can be used to Baur et al. (2018), since equities are not traded on weekends, hedge portfolios or financial assets. Furthermore, they sug- this research assumed zero returns for weekends and holi- gested that firm bonds could be used as a safe-haven instru- days to align with cryptocurrencies. ment in Indonesia. Moreover, cryptocurrencies can provide Since the cryptocurrencies and commodities were in U.S. diversification advantages (Bouri et al., 2019; Brière et al., dollars while the equities were in domestic currencies, this 2015). study converted the return of cryptocurrencies to the domes- Brière et al. (2015) suggested that including Bitcoin in a tic currencies (Indonesia, Malaysia, Vietnam, Thailand, and portfolio can provide significant diversification benefits. the Philippines). Moreover, the returns of the assets were cal- Furthermore, they showed that a small percentage of Bitcoin culated with the following formula: ln (P /P ). t t–1 may greatly increase the risk-adjusted return of a portfolio. Meanwhile, Corbet et al. (2018) investigated the movement between cryptocurrencies and other assets and found that Asymmetric generalized dynamic conditional cryptocurrencies are fairly different from other economic correlation GARCH assets. Moreover, Baur et al. (2018) concluded that Bitcoin is not This study used asymmetric generalized dynamic condi- correlated with a non-digital asset in the crisis period. They tional correlation (AG-DCC) GARCH of Cappiello et al. also argued that Bitcoin is a speculative asset. However, (2006), which is an enhanced version of Engle (2002). This Dyhrberg (2016a), who used GARCH (generalized autore- process measures the co-movement of financial markets gressive conditional heteroskedasticity), methodologies con- with the dynamic covariance matrix. Hence, the method can cluded that Bitcoin has some similarities to gold implying obtain time-varying correlations across different asset that Bitcoin has hedging capabilities. Also, Baur et al. (2018), returns. Bollerslev (1990) suggested that the covariance who extended the research of Dyhrberg (2016a), showed that matrix at time t is: Bitcoin has very different returns, volatilities, and correla- tion characteristics than gold. VD = CD , (1) tt t Susilo et al. 3 where D is a diagonal matrix of time-varying GARCH vola- Sensitivity Test (Mean-Variance Process) tilities and C is a correlation matrix. Engle (2002) modified This research used the classical mean-variance framework to C into time-varying but not stochastic. Cappiello et al. (2006) calculate the minimum variance portfolio that consists of enhanced the DCC model to have an asymmetric correlation. equities and other assets. Following the following is the opti- The conditional correlation matrix is given by: mization procedure: 1 1 −− n n 2 2 Cd = iagQ QdiagQ , (2) () () () tt tt min. σ xx (8) ik , ik ∑∑ i== 11 k where diag(Q ) is the diagonal matrix that is created from diagonal elements of Q , and Q is a positive definite matrix t t Subject to: that follows the process: x = 1, QA =+ Ωε ′′ ′ εη AG + ′ η GB + ′QB, (3) i tt−− 11 tt−− 11 tt−1 i=1 where η is the vector obtained from ε , Ω is positive defi- nite, and A and B are diagonal matrices. rx = r , ii p In a multivariate GARCH model, the variables of the con- i=1 ditional variance and conditional covariance should be cal- culated simultaneously by maximizing the following log 01 ≤= xi ,, 2,,  n, likelihood: where r and x are the returns and portfolio proportions, i i −1 respectively. σ is the covariance and the asset weight is ln LH θε =− ln + ′H ε (4) () () ik , () tt tt always positive. t=1 where H is the conditional covariance matrix and ε is the t t Empirical Results GARCH error vector at time t. Furthermore, the following formula is used to measure Table 1 shows the descriptive statistics. All returns were hedge ratio showing the amount, whereby a long (buy) posi- in domestic currencies. The average Bitcoin returns in tion of equity should be accompanied by a short or long posi- Indonesia, Malaysia, Vietnam, the Philippines, and Thailand tion of in other assets: xy ,t were around 0.26% daily. Meanwhile, the mean of Ethereum returns was around 0.42% daily. This result shows that Covr , r () h Ethereum had higher average daily returns than Bitcoin. xt yt xy ,t (5) β = = . xy ,t Also, Ethereum had a higher risk compared with Bitcoin. varr () yy ,t yt This finding is similar to Bouri et al. (2019). From equities markets, it shows that Vietnam had the highest average daily Also, the conditional volatilities can be utilized to compute returns compared with other emerging markets in Southeast the portfolio weights that consist of equities and other assets Asia. The Malaysian capital market had the lowest risk with the following formula (Kroner & Ng, 1998): compared with other markets and this result is in line with Muharam et al. (2019). Table 1 also shows that the differ- hh − yy,, txyt w = , ences between domestic returns and original returns of xy,t hh −+ 2 h xx,, txyt yy,t cryptocurrencies based on their original prices (US$) were not significant. For instance, the mean return bitcoin in Indonesian converted-price was 0.2595 while the mean ww =≤ {, 00 if <> ww if 01 ≤ 11 if w , with (6) xy,, txyt xy,, txyt xy,t return bitcoin in the original price was 0.2564. Similar differ- ences prevailed in other nations indicating that the exchange where w is the proportion of asset x at time t. The weight xy ,t rate was not a major issue in this research. Ripple was the of asset y is calculated as 1− w . xy,t riskiest cryptocurrency in this research, while commodities Also, hedging effectiveness (HE) was computed by the were riskier than equity indices although commodities had following formula (Chang et al., 2011): lower mean returns than the equities during the study period. Table 2 shows the unconditional correlation. The Indonesian σσ − unhedgedhedged equity had the lowest and positive correlation with ethereum, (7) Hedging Effectiveness = , while Malaysian equity had the lowest and negative correla- unhedged tion with bitcoin. Meanwhile, Thailand’s equity had the lowest where σ is the portfolio variance of cryptocurrency and and positive correlation with litecoin, while Philippine equity hedged equity and σ is the portfolio variance of equity. had the lowest and negative correlation with bitcoin. Also, unhedged 4 SAGE Open Table 1. Descriptive Statistics. Variables M (%) Max (%) Min (%) SD (%) Skewness Obs. Bitcoin (Indonesia) 0.2595 22.72 −20.53 3.98 −0.19 1,425 Bitcoin (Malaysia) 0.2601 22.81 −20.65 3.98 −0.21 1,425 Bitcoin (Thailand) 0.2466 22.51 −20.96 3.95 −0.22 1,425 Bitcoin (Philippine) 0.2642 22.32 −20.30 3.96 −0.20 1,425 Bitcoin (Vietnam) 0.2609 22.51 −20.75 3.96 −0.21 1,425 Ethereum (Indonesia) 0.4163 41.27 −31.55 6.61 0.48 1,425 Ethereum (Malaysia) 0.4218 41.86 −31.55 6.62 0.50 1,425 Ethereum (Thailand) 0.4083 42.08 −31.55 6.60 0.49 1,425 Ethereum (Philippine) 0.4259 41.83 −31.55 6.60 0.49 1,425 Ethereum (Vietnam) 0.4226 41.26 −31.55 6.60 0.50 1,425 Litecoin (Indonesia) 0.2377 50.98 −39.29 5.70 1.24 1,425 Litecoin (Malaysia) 0.2383 51.07 −39.19 5.71 1.22 1,425 Litecoin (Thailand) 0.2383 51.07 −39.19 5.71 1.22 1,425 Litecoin (Philippine) 0.2383 51.07 −39.19 5.71 1.22 1,425 Litecoin (Vietnam) 0.2383 51.07 −39.19 5.71 1.22 1,425 Ripple (Indonesia) 0.2763 102.74 −61.60 7.21 3.04 1,425 Ripple (Malaysia) 0.2769 102.74 −61.58 7.22 3.04 1,425 Ripple (Thailand) 0.2634 102.74 −61.57 7.21 3.04 1,425 Ripple (Philippine) 0.2810 102.74 −61.60 7.21 3.07 1,425 Ripple (Vietnam) 0.2777 102.74 −61.72 7.22 3.04 1,425 Monero (Indonesia) 0.3368 58.46 −29.09 6.82 1.02 1,425 Monero (Malaysia) 0.3374 58.46 −29.00 6.81 1.00 1,425 Monero (Thailand) 0.3239 58.46 −29.53 6.80 1.01 1,425 Monero (Philippine) 0.3415 58.46 −28.86 6.80 1.02 1,425 Monero (Vietnam) 0.3382 58.46 −29.32 6.81 1.01 1,425 Commodities (Indonesia) −0.0052 4.78 −4.41 1.03 −0.11 1,425 Commodities (Malaysia) −0.0045 4.45 −5.47 1.01 −0.22 1,425 Commodities (Thailand) −0.0180 4.77 −4.93 0.99 −0.19 1,425 Commodities (Philippine) −0.0005 4.77 −4.83 0.98 −0.15 1,425 Commodities (Vietnam) −0.0038 4.60 −4.76 1.00 −0.14 1,425 Equity (Indonesia) 0.0163 6.01 −5.31 0.96 −0.24 1,425 Equity (Malaysia) 0.0003 2.22 −3.24 0.47 −0.59 1,425 Equity (Thailand) 0.0153 4.59 −4.73 0.60 −0.37 1,425 Equity (Philippine) 0.0050 3.58 −6.94 0.84 −0.34 1,425 Equity (Vietnam) 0.0327 3.78 −5.42 0.83 −0.78 1,425 Developed Market (US$) 0.0161 3.23 −5.75 0.72 −0.94 1,425 Bitcoin (US$) 0.2564 22.51 −20.75 3.96 −0.21 1,425 Ethereum (US$) 0.4182 41.23 −31.55 6.60 0.49 1,425 Litecoin (US$) 0.2346 51.03 −39.52 5.69 1.24 1,425 Ripple (US$) 0.2732 102.74 −61.63 7.22 3.04 1,425 Monero (US$) 0.3337 58.46 −29.32 6.81 1.01 1,425 Source. Bloomberg, processed. Vietnam equity had the lowest and positive correlation with Furthermore, bitcoin, ethereum, and ripple had the high- litecoin. Furthermore, Indonesia equity had the highest and est and positive correlation with litecoin. Also, commodities positive correlation with ripple, while Malaysia equity also had a very low correlation with cryptocurrencies. The ETF- had the highest and positive correlation with ripple. Meanwhile, based equity from developed nations also had a very low cor- Thailand’s equity had the lowest and positive correlation with relation with cryptocurrencies. Moreover, the low correlation monero, while Philippine equity had the highest and positive between cryptocurrencies, commodities, and equities may correlation with ripple. In addition, Vietnam equity had the provide limited hedging benefits. Nevertheless, the uncondi- highest and positive correlation with ethereum. The maximum tional correlation cannot be efficiently used since there are values of correlation between equities and cryptocurrencies dynamic volatilities and covariances of the returns. Therefore, were always below 0.10 which were very low. the AG-DCC GARCH was utilized. 5 Table 2. Unconditional Correlation. Countries and Instruments Indonesia Malaysia Thailand Philippines Vietnam Bitcoin Ethereum Ripple Litecoin Monero Commodities Developed Indonesia 1.000 0.395 0.309 0.371 0.178 0.017 0.015 0.058 0.020 0.022 0.093 0.180 Malaysia 1.000 0.309 0.425 0.257 −0.023 −0.016 0.031 −0.022 0.003 0.094 0.163 Thailand 1.000 0.274 0.229 0.041 0.053 0.054 0.039 0.078 0.182 0.289 Philippines 1.000 0.234 −0.012 0.023 0.054 0.017 0.019 0.070 0.083 Vietnam 1.000 0.018 0.041 0.036 0.010 0.029 0.110 0.201 bitcoin 1.000 0.428 0.328 0.622 0.514 0.014 0.027 Ethereum 1.000 0.284 0.415 0.404 −0.006 0.053 Ripple 1.000 0.377 0.312 0.028 0.046 Litecoin 1.000 0.450 0.021 0.061 Monero 1.000 0.035 0.056 Commodities 1.000 0.412 Developed 1.000 Source. Bloomberg, processed. 6 SAGE Open Table 3. AG-DCC GARCH Parameters. Parameters Equity Bitcoin Ethereum Litecoin Monero Ripple Commodities 5 EW crypto Indonesia ω 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 α 0.058 0.164 0.243 0.047 0.109 0.326 0.069 0.083 β 0.921 0.833 0.754 0.920 0.835 0.622 0.919 0.895 λ 0.004 −0.005 −0.001 −0.043 −0.031 −0.016 0.004 −0.009 α + β 0.979 0.997 0.996 0.967 0.944 0.948 0.988 0.978 Long-term volatility 0.160 1.986 4.176 0.871 1.093 1.422 0.219 0.712 Log likelihood 6,012 4,053 3,309 3,510 3,228 3,480 5,849 3,859 Malaysia ω 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 α 0.058 0.164 0.263 0.046 0.110 0.299 0.048 0.088 β 0.921 0.833 0.734 0.921 0.835 0.646 0.949 0.891 λ 0.004 −0.005 0.001 −0.043 −0.030 −0.016 −0.001 −0.009 α + β 0.979 0.997 0.997 0.967 0.945 0.945 0.998 0.979 Long-term volatility 0.146 1.990 4.660 0.872 1.095 1.352 0.325 0.612 Log likelihood 6,896 4,048 3,309 3,503 3,229 3,472 5,871 3,846 Philippines ω 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 α 0.058 0.156 0.263 0.049 0.110 0.247 0.003 0.088 β 0.921 0.841 0.734 0.923 0.832 0.690 0.924 0.889 λ 0.004 −0.004 0.001 −0.035 −0.031 −0.018 −0.004 −0.008 α + β 0.979 0.997 0.997 0.972 0.943 0.937 0.927 0.977 Long-term volatility 0.159 1.783 4.663 0.886 1.090 1.201 0.149 0.725 Log likelihood 6,110 4,055 3,313 3,514 3,233 3,473 5,879 3,862 Thailand ω 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 α 0.058 0.162 0.259 0.046 0.107 0.280 0.014 0.081 β 0.921 0.835 0.738 0.927 0.837 0.659 0.915 0.900 λ 0.004 −0.003 0.000 −0.035 −0.031 −0.016 −0.003 −0.009 α + β 0.979 0.997 0.997 0.973 0.944 0.940 0.929 0.981 Long-term volatility 0.146 1.860 4.619 0.883 1.091 1.282 0.158 0.736 Log likelihood 6,658 4,060 3,313 3,509 3,232 3,472 5,872 3,861 Vietnam ω 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 α 0.058 0.164 0.251 0.048 0.110 0.281 0.053 0.084 β 0.921 0.833 0.746 0.924 0.836 0.659 0.911 0.896 λ 0.004 −0.005 0.000 −0.034 −0.030 −0.016 0.006 −0.009 α + β 0.979 0.997 0.997 0.972 0.946 0.940 0.964 0.980 Long-term volatility 0.146 1.986 4.460 0.881 1.096 1.282 0.162 0.730 Log likelihood 6,244 4,063 3,314 3,513 3,233 3,471 5,897 3,864 Developed ω 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 α 0.058 0.164 0.258 0.049 0.109 0.280 0.056 0.084 β 0.921 0.833 0.739 0.923 0.836 0.660 0.928 0.897 λ 0.004 −0.005 0.000 −0.034 −0.030 −0.016 −0.005 −0.009 α + β 0.979 0.997 0.997 0.972 0.946 0.941 0.984 0.980 Long-term volatility 0.146 1.986 4.528 0.889 1.095 1.282 0.193 0.730 Log likelihood 6,478 4,066 3,312 3,514 3,233 3,472 5,864 3,865 Source. Bloomberg, processed. Table 3 shows the AG-DCC GARCH parameters. The α Except for litecoin, the α values in cryptocurrencies were values in equities and commodities were relatively low indi- quite large indicating that the cryptocurrencies were sensi- cating that the equities were not sensitive to market events. tive to market events. Interestingly, the five equally weighted Susilo et al. 7 1.000 1.000 1.000 0.800 0.800 0.800 0.600 0.600 0.600 0.400 0.400 0.400 0.200 0.200 0.200 0.000 0.000 0.000 bitcoin equity - indonesia bitcoin equity - malaysia bitcoin equity - philippine 1.000 1.000 1.000 0.800 0.800 0.800 0.600 0.600 0.600 0.400 0.400 0.400 0.200 0.200 0.200 0.000 - 0.000 commodies equity - indonesia commodies equity - malaysia commodies equity - philippine 1.000 1.000 1.000 0.800 0.800 0.800 0.600 0.600 0.600 0.400 0.400 0.400 0.200 0.200 0.200 0.000 0.000 0.000 5 EW cryptocurrency equity - indonesia 5 EW cryptocurrency equity - philippine 5 EW cryptocurrency equity - malaysia Figure 1. Time-varying of optimal weights. Source. Bloomberg, processed. cryptocurrencies had lower α compared with any single of bitcoin in the equity-cryptocurrency portfolio were less cryptocurrency except litecoin implying that the diversifica- than 0.10%. Also, the average weight of five equally tion benefits existed when combining five cryptocurrencies weighted cryptocurrencies in the equity-cryptocurrency into a portfolio. Moreover, β values in the equities were rela- portfolio was less than 0.10%. However, Figure 1 shows tively large indicating that volatilities took a long time to die that having five equally weighted cryptocurrencies in the out following turmoil in the market. Furthermore, since the equity-cryptocurrency portfolio could reduce the rebalanc- values were above 0.99, the α + β values of bitcoin and ethe- ing frequencies. Also, the average weight of commodities reum showed that the term forecasts from the model were in the equity-commodities portfolio was above 0.3%. relatively flat. Furthermore, ethereum had the highest long- Furthermore, Figure 2 and Table 4 show the time-varying term volatility compared with other cryptocurrencies mean- and summary of hedge ratios, respectively. When buying while commodities had higher long-term volatility than the one domestic currency of equity in Indonesia, it should be equities. It is worth noting that the five equally weighted accompanied by shorting (sell) 0.006 of bitcoin. So, buying cryptocurrencies had lower long-term volatility compared one domestic currency of equity in Malaysia should be with any single cryptocurrency implying that combining five accompanied by buying (long) 0.008 of bitcoin. The average cryptocurrencies into a portfolio could reduce risk. As previ- hedge ratio for Southeast Asia emerging markets was less ously mentioned, in a multivariate GARCH model, the vari- than 0.10 indicating that it was a cheap hedge. Moreover, ables of the conditional variance and conditional covariance buying one dollar of equity in the developed market should should be calculated simultaneously by maximizing the fol- be accompanied by buying (long) 0.003 cents of bitcoin. lowing log likelihood. The table also presents the maximum The average hedge ratio for developed markets was also log likelihood values of each variable. less than 0.10 indicating that it was also a cheap hedge. Figure 1 shows the time-varying optimal weights Meanwhile, the value of hedge ratios in commodities hedg- obtained from the AG-DCC GARCH model. To save space, ing were bigger than the cryptocurrencies hedge. For exam- the authors only present some of the optimal weights. The ple, the value of the hedge ratio for Indonesia/commodities graphs from Figure 1 could be used to generalize other port- portfolio was −0.025 indicating that buying one equity in folio weights in this study. For instance, the average weights Indonesia should be accompanied by buying 0.025 of 2015 2015 2015 2015 2015 2015 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2019 2019 2019 2019 2019 2019 2015 2015 2015 2015 2016 2016 2016 2016 2016 2016 2016 2016 2017 2017 2017 2017 2017 2017 2017 2017 2018 2018 2018 2018 2018 2018 2018 2018 2019 2019 2019 2019 2015 2015 2015 2015 2015 2015 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2019 2019 2019 2019 2019 8 SAGE Open Bitcoin 0.70 0.50 0.30 0.10 -0.10 -0.30 -0.50 -0.70 Indonesia Malaysia Philippine Thailand Vietnam Developed Ethereum 0.70 0.20 -0.30 -0.80 Indonesia Malaysia Philippine Thailand Vietnam Developed Litecoin 1.00 0.50 0.00 -0.50 -1.00 Indonesia Malaysia Philippine Thailand Vietnam Developed Monero 1.00 0.50 0.00 -0.50 -1.00 Indonesia Malaysia Philippine Thailand Vietnam Developed Figure 2. (continued) 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 Susilo et al. 9 Ripple 1.30 0.80 0.30 -0.20 -0.70 Indonesia Malaysia Philippine Thailand Vietnam Developed Commodies 1.00 0.50 0.00 -0.50 -1.00 Indonesia Malaysia Philippine Thailand Vietnam Developed Figure 2. Time-varying hedge ratio. Source. Bloomberg, processed. Table 4. Hedged Portfolio Performances. Hedge effectiveness Sharpe ratio Portfolio GARCH Minimum variance GARCH Minimum variance Indonesia/Bitcoin 0.038 0.056 0.376 0.502 Malaysia/Bitcoin −0.099 0.029 0.233 0.177 Philippine/Bitcoin 0.027 0.050 0.367 0.319 Thailand/Bitcoin 0.026 0.015 0.478 0.528 Vietnam/Bitcoin −0.048 0.037 0.537 0.809 Developed/Bitcoin −0.122 0.024 0.312 0.508 Indonesia/Ethereum 0.005 0.020 0.263 0.404 Malaysia/Ethereum 0.000 0.011 0.076 0.107 Philippine/Ethereum 0.000 0.011 0.155 0.194 Thailand/Ethereum 0.006 0.002 0.349 0.455 Vietnam/Ethereum 0.008 0.007 0.661 0.708 Developed/Ethereum 0.006 0.003 0.463 0.412 Indonesia/Litecoin −0.045 0.025 0.201 0.369 Malaysia/Litecoin 0.000 0.016 0.025 0.085 Philippine/Litecoin −0.219 0.018 0.433 0.176 Thailand/Litecoin −0.019 0.006 0.256 0.458 Vietnam/Litecoin −0.015 0.019 0.512 0.710 Developed/Litecoin −0.264 0.004 0.227 0.397 Indonesia/Ripple −0.096 0.007 0.291 0.317 Malaysia/Ripple 0.020 0.003 0.043 0.033 (continued) 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 10 SAGE Open Table 4. (continued) Hedge effectiveness Sharpe ratio Portfolio GARCH Minimum variance GARCH Minimum variance Philippine/Ripple −0.255 0.004 0.280 0.126 Thailand/Ripple −0.781 0.001 0.571 0.432 Vietnam/Ripple −0.011 0.007 0.568 0.670 Developed/Ripple −0.294 0.003 0.737 0.387 Indonesia/Monero 0.006 0.017 0.286 0.365 Malaysia/Monero 0.002 0.007 0.009 0.067 Philippine/Monero 0.010 0.011 0.139 0.171 Thailand/Monero 0.000 0.000 0.415 0.425 Vietnam/Monero 0.003 0.009 0.572 0.695 Developed/Monero 0.000 0.003 0.340 0.394 Indonesia/Commodities 0.357 0.456 −0.092 0.135 Malaysia/Commodities 0.019 0.825 −0.684 −0.003 Philippine/Commodities 0.260 0.396 0.161 0.053 Thailand/Commodities 0.072 0.182 −0.158 0.223 Vietnam/Commodities 0.308 0.353 −0.371 0.424 Developed/Commodities 0.294 0.528 0.375 0.234 Indonesia/5 equally weighted cryptocurrencies 0.049 0.038 0.133 0.475 Malaysia/5 equally weighted cryptocurrencies 0.033 0.015 0.048 0.221 Philippine/5 equally weighted cryptocurrencies 0.014 0.026 0.090 0.267 Thailand/5 equally weighted cryptocurrencies 0.001 0.005 0.280 0.487 Vietnam/5 equally weighted cryptocurrencies 0.015 0.023 0.504 0.784 Developed/5 equally weighted cryptocurrencies 0.007 0.009 0.356 0.458 Source. Bloomberg, processed. commodities futures contract. The commodities hedge was The values of hedge effectiveness in the equities/five equally more expensive than cryptocurrencies hedge for Southeast weighted cryptocurrencies portfolio were always positive. Asia emerging markets. Similar patterns could be found in Also, commodities had better abilities to hedge equities than the developed market/commodities portfolio. The cheap cryptocurrencies from GARCH and minimum variance per- hedge of cryptocurrencies was consistent with Figure 3 spectives. Table 4 also shows the Sharpe ratio calculation. which was the result of the time-varying correlation between The average Sharpe ratio from equities/cryptocurrencies cryptocurrencies and equities. Cryptocurrencies had a very portfolio was higher than commodities/cryptocurrencies low dynamic correlation with equities in Southeast Asia portfolio implying that adding more cryptocurrencies in the emerging markets and developed markets. Meanwhile, portfolio could improve risk-adjusted returns in the expense commodities had a higher dynamic correlation with equities of hedge effectiveness. than cryptocurrencies. Table 4 shows the performance of the hedged portfolio. Managerial Implications Over the sample period, the values of hedge effectiveness were higher in the equities/commodities combination than in The overall results show that the optimum weights crypto- equities/cryptocurrencies. Based on the GARCH method, currencies in the combined portfolio are recommended cryptocurrencies could not consistently hedge equities since between 1% and 10%. This result is in line with Brière et al. the values of hedge effectiveness were not always positive. (2015). For risk takers, adding more cryptocurrencies can Meanwhile, the values of hedge effectiveness were always lead to higher risk-adjusted returns. The results of this study positive in the minimum variance method. The reason for the also support the work of Dyhrberg (2016b). This research difference was largely due to GARCH’s ability to capture complements the results of previous studies. However, the time-varying volatility compared with the classical mini- findings from this study are also lined with Klein et al. mum variance portfolio. The hedge effectiveness values (2018) who suggested that Bitcoin is a very weak hedge. from the minimum variance model showed that cryptocur- Interestingly, the five equally weighted cryptocurrencies rencies were not good hedge since the values were signifi- had lower GARCH error or α implying that the diversifica- cantly low which were also consistent with the GARCH tion benefits existed when combining five cryptocurrencies method. More interestingly, five equally weighted crypto- into a portfolio. Also, the values of hedge effectiveness in currencies had better performance for hedging the equities. the equities/five equally weighted cryptocurrencies portfolio Susilo et al. 11 Bitcoin 0.40 0.20 0.00 -0.20 -0.40 -0.60 Indonesia Malaysia Philippine Thailand Vietnam Developed Ethereum 0.20 0.10 0.00 -0.10 -0.20 Indonesia Malaysia Philippine Thailand Vietnam Developed Litecoin 0.40 0.20 0.00 -0.20 -0.40 Indonesia Malaysia Philippine Thailand Vietnam Developed Monero 0.20 0.10 0.00 -0.10 -0.20 Indonesia Malaysia Philippine Thailand Vietnam Developed Figure 3. (continued) 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 12 SAGE Open Ripple 0.20 0.10 0.00 -0.10 -0.20 Indonesia Malaysia Philippine Thailand Vietnam Developed Commodies 1.00 0.50 0.00 -0.50 -1.00 Indonesia Malaysia Philippine Thailand Vietnam Developed 5 equally weighted of cryptocurrencies 0.30 0.20 0.10 0.00 -0.10 -0.20 -0.30 Indonesia Malaysia Philippine Thailand Vietnam Developed Figure 3. Time-varying correlation. Source. Bloomberg, processed. were always positive. Although commodities were the better instability is quite similar to that shown in gold. This finding hedge, they were more expensive too. Moreover, commodi- also implies that managers should not constrain their prefer- ties that can be used to hedge equities are consistent with the ences to the top two cryptocurrencies which are bitcoin and findings of Chunhachinda et al. (2018). ethereum but also other cryptocurrencies to gain better This result gives a practical implication for investors and diversification benefits. policymakers. The availability of cryptocurrencies as a low-cost hedge may attract some equity traders. However, Conclusion cryptocurrencies are still far riskier than equities or com- modities. Hence, investors should be cautious when using This study considered the perspective of domestic investors cryptocurrencies. Moreover, the dynamic ability in the when combining cryptocurrencies or commodities with equi- diversification benefits of cryptocurrencies suggests that ties in a portfolio. This study also investigated the evolution 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2018 2018 2018 2018 2018 2018 2018 2018 2019 2019 2019 Susilo et al. 13 of the conditional correlations between cryptocurrencies or Bollerslev, T. (1990). Modelling the coherence in short-run nomi- nal exchange rates: A multivariate generalized arch model. The commodities with equities and found very low fluctuations Review of Economics and Statistics, 72(3), 498–505. https:// of correlations between cryptocurrencies and equities. doi.org/10.2307/2109358 Moreover, this study observed that the fluctuations of corre- Bouri, E., Gupta, R., Tiwari, A. K., & Roubaud, D. (2017). Does lations between cryptocurrencies and equities were below Bitcoin hedge global uncertainty? Evidence from wavelet- commodities and equities. based quantile-in-quantile regressions. Finance Research The results of the AG-DCC GARCH model, the hedge Letters, 23, 87–95. http://doi.org/10.1016/j.frl.2017.02.009 ratios, and the hedge effectiveness show that only five Bouri, E., Lucey, B., & Roubaud, D. (2019). Cryptocurrencies and equally weighted cryptocurrencies could consistently but the downside risk in equity investments. Finance Research marginally reduce portfolio risk. However, they did not Letters, 33, Article 101211. https://doi.org/10.1016/j.frl.2019 reduce risk as much as when commodities and equities .06.009 were combined. Also, the classical model of the minimum Bouri, E., Molnárb, P., Azzic, G., Roubaudd, D., & Hagfors, L. I. (2017). On the hedge and safe haven properties of Bitcoin: Is variance portfolio more or less supported the findings of it really more than a diversifier? Finance Research Letters, 20, AG-DCC GARCH. 192–198. https://doi.org/10.1016/j.frl.2016.09.025 The findings of this research can be useful for portfolio Brière, M., Oosterlinck, K., & Szafarz, A. (2015). Virtual currency, managers assessing diversification opportunities besides tangible return: Portfolio diversification with bitcoin. Journal gold. From a hedge effectiveness perspective, an investment of Asset Management, 16(6), 365–373. https://doi.org/10.1057/ strategy focused on equities/commodities portfolio may jam.2015.5 lead to more risk reduction but when risk-adjusted portfolio Cappiello, L., Engle, R. F., & Sheppard, K. (2006). Asymmetric returns are considered, equities/cryptocurrencies give a bet- dynamics in the correlations of global equity and bond returns. ter investment performance. Journal of Financial Econometrics, 4(4), 537–572. https://doi. The limitation of this study is that the authors did not org/10.1093/jjfinec/nbl005 make a sub-sample analysis regarding the cryptocurrency Chang, C.-L., McAleer, M., & Tansuchat, R. (2011). Crude oil hedging strategies using dynamic multivariate GARCH. bubbles starting from the end of 2017 to 2018. For future Energy Economics, 33(5), 912–923. https://doi.org/10.1016/j. studies, it is recommended to consider the non-linear rela- eneco.2011.01.009 tionship between cryptocurrencies and equities when exam- Charfeddine, L., & Maouchi, Y. (2019). Are shocks on the returns ining hedge effectiveness. and volatility of cryptocurrencies really persistent? Finance Research Letters, 28, 423–430. https://doi.org/10.1016/j.frl Authors Contributions .2018.06.017 All the authors have made an equal contribution to this study. Chunhachinda, P., de Boyrie, M. E., & Pavlova, I. (2018). Measuring the hedging effectiveness of commodities. Finance Research Letters, 30, 201–207. https://doi.org/10.1016/j.frl.2018.09.012 Availability of Data and Materials Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. The datasets used and/or analyzed during the current study are (2018). Exploring the dynamic relationships between crypto- available from the corresponding author on reasonable request. currencies and other financial assets. Economics Letters, 165, 28–34. https://doi.org/10.1016/j.econlet.2018.01.004 Declaration of Conflicting Interests Demir, E., Gozgor, G., Lau, C. K. M., & Vigne, S. A. (2018). Does The author(s) declared no potential conflicts of interest with respect economic policy uncertainty predict the Bitcoin returns? An to the research, authorship, and/or publication of this article. empirical investigation. Finance Research Letters, 26, 145–149. https://doi.org/10.1016/j.frl.2018.01.005 Dyhrberg, A. H. (2016a). Bitcoin, gold and the dollar—A GARCH Funding volatility analysis. Finance Research Letters, 16, 85–92. The author(s) received no financial support for the research, author- https://doi.org/10.1016/j.frl.2015.10.008 ship, and/or publication of this article. Dyhrberg, A. H. (2016b). Hedging capabilities of bitcoin. Is it the virtual gold? Finance Research Letters, 16, 139–144. https:// ORCID iDs doi.org/10.1016/j.frl.2015.10.025 Engle, R. F. (2002). Dynamic conditional correlation: A simple class Didik Susilo https://orcid.org/0000-0003-1522-2389 of multivariate generalized autoregressive conditional hetero- Robiyanto Robiyanto https://orcid.org/0000-0003-3565-1594 skedasticity models. Journal of Business & Economic Statistics, 20(3), 339–350. http://doi.org/10.1198/073500102288618487 References Fang, L., Bouri, E., Gupta, R., & Roubaud, D. (2019). Does global Baur, D. G., Dimpfl, T., & Kuck, K. (2018). Bitcoin, gold and the economic uncertainty matter for the volatility and hedging US dollar—A replication and extension. 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Cryptocurrencies: Hedging Opportunities From Domestic Perspectives in Southeast Asia Emerging Markets:

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Abstract

Previous studies have shown that cryptocurrencies could hedge equities. However, most of those studies did not take into account the recent cryptocurrencies bubbles in 2018 and domestic currencies. Therefore, this research aimed to study whether the hedge effectiveness of cryptocurrencies still exists. This research used five cryptocurrencies (bitcoin, ethereum, monero, ripple, and litecoin), equity indices (Indonesia, Malaysia, Vietnam, Thailand, and the Philippines), and iShares ETF MSCI World (developed world). Commodities-based hedging using iShares S&P GSCI Commodity-Indexed Trust was also analyzed as a comparison. The asymmetric generalized dynamic conditional correlation (AG-DCC) GARCH showed that one cryptocurrency could not significantly and consistently hedge equities while five equally weighted cryptocurrencies could marginally hedge equities. Meanwhile, the classical minimum variance model also showed that the hedge effectiveness of cryptocurrencies was insignificantly positive. Equity traders could add cryptocurrencies into portfolios when the purpose was to maximize the Sharpe ratio instead of hedging. Overall, commodities were the better hedge for Southeast Asia emerging markets. Keywords cryptocurrency, AG-DCC-GARCH, hedge ratio, mean-variance portfolio, Southeast Asia emerging markets 2019). However, most of those studies did not incorporate Introduction the cryptocurrency bubbles in 2018 and domestic currency. Since the work of Markowitz (1952), many researchers have In this research, there are at least six different geographi- focused on the advantages of diversification and many stud- cal portfolios that lead to cryptocurrencies’ effects on risk- ies have analyzed the optimal combination of assets that adjusted returns and hedging abilities. Therefore, there are could maximize returns and also minimize volatility. The three major purposes of this research: to analyze whether popularity of cryptocurrencies has begun to attract investors cryptocurrencies can increase the equity portfolio’s perfor- who wish to add cryptocurrencies into their portfolio. Few mance, to explore those effects on the domestic currency, studies have analyzed the correlation between cryptocurren- and to compare cryptocurrency-hedged portfolios with com- cies and other assets (Corbet et al., 2018). Also, few types of modity-hedged portfolios. research have concentrated on hedging and diversification This research fits in the category of cryptocurrency– capabilities (Baur et al., 2018; Bouri, Molnárb, et al., 2017; equity relationship (Bouri et al., 2019; Corbet et al., 2018; Brière et al., 2015; Dyhrberg, 2016a; Kajtazi & Moro, 2019; Kajtazi & Moro, 2019; Shahzad et al., 2019). Nevertheless, Robiyanto et al., 2019; Shahzad et al., 2019; Urquhart & this study broadens previous studies in three areas. First, this Zhang, 2019). Few studies have analyzed whether bitcoin has similar Universitas Diponegoro, Semarang, Indonesia financial properties with gold (Dyhrberg, 2016a; Klein et al., Perbanas Institute, South Jakarta, Indonesia 2018). Moreover, few academics have examined cryptocur- Satya Wacana Christian University, Salatiga, Indonesia rencies’ volatility (Charfeddine & Maouchi, 2019) and their Corresponding Author: connection with global instability (Bouri, Gupta, et al., 2017; Robiyanto Robiyanto, Department of Management, Faculty of Economics Demir et al., 2018; Fang et al., 2019). Besides, the profi- and Business, Satya Wacana Christian University, Jl. Diponegoro ciency of Bitcoin to minimize market risk has been pub- No. 52-60, Salatiga 50711, Indonesia. lished, regardless of the speculative nature (Bouri et al., Email: robiyanto@staff.uksw.edu Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 SAGE Open research used five cryptocurrencies and commodities as a Furthermore, Bitcoin is a hedge for equities (Dyhrberg, comparison. Second, this research explored the time vari- 2016b). Bitcoin is regarded to have some of the same hedging ability in diversification analyses of cryptocurrencies against abilities as gold and may be utilized to hedge market risk. major Southeast Asia emerging markets and developed mar- Meanwhile, Bitcoin can be regarded as a poor hedge for other kets as a comparison. Third, the performance analyses were non-digital assets such as gold and currencies (Shahzad et al., based on domestic currencies of Indonesia, Malaysia, 2019). Furthermore, Bitcoin can be utilized as a shield against Vietnam, the Philippines, Thailand, and developed markets. uncertainty such as implied volatilities (Bouri, Gupta, et al., The remainder of this research is organized as follows. 2017). Similarly, Demir et al. (2018) showed that uncertainty Section “Portfolio of Different Asset Classes” gives the in economic policy can be hedged by using Bitcoin. description of the portfolio of different asset classes. Section “Method” describes the methodology used in this research. Method Section “Empirical Results” explores the results. Section “Managerial Implications” is the managerial implications. The Datasets Finally, section “Conclusion” concludes the paper. This research utilized five cryptocurrencies (bitcoin, ethe- reum, litecoin, monero, and ripple), obtained from https:// Portfolio of Different Asset Classes coinmarketcap.com/ followed Bouri et al. (2019). The daily prices of equity indices (Indonesia, Malaysia, Vietnam, Generally, there are three types of financial classes: shares, Thailand, and the Philippines) were obtained from fixed income, and cash (Baur & Lucey, 2010; Robiyanto, Bloomberg. The equities indices were LQ45 (Indonesia), 2018). Shares have the ownership element and dividend pay- FTSE Malaysia (Malaysia), VN (Vietnam), SET (Thailand), ment that can change over time, while bonds are debt ele- and PSEI (the Philippines). The Singapore market was ments with an interest payment. Moreover, Corbet et al. excluded since Singapore is not considered as an emerging (2018) imply that Bitcoin and Ethereum are homogeneous market in Southeast Asia. Furthermore, this research also (digital) asset classes. Bitcoin and Ethereum are peer-to-peer used the return of iShare ETF MSCI World, as the represen- electronic cash systems that allow trading to be conducted tative of the developed world (Chunhachinda et al., 2018). without using financial intermediaries. Hence, unlike other Commodity-based hedging using iShares S&P GSCI financial asset classes, they have no physical forms and they Commodity-Indexed Trust was also analyzed as a compari- are divisible. The value of Bitcoin and Ethereum is based on son (Chunhachinda et al., 2018). Based on the data availabil- algorithm security. ity of the cryptocurrencies, the sample starts from August 8, Previous studies have shown that different asset classes, 2015, to July 2, 2019, consisting of 1,425 observations. such as gold and fixed income assets, might enhance or mini- Moreover, the sample included weekends since trading in mize portfolio performance and risk (Robiyanto et al., 2019; cryptocurrency is not constricted to business days. Following Shakil et al., 2018). Fixed income securities can be used to Baur et al. (2018), since equities are not traded on weekends, hedge portfolios or financial assets. Furthermore, they sug- this research assumed zero returns for weekends and holi- gested that firm bonds could be used as a safe-haven instru- days to align with cryptocurrencies. ment in Indonesia. Moreover, cryptocurrencies can provide Since the cryptocurrencies and commodities were in U.S. diversification advantages (Bouri et al., 2019; Brière et al., dollars while the equities were in domestic currencies, this 2015). study converted the return of cryptocurrencies to the domes- Brière et al. (2015) suggested that including Bitcoin in a tic currencies (Indonesia, Malaysia, Vietnam, Thailand, and portfolio can provide significant diversification benefits. the Philippines). Moreover, the returns of the assets were cal- Furthermore, they showed that a small percentage of Bitcoin culated with the following formula: ln (P /P ). t t–1 may greatly increase the risk-adjusted return of a portfolio. Meanwhile, Corbet et al. (2018) investigated the movement between cryptocurrencies and other assets and found that Asymmetric generalized dynamic conditional cryptocurrencies are fairly different from other economic correlation GARCH assets. Moreover, Baur et al. (2018) concluded that Bitcoin is not This study used asymmetric generalized dynamic condi- correlated with a non-digital asset in the crisis period. They tional correlation (AG-DCC) GARCH of Cappiello et al. also argued that Bitcoin is a speculative asset. However, (2006), which is an enhanced version of Engle (2002). This Dyhrberg (2016a), who used GARCH (generalized autore- process measures the co-movement of financial markets gressive conditional heteroskedasticity), methodologies con- with the dynamic covariance matrix. Hence, the method can cluded that Bitcoin has some similarities to gold implying obtain time-varying correlations across different asset that Bitcoin has hedging capabilities. Also, Baur et al. (2018), returns. Bollerslev (1990) suggested that the covariance who extended the research of Dyhrberg (2016a), showed that matrix at time t is: Bitcoin has very different returns, volatilities, and correla- tion characteristics than gold. VD = CD , (1) tt t Susilo et al. 3 where D is a diagonal matrix of time-varying GARCH vola- Sensitivity Test (Mean-Variance Process) tilities and C is a correlation matrix. Engle (2002) modified This research used the classical mean-variance framework to C into time-varying but not stochastic. Cappiello et al. (2006) calculate the minimum variance portfolio that consists of enhanced the DCC model to have an asymmetric correlation. equities and other assets. Following the following is the opti- The conditional correlation matrix is given by: mization procedure: 1 1 −− n n 2 2 Cd = iagQ QdiagQ , (2) () () () tt tt min. σ xx (8) ik , ik ∑∑ i== 11 k where diag(Q ) is the diagonal matrix that is created from diagonal elements of Q , and Q is a positive definite matrix t t Subject to: that follows the process: x = 1, QA =+ Ωε ′′ ′ εη AG + ′ η GB + ′QB, (3) i tt−− 11 tt−− 11 tt−1 i=1 where η is the vector obtained from ε , Ω is positive defi- nite, and A and B are diagonal matrices. rx = r , ii p In a multivariate GARCH model, the variables of the con- i=1 ditional variance and conditional covariance should be cal- culated simultaneously by maximizing the following log 01 ≤= xi ,, 2,,  n, likelihood: where r and x are the returns and portfolio proportions, i i −1 respectively. σ is the covariance and the asset weight is ln LH θε =− ln + ′H ε (4) () () ik , () tt tt always positive. t=1 where H is the conditional covariance matrix and ε is the t t Empirical Results GARCH error vector at time t. Furthermore, the following formula is used to measure Table 1 shows the descriptive statistics. All returns were hedge ratio showing the amount, whereby a long (buy) posi- in domestic currencies. The average Bitcoin returns in tion of equity should be accompanied by a short or long posi- Indonesia, Malaysia, Vietnam, the Philippines, and Thailand tion of in other assets: xy ,t were around 0.26% daily. Meanwhile, the mean of Ethereum returns was around 0.42% daily. This result shows that Covr , r () h Ethereum had higher average daily returns than Bitcoin. xt yt xy ,t (5) β = = . xy ,t Also, Ethereum had a higher risk compared with Bitcoin. varr () yy ,t yt This finding is similar to Bouri et al. (2019). From equities markets, it shows that Vietnam had the highest average daily Also, the conditional volatilities can be utilized to compute returns compared with other emerging markets in Southeast the portfolio weights that consist of equities and other assets Asia. The Malaysian capital market had the lowest risk with the following formula (Kroner & Ng, 1998): compared with other markets and this result is in line with Muharam et al. (2019). Table 1 also shows that the differ- hh − yy,, txyt w = , ences between domestic returns and original returns of xy,t hh −+ 2 h xx,, txyt yy,t cryptocurrencies based on their original prices (US$) were not significant. For instance, the mean return bitcoin in Indonesian converted-price was 0.2595 while the mean ww =≤ {, 00 if <> ww if 01 ≤ 11 if w , with (6) xy,, txyt xy,, txyt xy,t return bitcoin in the original price was 0.2564. Similar differ- ences prevailed in other nations indicating that the exchange where w is the proportion of asset x at time t. The weight xy ,t rate was not a major issue in this research. Ripple was the of asset y is calculated as 1− w . xy,t riskiest cryptocurrency in this research, while commodities Also, hedging effectiveness (HE) was computed by the were riskier than equity indices although commodities had following formula (Chang et al., 2011): lower mean returns than the equities during the study period. Table 2 shows the unconditional correlation. The Indonesian σσ − unhedgedhedged equity had the lowest and positive correlation with ethereum, (7) Hedging Effectiveness = , while Malaysian equity had the lowest and negative correla- unhedged tion with bitcoin. Meanwhile, Thailand’s equity had the lowest where σ is the portfolio variance of cryptocurrency and and positive correlation with litecoin, while Philippine equity hedged equity and σ is the portfolio variance of equity. had the lowest and negative correlation with bitcoin. Also, unhedged 4 SAGE Open Table 1. Descriptive Statistics. Variables M (%) Max (%) Min (%) SD (%) Skewness Obs. Bitcoin (Indonesia) 0.2595 22.72 −20.53 3.98 −0.19 1,425 Bitcoin (Malaysia) 0.2601 22.81 −20.65 3.98 −0.21 1,425 Bitcoin (Thailand) 0.2466 22.51 −20.96 3.95 −0.22 1,425 Bitcoin (Philippine) 0.2642 22.32 −20.30 3.96 −0.20 1,425 Bitcoin (Vietnam) 0.2609 22.51 −20.75 3.96 −0.21 1,425 Ethereum (Indonesia) 0.4163 41.27 −31.55 6.61 0.48 1,425 Ethereum (Malaysia) 0.4218 41.86 −31.55 6.62 0.50 1,425 Ethereum (Thailand) 0.4083 42.08 −31.55 6.60 0.49 1,425 Ethereum (Philippine) 0.4259 41.83 −31.55 6.60 0.49 1,425 Ethereum (Vietnam) 0.4226 41.26 −31.55 6.60 0.50 1,425 Litecoin (Indonesia) 0.2377 50.98 −39.29 5.70 1.24 1,425 Litecoin (Malaysia) 0.2383 51.07 −39.19 5.71 1.22 1,425 Litecoin (Thailand) 0.2383 51.07 −39.19 5.71 1.22 1,425 Litecoin (Philippine) 0.2383 51.07 −39.19 5.71 1.22 1,425 Litecoin (Vietnam) 0.2383 51.07 −39.19 5.71 1.22 1,425 Ripple (Indonesia) 0.2763 102.74 −61.60 7.21 3.04 1,425 Ripple (Malaysia) 0.2769 102.74 −61.58 7.22 3.04 1,425 Ripple (Thailand) 0.2634 102.74 −61.57 7.21 3.04 1,425 Ripple (Philippine) 0.2810 102.74 −61.60 7.21 3.07 1,425 Ripple (Vietnam) 0.2777 102.74 −61.72 7.22 3.04 1,425 Monero (Indonesia) 0.3368 58.46 −29.09 6.82 1.02 1,425 Monero (Malaysia) 0.3374 58.46 −29.00 6.81 1.00 1,425 Monero (Thailand) 0.3239 58.46 −29.53 6.80 1.01 1,425 Monero (Philippine) 0.3415 58.46 −28.86 6.80 1.02 1,425 Monero (Vietnam) 0.3382 58.46 −29.32 6.81 1.01 1,425 Commodities (Indonesia) −0.0052 4.78 −4.41 1.03 −0.11 1,425 Commodities (Malaysia) −0.0045 4.45 −5.47 1.01 −0.22 1,425 Commodities (Thailand) −0.0180 4.77 −4.93 0.99 −0.19 1,425 Commodities (Philippine) −0.0005 4.77 −4.83 0.98 −0.15 1,425 Commodities (Vietnam) −0.0038 4.60 −4.76 1.00 −0.14 1,425 Equity (Indonesia) 0.0163 6.01 −5.31 0.96 −0.24 1,425 Equity (Malaysia) 0.0003 2.22 −3.24 0.47 −0.59 1,425 Equity (Thailand) 0.0153 4.59 −4.73 0.60 −0.37 1,425 Equity (Philippine) 0.0050 3.58 −6.94 0.84 −0.34 1,425 Equity (Vietnam) 0.0327 3.78 −5.42 0.83 −0.78 1,425 Developed Market (US$) 0.0161 3.23 −5.75 0.72 −0.94 1,425 Bitcoin (US$) 0.2564 22.51 −20.75 3.96 −0.21 1,425 Ethereum (US$) 0.4182 41.23 −31.55 6.60 0.49 1,425 Litecoin (US$) 0.2346 51.03 −39.52 5.69 1.24 1,425 Ripple (US$) 0.2732 102.74 −61.63 7.22 3.04 1,425 Monero (US$) 0.3337 58.46 −29.32 6.81 1.01 1,425 Source. Bloomberg, processed. Vietnam equity had the lowest and positive correlation with Furthermore, bitcoin, ethereum, and ripple had the high- litecoin. Furthermore, Indonesia equity had the highest and est and positive correlation with litecoin. Also, commodities positive correlation with ripple, while Malaysia equity also had a very low correlation with cryptocurrencies. The ETF- had the highest and positive correlation with ripple. Meanwhile, based equity from developed nations also had a very low cor- Thailand’s equity had the lowest and positive correlation with relation with cryptocurrencies. Moreover, the low correlation monero, while Philippine equity had the highest and positive between cryptocurrencies, commodities, and equities may correlation with ripple. In addition, Vietnam equity had the provide limited hedging benefits. Nevertheless, the uncondi- highest and positive correlation with ethereum. The maximum tional correlation cannot be efficiently used since there are values of correlation between equities and cryptocurrencies dynamic volatilities and covariances of the returns. Therefore, were always below 0.10 which were very low. the AG-DCC GARCH was utilized. 5 Table 2. Unconditional Correlation. Countries and Instruments Indonesia Malaysia Thailand Philippines Vietnam Bitcoin Ethereum Ripple Litecoin Monero Commodities Developed Indonesia 1.000 0.395 0.309 0.371 0.178 0.017 0.015 0.058 0.020 0.022 0.093 0.180 Malaysia 1.000 0.309 0.425 0.257 −0.023 −0.016 0.031 −0.022 0.003 0.094 0.163 Thailand 1.000 0.274 0.229 0.041 0.053 0.054 0.039 0.078 0.182 0.289 Philippines 1.000 0.234 −0.012 0.023 0.054 0.017 0.019 0.070 0.083 Vietnam 1.000 0.018 0.041 0.036 0.010 0.029 0.110 0.201 bitcoin 1.000 0.428 0.328 0.622 0.514 0.014 0.027 Ethereum 1.000 0.284 0.415 0.404 −0.006 0.053 Ripple 1.000 0.377 0.312 0.028 0.046 Litecoin 1.000 0.450 0.021 0.061 Monero 1.000 0.035 0.056 Commodities 1.000 0.412 Developed 1.000 Source. Bloomberg, processed. 6 SAGE Open Table 3. AG-DCC GARCH Parameters. Parameters Equity Bitcoin Ethereum Litecoin Monero Ripple Commodities 5 EW crypto Indonesia ω 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 α 0.058 0.164 0.243 0.047 0.109 0.326 0.069 0.083 β 0.921 0.833 0.754 0.920 0.835 0.622 0.919 0.895 λ 0.004 −0.005 −0.001 −0.043 −0.031 −0.016 0.004 −0.009 α + β 0.979 0.997 0.996 0.967 0.944 0.948 0.988 0.978 Long-term volatility 0.160 1.986 4.176 0.871 1.093 1.422 0.219 0.712 Log likelihood 6,012 4,053 3,309 3,510 3,228 3,480 5,849 3,859 Malaysia ω 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 α 0.058 0.164 0.263 0.046 0.110 0.299 0.048 0.088 β 0.921 0.833 0.734 0.921 0.835 0.646 0.949 0.891 λ 0.004 −0.005 0.001 −0.043 −0.030 −0.016 −0.001 −0.009 α + β 0.979 0.997 0.997 0.967 0.945 0.945 0.998 0.979 Long-term volatility 0.146 1.990 4.660 0.872 1.095 1.352 0.325 0.612 Log likelihood 6,896 4,048 3,309 3,503 3,229 3,472 5,871 3,846 Philippines ω 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 α 0.058 0.156 0.263 0.049 0.110 0.247 0.003 0.088 β 0.921 0.841 0.734 0.923 0.832 0.690 0.924 0.889 λ 0.004 −0.004 0.001 −0.035 −0.031 −0.018 −0.004 −0.008 α + β 0.979 0.997 0.997 0.972 0.943 0.937 0.927 0.977 Long-term volatility 0.159 1.783 4.663 0.886 1.090 1.201 0.149 0.725 Log likelihood 6,110 4,055 3,313 3,514 3,233 3,473 5,879 3,862 Thailand ω 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 α 0.058 0.162 0.259 0.046 0.107 0.280 0.014 0.081 β 0.921 0.835 0.738 0.927 0.837 0.659 0.915 0.900 λ 0.004 −0.003 0.000 −0.035 −0.031 −0.016 −0.003 −0.009 α + β 0.979 0.997 0.997 0.973 0.944 0.940 0.929 0.981 Long-term volatility 0.146 1.860 4.619 0.883 1.091 1.282 0.158 0.736 Log likelihood 6,658 4,060 3,313 3,509 3,232 3,472 5,872 3,861 Vietnam ω 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 α 0.058 0.164 0.251 0.048 0.110 0.281 0.053 0.084 β 0.921 0.833 0.746 0.924 0.836 0.659 0.911 0.896 λ 0.004 −0.005 0.000 −0.034 −0.030 −0.016 0.006 −0.009 α + β 0.979 0.997 0.997 0.972 0.946 0.940 0.964 0.980 Long-term volatility 0.146 1.986 4.460 0.881 1.096 1.282 0.162 0.730 Log likelihood 6,244 4,063 3,314 3,513 3,233 3,471 5,897 3,864 Developed ω 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 α 0.058 0.164 0.258 0.049 0.109 0.280 0.056 0.084 β 0.921 0.833 0.739 0.923 0.836 0.660 0.928 0.897 λ 0.004 −0.005 0.000 −0.034 −0.030 −0.016 −0.005 −0.009 α + β 0.979 0.997 0.997 0.972 0.946 0.941 0.984 0.980 Long-term volatility 0.146 1.986 4.528 0.889 1.095 1.282 0.193 0.730 Log likelihood 6,478 4,066 3,312 3,514 3,233 3,472 5,864 3,865 Source. Bloomberg, processed. Table 3 shows the AG-DCC GARCH parameters. The α Except for litecoin, the α values in cryptocurrencies were values in equities and commodities were relatively low indi- quite large indicating that the cryptocurrencies were sensi- cating that the equities were not sensitive to market events. tive to market events. Interestingly, the five equally weighted Susilo et al. 7 1.000 1.000 1.000 0.800 0.800 0.800 0.600 0.600 0.600 0.400 0.400 0.400 0.200 0.200 0.200 0.000 0.000 0.000 bitcoin equity - indonesia bitcoin equity - malaysia bitcoin equity - philippine 1.000 1.000 1.000 0.800 0.800 0.800 0.600 0.600 0.600 0.400 0.400 0.400 0.200 0.200 0.200 0.000 - 0.000 commodies equity - indonesia commodies equity - malaysia commodies equity - philippine 1.000 1.000 1.000 0.800 0.800 0.800 0.600 0.600 0.600 0.400 0.400 0.400 0.200 0.200 0.200 0.000 0.000 0.000 5 EW cryptocurrency equity - indonesia 5 EW cryptocurrency equity - philippine 5 EW cryptocurrency equity - malaysia Figure 1. Time-varying of optimal weights. Source. Bloomberg, processed. cryptocurrencies had lower α compared with any single of bitcoin in the equity-cryptocurrency portfolio were less cryptocurrency except litecoin implying that the diversifica- than 0.10%. Also, the average weight of five equally tion benefits existed when combining five cryptocurrencies weighted cryptocurrencies in the equity-cryptocurrency into a portfolio. Moreover, β values in the equities were rela- portfolio was less than 0.10%. However, Figure 1 shows tively large indicating that volatilities took a long time to die that having five equally weighted cryptocurrencies in the out following turmoil in the market. Furthermore, since the equity-cryptocurrency portfolio could reduce the rebalanc- values were above 0.99, the α + β values of bitcoin and ethe- ing frequencies. Also, the average weight of commodities reum showed that the term forecasts from the model were in the equity-commodities portfolio was above 0.3%. relatively flat. Furthermore, ethereum had the highest long- Furthermore, Figure 2 and Table 4 show the time-varying term volatility compared with other cryptocurrencies mean- and summary of hedge ratios, respectively. When buying while commodities had higher long-term volatility than the one domestic currency of equity in Indonesia, it should be equities. It is worth noting that the five equally weighted accompanied by shorting (sell) 0.006 of bitcoin. So, buying cryptocurrencies had lower long-term volatility compared one domestic currency of equity in Malaysia should be with any single cryptocurrency implying that combining five accompanied by buying (long) 0.008 of bitcoin. The average cryptocurrencies into a portfolio could reduce risk. As previ- hedge ratio for Southeast Asia emerging markets was less ously mentioned, in a multivariate GARCH model, the vari- than 0.10 indicating that it was a cheap hedge. Moreover, ables of the conditional variance and conditional covariance buying one dollar of equity in the developed market should should be calculated simultaneously by maximizing the fol- be accompanied by buying (long) 0.003 cents of bitcoin. lowing log likelihood. The table also presents the maximum The average hedge ratio for developed markets was also log likelihood values of each variable. less than 0.10 indicating that it was also a cheap hedge. Figure 1 shows the time-varying optimal weights Meanwhile, the value of hedge ratios in commodities hedg- obtained from the AG-DCC GARCH model. To save space, ing were bigger than the cryptocurrencies hedge. For exam- the authors only present some of the optimal weights. The ple, the value of the hedge ratio for Indonesia/commodities graphs from Figure 1 could be used to generalize other port- portfolio was −0.025 indicating that buying one equity in folio weights in this study. For instance, the average weights Indonesia should be accompanied by buying 0.025 of 2015 2015 2015 2015 2015 2015 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2019 2019 2019 2019 2019 2019 2015 2015 2015 2015 2016 2016 2016 2016 2016 2016 2016 2016 2017 2017 2017 2017 2017 2017 2017 2017 2018 2018 2018 2018 2018 2018 2018 2018 2019 2019 2019 2019 2015 2015 2015 2015 2015 2015 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2019 2019 2019 2019 2019 8 SAGE Open Bitcoin 0.70 0.50 0.30 0.10 -0.10 -0.30 -0.50 -0.70 Indonesia Malaysia Philippine Thailand Vietnam Developed Ethereum 0.70 0.20 -0.30 -0.80 Indonesia Malaysia Philippine Thailand Vietnam Developed Litecoin 1.00 0.50 0.00 -0.50 -1.00 Indonesia Malaysia Philippine Thailand Vietnam Developed Monero 1.00 0.50 0.00 -0.50 -1.00 Indonesia Malaysia Philippine Thailand Vietnam Developed Figure 2. (continued) 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 Susilo et al. 9 Ripple 1.30 0.80 0.30 -0.20 -0.70 Indonesia Malaysia Philippine Thailand Vietnam Developed Commodies 1.00 0.50 0.00 -0.50 -1.00 Indonesia Malaysia Philippine Thailand Vietnam Developed Figure 2. Time-varying hedge ratio. Source. Bloomberg, processed. Table 4. Hedged Portfolio Performances. Hedge effectiveness Sharpe ratio Portfolio GARCH Minimum variance GARCH Minimum variance Indonesia/Bitcoin 0.038 0.056 0.376 0.502 Malaysia/Bitcoin −0.099 0.029 0.233 0.177 Philippine/Bitcoin 0.027 0.050 0.367 0.319 Thailand/Bitcoin 0.026 0.015 0.478 0.528 Vietnam/Bitcoin −0.048 0.037 0.537 0.809 Developed/Bitcoin −0.122 0.024 0.312 0.508 Indonesia/Ethereum 0.005 0.020 0.263 0.404 Malaysia/Ethereum 0.000 0.011 0.076 0.107 Philippine/Ethereum 0.000 0.011 0.155 0.194 Thailand/Ethereum 0.006 0.002 0.349 0.455 Vietnam/Ethereum 0.008 0.007 0.661 0.708 Developed/Ethereum 0.006 0.003 0.463 0.412 Indonesia/Litecoin −0.045 0.025 0.201 0.369 Malaysia/Litecoin 0.000 0.016 0.025 0.085 Philippine/Litecoin −0.219 0.018 0.433 0.176 Thailand/Litecoin −0.019 0.006 0.256 0.458 Vietnam/Litecoin −0.015 0.019 0.512 0.710 Developed/Litecoin −0.264 0.004 0.227 0.397 Indonesia/Ripple −0.096 0.007 0.291 0.317 Malaysia/Ripple 0.020 0.003 0.043 0.033 (continued) 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 10 SAGE Open Table 4. (continued) Hedge effectiveness Sharpe ratio Portfolio GARCH Minimum variance GARCH Minimum variance Philippine/Ripple −0.255 0.004 0.280 0.126 Thailand/Ripple −0.781 0.001 0.571 0.432 Vietnam/Ripple −0.011 0.007 0.568 0.670 Developed/Ripple −0.294 0.003 0.737 0.387 Indonesia/Monero 0.006 0.017 0.286 0.365 Malaysia/Monero 0.002 0.007 0.009 0.067 Philippine/Monero 0.010 0.011 0.139 0.171 Thailand/Monero 0.000 0.000 0.415 0.425 Vietnam/Monero 0.003 0.009 0.572 0.695 Developed/Monero 0.000 0.003 0.340 0.394 Indonesia/Commodities 0.357 0.456 −0.092 0.135 Malaysia/Commodities 0.019 0.825 −0.684 −0.003 Philippine/Commodities 0.260 0.396 0.161 0.053 Thailand/Commodities 0.072 0.182 −0.158 0.223 Vietnam/Commodities 0.308 0.353 −0.371 0.424 Developed/Commodities 0.294 0.528 0.375 0.234 Indonesia/5 equally weighted cryptocurrencies 0.049 0.038 0.133 0.475 Malaysia/5 equally weighted cryptocurrencies 0.033 0.015 0.048 0.221 Philippine/5 equally weighted cryptocurrencies 0.014 0.026 0.090 0.267 Thailand/5 equally weighted cryptocurrencies 0.001 0.005 0.280 0.487 Vietnam/5 equally weighted cryptocurrencies 0.015 0.023 0.504 0.784 Developed/5 equally weighted cryptocurrencies 0.007 0.009 0.356 0.458 Source. Bloomberg, processed. commodities futures contract. The commodities hedge was The values of hedge effectiveness in the equities/five equally more expensive than cryptocurrencies hedge for Southeast weighted cryptocurrencies portfolio were always positive. Asia emerging markets. Similar patterns could be found in Also, commodities had better abilities to hedge equities than the developed market/commodities portfolio. The cheap cryptocurrencies from GARCH and minimum variance per- hedge of cryptocurrencies was consistent with Figure 3 spectives. Table 4 also shows the Sharpe ratio calculation. which was the result of the time-varying correlation between The average Sharpe ratio from equities/cryptocurrencies cryptocurrencies and equities. Cryptocurrencies had a very portfolio was higher than commodities/cryptocurrencies low dynamic correlation with equities in Southeast Asia portfolio implying that adding more cryptocurrencies in the emerging markets and developed markets. Meanwhile, portfolio could improve risk-adjusted returns in the expense commodities had a higher dynamic correlation with equities of hedge effectiveness. than cryptocurrencies. Table 4 shows the performance of the hedged portfolio. Managerial Implications Over the sample period, the values of hedge effectiveness were higher in the equities/commodities combination than in The overall results show that the optimum weights crypto- equities/cryptocurrencies. Based on the GARCH method, currencies in the combined portfolio are recommended cryptocurrencies could not consistently hedge equities since between 1% and 10%. This result is in line with Brière et al. the values of hedge effectiveness were not always positive. (2015). For risk takers, adding more cryptocurrencies can Meanwhile, the values of hedge effectiveness were always lead to higher risk-adjusted returns. The results of this study positive in the minimum variance method. The reason for the also support the work of Dyhrberg (2016b). This research difference was largely due to GARCH’s ability to capture complements the results of previous studies. However, the time-varying volatility compared with the classical mini- findings from this study are also lined with Klein et al. mum variance portfolio. The hedge effectiveness values (2018) who suggested that Bitcoin is a very weak hedge. from the minimum variance model showed that cryptocur- Interestingly, the five equally weighted cryptocurrencies rencies were not good hedge since the values were signifi- had lower GARCH error or α implying that the diversifica- cantly low which were also consistent with the GARCH tion benefits existed when combining five cryptocurrencies method. More interestingly, five equally weighted crypto- into a portfolio. Also, the values of hedge effectiveness in currencies had better performance for hedging the equities. the equities/five equally weighted cryptocurrencies portfolio Susilo et al. 11 Bitcoin 0.40 0.20 0.00 -0.20 -0.40 -0.60 Indonesia Malaysia Philippine Thailand Vietnam Developed Ethereum 0.20 0.10 0.00 -0.10 -0.20 Indonesia Malaysia Philippine Thailand Vietnam Developed Litecoin 0.40 0.20 0.00 -0.20 -0.40 Indonesia Malaysia Philippine Thailand Vietnam Developed Monero 0.20 0.10 0.00 -0.10 -0.20 Indonesia Malaysia Philippine Thailand Vietnam Developed Figure 3. (continued) 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 12 SAGE Open Ripple 0.20 0.10 0.00 -0.10 -0.20 Indonesia Malaysia Philippine Thailand Vietnam Developed Commodies 1.00 0.50 0.00 -0.50 -1.00 Indonesia Malaysia Philippine Thailand Vietnam Developed 5 equally weighted of cryptocurrencies 0.30 0.20 0.10 0.00 -0.10 -0.20 -0.30 Indonesia Malaysia Philippine Thailand Vietnam Developed Figure 3. Time-varying correlation. Source. Bloomberg, processed. were always positive. Although commodities were the better instability is quite similar to that shown in gold. This finding hedge, they were more expensive too. Moreover, commodi- also implies that managers should not constrain their prefer- ties that can be used to hedge equities are consistent with the ences to the top two cryptocurrencies which are bitcoin and findings of Chunhachinda et al. (2018). ethereum but also other cryptocurrencies to gain better This result gives a practical implication for investors and diversification benefits. policymakers. The availability of cryptocurrencies as a low-cost hedge may attract some equity traders. However, Conclusion cryptocurrencies are still far riskier than equities or com- modities. Hence, investors should be cautious when using This study considered the perspective of domestic investors cryptocurrencies. Moreover, the dynamic ability in the when combining cryptocurrencies or commodities with equi- diversification benefits of cryptocurrencies suggests that ties in a portfolio. This study also investigated the evolution 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2018 2018 2018 2018 2018 2018 2018 2018 2019 2019 2019 Susilo et al. 13 of the conditional correlations between cryptocurrencies or Bollerslev, T. (1990). Modelling the coherence in short-run nomi- nal exchange rates: A multivariate generalized arch model. The commodities with equities and found very low fluctuations Review of Economics and Statistics, 72(3), 498–505. https:// of correlations between cryptocurrencies and equities. doi.org/10.2307/2109358 Moreover, this study observed that the fluctuations of corre- Bouri, E., Gupta, R., Tiwari, A. K., & Roubaud, D. (2017). Does lations between cryptocurrencies and equities were below Bitcoin hedge global uncertainty? Evidence from wavelet- commodities and equities. based quantile-in-quantile regressions. Finance Research The results of the AG-DCC GARCH model, the hedge Letters, 23, 87–95. http://doi.org/10.1016/j.frl.2017.02.009 ratios, and the hedge effectiveness show that only five Bouri, E., Lucey, B., & Roubaud, D. (2019). Cryptocurrencies and equally weighted cryptocurrencies could consistently but the downside risk in equity investments. Finance Research marginally reduce portfolio risk. However, they did not Letters, 33, Article 101211. https://doi.org/10.1016/j.frl.2019 reduce risk as much as when commodities and equities .06.009 were combined. Also, the classical model of the minimum Bouri, E., Molnárb, P., Azzic, G., Roubaudd, D., & Hagfors, L. I. (2017). On the hedge and safe haven properties of Bitcoin: Is variance portfolio more or less supported the findings of it really more than a diversifier? Finance Research Letters, 20, AG-DCC GARCH. 192–198. https://doi.org/10.1016/j.frl.2016.09.025 The findings of this research can be useful for portfolio Brière, M., Oosterlinck, K., & Szafarz, A. (2015). Virtual currency, managers assessing diversification opportunities besides tangible return: Portfolio diversification with bitcoin. Journal gold. From a hedge effectiveness perspective, an investment of Asset Management, 16(6), 365–373. https://doi.org/10.1057/ strategy focused on equities/commodities portfolio may jam.2015.5 lead to more risk reduction but when risk-adjusted portfolio Cappiello, L., Engle, R. F., & Sheppard, K. (2006). Asymmetric returns are considered, equities/cryptocurrencies give a bet- dynamics in the correlations of global equity and bond returns. ter investment performance. Journal of Financial Econometrics, 4(4), 537–572. https://doi. The limitation of this study is that the authors did not org/10.1093/jjfinec/nbl005 make a sub-sample analysis regarding the cryptocurrency Chang, C.-L., McAleer, M., & Tansuchat, R. (2011). Crude oil hedging strategies using dynamic multivariate GARCH. bubbles starting from the end of 2017 to 2018. For future Energy Economics, 33(5), 912–923. https://doi.org/10.1016/j. studies, it is recommended to consider the non-linear rela- eneco.2011.01.009 tionship between cryptocurrencies and equities when exam- Charfeddine, L., & Maouchi, Y. (2019). Are shocks on the returns ining hedge effectiveness. and volatility of cryptocurrencies really persistent? Finance Research Letters, 28, 423–430. https://doi.org/10.1016/j.frl Authors Contributions .2018.06.017 All the authors have made an equal contribution to this study. Chunhachinda, P., de Boyrie, M. E., & Pavlova, I. (2018). Measuring the hedging effectiveness of commodities. Finance Research Letters, 30, 201–207. https://doi.org/10.1016/j.frl.2018.09.012 Availability of Data and Materials Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. The datasets used and/or analyzed during the current study are (2018). Exploring the dynamic relationships between crypto- available from the corresponding author on reasonable request. currencies and other financial assets. Economics Letters, 165, 28–34. https://doi.org/10.1016/j.econlet.2018.01.004 Declaration of Conflicting Interests Demir, E., Gozgor, G., Lau, C. K. M., & Vigne, S. A. (2018). Does The author(s) declared no potential conflicts of interest with respect economic policy uncertainty predict the Bitcoin returns? An to the research, authorship, and/or publication of this article. empirical investigation. Finance Research Letters, 26, 145–149. https://doi.org/10.1016/j.frl.2018.01.005 Dyhrberg, A. H. (2016a). Bitcoin, gold and the dollar—A GARCH Funding volatility analysis. 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SAGE OpenSAGE

Published: Nov 27, 2020

Keywords: cryptocurrency; AG-DCC-GARCH; hedge ratio; mean-variance portfolio; Southeast Asia emerging markets

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