Journal of Islamic Monetary Economics and Finance.
Vol.
No.
, pp.
269 - 306
p-ISSN: 2460-6146, e-ISSN: 2460-6618
RISK-ADJUSTED RETURNS AND SPILLOVER DYNAMICS
AMONG EMERGING DIGITAL CURRENCIES
Zayfri Ananto Husodo1.
Md.
Bokthiar Hasan2.
Humaira Tahsin Rafia3.
Masagus M.
Ridhwan4,5.
Gazi Salah Uddin6,7 and Muhammad Budi Prasetyo7 Department of Management.
Faculty of Economics and Business.
Universitas Indonesia.
Indonesia, husodo@ui.
Department of Finance and Banking.
Islamic University.
Kushtia.
Bangladesh, bokhtiar_bank@yahoo.
Department of Finance and Banking.
Islamic University.
Kushtia.
Bangladesh, humairatahsin52@gmail.
Bank Indonesia Institute.
Indonesia, mha_ridhwan@bi.
Perbanas Institute.
Indonesia Department of Management and Engineering.
Linkyping University.
Sweden, uddin@liu.
Department of Management.
Faculty of Economics and Business.
Universitas Indonesia.
Indonesia, prasetyo@ui.
ABSTRACT
This study investigates the interconnected dynamics among diverse digital currencies, specifically focusing on risk-adjusted returns, tail risks, dynamic spillovers, and portfolio implications.
Unlike prior research, which typically examines individual digital currency classes separately or in limited combinations, our study integrates six distinct classes of digital currencies, namely Islamic gold-backed cryptocurrencies, green cryptocurrencies, gold-backed stablecoins, non-fungible tokens (NFT.
, decentralized finance (DeF.
assets, and conventional cryptocurrencies, enabling direct comparisons of risk-return dynamics and systemic interdependencies.
Using Value at Risk (VaR).
Conditional Value at Risk (CVaR), quantile-based Vector Autoregression (Quantile VAR), and network connectedness analysis, we provide nuanced insights into the behavior of these assets across various market conditions .
ullish, bearish, and normal state.
Our results demonstrate that conventional cryptocurrencies and DeFi assets consistently deliver positive risk-adjusted returns, whereas Islamic gold-backed cryptocurrencies exhibit notably higher downside risks and negative performance.
Spillover analysis reveals pronounced connectedness, particularly in extreme market states, with conventional cryptocurrencies identified as primary transmitters of market shocks and gold-backed stablecoins and Islamic gold-backed cryptocurrencies as Our findings underscore significant diversification opportunities offered by pairs of assets exhibiting low connectedness, especially in normal market conditions.
Furthermore, portfolio optimization analysis highlights the superior hedging effectiveness and lower hedging costs associated with gold-backed stablecoins and conventional cryptocurrency pairs.
This comprehensive investigation delivers critical implications for investors, suggesting informed strategies for asset allocation and risk Policymakers can also utilize our insights to design adaptive regulatory frameworks that address systemic risks arising from digital currency markets.
Risk-adjusted Returns and Spillover Dynamics Among Emerging Digital Currencies Keywords: Digital currency.
Tail risks.
Dynamic spillovers.
Quantile VAR.
Portfolio management.
Islamic cryptocurrency.
Green cryptocurrency.
JEL classification: C58.
F36.
G11.
G15.
Article history:
Received : October 16, 2024 Revised : April 11, 2025 Accepted : May 30, 2025 Available online : June 26, 2025 https://doi.
org/10.
21098/jimf.
Journal of Islamic Monetary Economics and Finance.
Vol.
Number 2, 2025
INTRODUCTION
The past decade has witnessed a remarkable surge in digital currencies, offering both opportunities and challenges for investors, policymakers, and researchers.
Traditional cryptocurrencies, like Bitcoin and Ethereum, have captured the attention of investors, academics, and policymakers alike (Yousaf & Yarovaya, 2022.
for their decentralized nature and potential as alternative investments (Corbet et al.
, 2.
This has fueled a surge in trading volume, volatility, and pricing of these digital assets.
Consequently, a growing body of research (Demir et al.
, 2018.
Rizvi & Ali, 2022.
Hasan et al.
, 2022a.
Conlon & McGee, 2.
has explored the potential of cryptocurrencies as diversification and hedging tools.
However, despite conflicting findings, the attractiveness of the cryptocurrency market persists, driven by substantial investments from global stakeholders.
2021, global investments in the crypto market surpassed 30 billion USD (Hasan et , 2022.
, propelling the market capitalization to a staggering 1.
19 trillion USD by July 20231.
However, this rapid growth of the market has been overshadowed by its inherent volatility.
In addition, environmental concerns associated with traditional cryptocurrencies cast a further shadow on this already unpredictable landscape (Wang et al.
, 2.
These challenges have prompted the cryptocurrency community to diversify their holdings, seeking to mitigate portfolio volatility and lessen the impact of crises.
In response, a new wave of digital currencies has emerged, such as Islamic gold-backed crypto and green cryptocurrencies, addressing ethical and sustainability concerns.
Furthermore, gold-backed stablecoins, non-fungible tokens (NFT.
, and Decentralized Finance (DeF.
assets have also entered the scene.
While all are classified as digital currencies, these novel assets possess distinct features, investment purposes, and trading characteristics.
While the potential decoupling of these new asset classes from existing ones and even between themselves sould offer alternative investment opportunities .
edging potentia.
for cryptocurrency investors (Mnif & Jarboui, 2021.
Yousaf & Yarovaya, 2022.
, a concern remains whether shocks experienced by one class could still ripple through the entire ecosystem, given their shared nature as digital currencies.
Green cryptocurrencies are designed to address environmental concerns by minimizing energy consumption and integrating renewable energy into the mining process.
Islamic cryptocurrencies, on the other hand, adhere to Islamic principles by preventing speculative activities and ensuring asset backing, such as These two emerging classes focus on ethical and sustainable finance, making them particularly relevant in todayAos investment landscape, where investors increasingly prioritize environmental, social, and governance (ESG) factors (Starks.
Traditional Islamic assets .
Islamic stocks and Suku.
and green assets .
, green bond.
are usually treated as stable and resilient during crises due to their conservative nature (Hasan et al.
, 2022b.
Yousaf et al.
, 2024.
Similarly, these new digital currencies, especially Islamic gold-backed and green cryptocurrencies, are ethical and sustainable assets that are perceived to offer comparatively stable returns with lower volatility, particularly during turbulent market conditions (Husain et al.
, 2023.
Yousaf et al.
, 2024.
Additionally, gold-backed stablecoins.
Based on the data from https://coinmarketcap.
Risk-adjusted Returns and Spillover Dynamics Among Emerging Digital Currencies being asset-backed by gold, function as natural hedging instruments (Dyaz et al.
In contrast.
NFTs and DeFi assets remain nascent and evolving, presenting alternative investment avenues with inherently high volatility (Yousaf & Yarovaya.
Karim et al.
, 2.
Studying these diverse digital currencies together reflects their growing importance and provides a unique opportunity to assess their portfolio diversification and hedging potential.
Unveiling the complex interplay between these digital currencies, particularly regarding risk-return dynamics, interconnectedness .
pillover effect.
, and portfolio implications, is crucial for investors, policymakers, and researchers alike.
While existing literature predominantly addresses conventional cryptocurrencies and often neglects comprehensive comparative analyses across newer digital currencies, some studies .
Mnif & Jarboui, 2021.
Ali et al.
, 2022.
Rizvi & Ali, 2022.
Yousaf & Yarovaya, 2022a.
Wasiuzzaman et al.
, 2023.
Ali et al.
Yousaf et al.
, 2024b.
Yousaf et al.
, 2024.
on these specific newer classes begin to emerge.
However, studies on these emerging classes tend to focus narrowly on volatility, return connectedness, or their roles as hedging instruments visy-vis traditional financial assets, without adequately exploring their collective risk-return dynamics and systemic interdependencies.
This limited perspective restricts investorsAo ability to make informed diversification decisions and inhibits policymakers from developing targeted regulatory responses to the emerging risks and dynamics in digital currency markets.
Motivated by this notable gap, our study aims to contribute in several significant ways.
First, we offer a detailed comparative analysis of the risk-return dynamics, specifically examining risk-adjusted returns and tail risks across six distinct classes of digital currencies.
Second, we provide an in-depth assessment of dynamic spillover effects and interconnectedness among these assets across diverse market conditions .
ull, bear, and normal market state.
Lastly, we evaluate practical portfolio implications, including hedge ratios, optimal portfolio weights, and hedging effectiveness, to guide investors in managing risks and optimizing returns.
To these ends, we utilize a comprehensive dataset that covers six distinct and emerging classes of digital currencies: Islamic gold-backed cryptocurrencies, green cryptocurrencies, gold-backed stablecoins.
NFTs.
DeFi assets, and conventional Specifically, we select two representative digital currencies from each asset class, yielding a total of twelve assets for our empirical analysis.
Our data are daily spanning from February 7, 2020, to June 1, 2023.
We measure the risk-adjusted returns of these assets using three established metrics: the Sharpe ratio.
Treynor ratio, and JensenAos Alpha, following Al-Yahyaee et al.
and Yarovaya et al.
For assessing tail risk, we adopt a two-tiered approach by employing Value-at-Risk (VaR), capturing potential downside risk at specified confidence levels, and Conditional Value-at-Risk (CVaR), measuring expected shortfalls to address extreme losses beyond VaR thresholds (Halkos & Tsirivis.
Demiralay et al.
, 2.
Both VaR and CVaR are estimated at various quantiles .
%, 5%, and 10%) to robustly assess potential extreme outcomes.
Given structural breaks and heavy tails often observed in digital currency returns .
ee Section 3.
we employ a quantile-based Vector Autoregression (Quantile VAR) method (Ando Journal of Islamic Monetary Economics and Finance.
Vol.
Number 2, 2025 et al.
, 2.
Additionally, we adopt a pair-wise quantile network connectedness technique for enhanced robustness.
Our empirical findings underscore several critical insights.
First, conventional cryptocurrencies and DeFi assets consistently yield superior positive risk-adjusted returns, while Islamic gold-backed cryptocurrencies exhibit significantly lower or negative returns.
Moreover, we identify pronounced spillover effects during extreme market conditions, with conventional cryptocurrencies serving primarily as shock transmitters.
Importantly, we demonstrate that gold-backed stablecoins and conventional cryptocurrencies possess superior hedging characteristics, making them valuable components of diversified portfolios.
Our analysis explicitly grounds empirical expectations and interpretations within fundamental finance theories, including Modern Portfolio Theory (Markowitz, 1.
, the Efficient Market Hypothesis (Fama, 1.
, and classical portfolio hedging frameworks (Ederington, 1979.
Kroner & Sultan, 1.
, thereby enriching our theoretical contributions and enhancing practical implications.
The remainder of the study is structured as follows.
Section 2 reviews the relevant literature.
Section 3 describes the data and methodology.
Section 4 presents the empirical results and discussion.
Lastly.
Section 5 concludes the study with practical policy insights.
II.
RELATED LITERATURE
The existing literature on digital currencies has extensively explored various Issues, which cover market efficiency (Urquhart, 2016.
Nadarajah & Chu, 2.
, cryptocurrency as a medium of exchange (Baur et al.
, 2.
, hedging and safehaven properties (Demir et al.
, 2018.
Conlon & McGee, 2020.
Hasan et al.
, 2022.
, and herding behaviors (Yarovaya et al.
, 2021.
Despite extensive coverage, findings from these studies often remain inconsistent and inconclusive, reflecting the complexity and evolving nature of the cryptocurrency market.
Recent literature increasingly differentiates emerging digital currency categories from conventional cryptocurrencies based on unique characteristics, underlying assets, ethical principles, and sustainability considerations.
Islamic gold-backed cryptocurrencies, for example, are distinguished by their compliance with Sharia principles, notably avoiding speculation and ensuring asset backing.
Wasiuzzaman et al.
and Ali et al.
examine their performance during financial crises, highlighting their significant declines but affirming their effectiveness as hedging tools against traditional cryptocurrencies.
Similarly.
Ali et al.
identify OneGram as a safe haven against Islamic equity indices, particularly during the COVID-19 pandemic.
Aloui et al.
further highlight distinct behavioral differences, revealing Islamic gold-backed cryptocurrencies exhibit positive correlations with gold, whereas conventional cryptocurrencies typically show weaker or negative correlations.
Zhang et al.
provide complementary insights by exploring the benefits of holding cryptocurrencies during periods of downside risk, documenting a positive cross-sectional correlation between downside risk and future returns.
Green cryptocurrencies, designed to mitigate environmental impact by reducing energy consumption and promoting renewable energy usage, align Risk-adjusted Returns and Spillover Dynamics Among Emerging Digital Currencies closely with increasing investor preference for sustainable investment options (Starks, 2023.
Husain et al.
, 2023.
Yousaf et al.
, 2024.
Gold-backed stablecoins similarly serve as natural hedging instruments, given their underlying asset base in gold (Dyaz et al.
, 2.
Conversely.
NFTs and DeFi assets, while innovative, remain inherently volatile and present novel investment opportunities (Yousaf & Yarovaya, 2022a.
Karim et al.
, 2.
Yousaf & Yarovaya .
find that NFTs and DeFi assets are relatively disconnected from conventional markets, providing considerable diversification potential, although their volatility and spillover risks are notably higher during market crises.
Although insightful, existing studies typically approach these emerging digital currencies narrowly, either by examining them individually or by limiting their focus to volatility, returns, or their hedging role against traditional financial assets.
This fragmented perspective inadequately addresses the broader comparative risk-return dynamics, systemic interconnectedness, and portfolio implications across various digital currencies.
This research gap limits the ability of investors to effectively navigate systemic risks and diversification opportunities, while simultaneously constraining policymakersAo ability to develop responsive regulatory frameworks.
our study bridges this significant gap by conducting a comparative analysis across six distinct classes of emerging digital currencies.
Specifically, we explore risk-adjusted returns, tail risk profiles, spillover effects, and portfolio management By employing advanced econometric techniquesAiincluding VaR.
CVaR, quantile-based VAR, and network connectedness analysesAiwe aim to provide nuanced insights into the interconnected dynamics of digital currencies, informing both investors on diversification and policymakers on managing systemic risk within this rapidly evolving financial ecosystem.
DATA AND METHODOLOGY
Our methodological approach is motivated by fundamental financial theories.
Specifically.
Modern Portfolio Theory guides our examination of risk-adjusted returns and portfolio optimization strategies, while Efficient Market Hypothesis provides context for evaluating return predictability and market interconnectedness.
Moreover, established hedging theory (Ederington, 1979.
Kroner & Sultan, 1.
underpins our analysis of hedge ratios and hedging effectiveness.
Incorporating these theories allows us to form explicit expectations regarding asset behavior and provides robust theoretical foundations for interpreting our empirical results.
Data and Preliminary Analysis This study analyzes the comparative performance using risk-adjusted returns, downside risk, dynamic spillover, and portfolio strategies among different types of digital currencies by utilizing the daily closing prices.
We categorize digital currencies into six distinct asset classes based on their nature and the purposes they serve.
They include: .
Islamic gold-backed cryptocurrency, which is backed by gold, one of the six AuRabawiAy commodities .
old, silver, wheat, barley, salt, and date.
that are permissible for trade under Shariah law (Aloui et al.
, 2021.
Irfan Journal of Islamic Monetary Economics and Finance.
Vol.
Number 2, 2025 et al.
, 2.
Green cryptocurrency, which is energy efficient and incorporates renewable energy into the mining process (Husain et al.
, 2.
Gold-backed stablecoins, which are asset-backed stablecoins pegged to gold as their underlying asset (Dyaz et al.
, 2.
Non-fungible tokens (NFT.
, which are unique digital tokens representing ownership of specific digital objects such as artwork, music, videos, photos, tweets, and digital land (Dowling, 2022.
Yousaf & Yarovaya, .
Decentralized Finance (DeF.
assets, which refer to financial services like lending, borrowing, online wallets, derivatives, and spot trading that operate in a peer-to-peer manner without central authority (Yousaf & Yarovaya, 2022.
Conventional cryptocurrency, which encompasses any form of digital or virtual currency that uses cryptography to secure transactions and operates without a central issuing or regulating authority.
For each asset class, we select two assets based on their market capitalization and trading volume.
The selected assets, along with their symbols and data sources, are summarized in Table 1.
Our sample period spans from 07 February 2020 to 01 June 2023.
The sample period is based on the availability of data for these digital currencies.
The analysis of this study begins with transforming the prices of these assets into logarithmic returns using the formula:
Table 1.
Data Definitions and Sources Asset classes Asset names Symbols Data sources Islamic Gold-backed Cryptocurrency OneGram
X8X
Cardano
TRON
PAX Gold
Tether Gold
Theta Network
Decentraland
Chainlink
Maker
Bitcoin
Ethereum
OGC
X8X
ADA
TRX
PAXG
XAUT
THETA
MANA
LINK
MKR
BTC
ETH
https://coincodex.
Green Cryptocurrency Gold-backed Stablecoin NFTs DeFi Conventional Cryptocurrency https://coinmarketcap.
Table 2 presents the descriptive statistics of daily returns for all variables analyzed in our study.
We witness that except for OGC and X8X, all assets exhibit positive mean returns, with MANA having the highest mean returns, followed by ETH and THETA.
OGC demonstrates the highest volatility, with X8X following, as indicated by their standard deviations, whereas XUAT displays the lowest volatility, followed by PAXG.
The negative skewness observed in the return series of all variables, except MANA, suggests that the distribution of returns is asymmetric, with a longer or fatter tail on the left side.
This implies that the returns series experiences more extreme losses than gains.
Additionally, the extremely high kurtosis values across all variables highlight the presence of heavy tails or outliers, indicating leptokurtic distributions.
These findings imply that the return Risk-adjusted Returns and Spillover Dynamics Among Emerging Digital Currencies series of the variables are likely non-normal, a hypothesis further supported by the significant Jarque-Bera test statistics, suggesting time-varying or quantile regression models over simple linear regression for more robust outcomes.
Furthermore, we employ the Augmented Dickey-Fuller (ADF) and Phillips and Perron (PP) unit root tests to ascertain the stationarity of the data series.
The results from both tests confirm that our datasets are stationary.
Lastly, the Ljung-Box Q.
and Q2.
tests indicate that our return series exhibits both autocorrelation and heteroscedasticity (ARCH effec.
Consequently, employing simple linear models to estimate the interconnection between asset return series may generate spurious outcomes, warranting the use of GARCH- and quantilebased time-varying approaches (Siddique et al.
, 2.
Table 2.
Descriptive Statistics.
Autocorrelation.
Heteroscedasticity, and Stationarity Test Variables Mean Std.
Skewness Kurtosis Jarque-Bera Dev.
OGC
X8X
ADA
TRX
PAXG
XUAT
THETA
MANA
LINK
MKR
BTC
ETH
Q2.
ADF
70*** 13.
874***
87*** 65.
352***
48*** 22.
825***
83*** 40.
056***
78*** 23.
652***
52***
10*** 22.
507***
53***
15*** 21.
163***
33*** 38.
163***
96*** 17.
619***
98*** 25.
203***
228***
971***
037***
102***
296***
007***
045***
496***
922***
797***
64***
17***
16***
17***
39***
93***
08***
09***
60***
33***
59***
19***
53***
14***
03***
08***
70***
92***
95***
09***
60***
10***
47***
05***
Notes: The table reports the outcomes of descriptive statistics and unit root tests for all return series.
and Q2.
represent the Ljung-Box test statistic for autocorrelation with 10 lags and the Ljung-Box Q2 test for heteroscedasticity (ARCH effect.
with 10 lags, respectively.
ADF and PP are the Augmented Dickey-Fuller and PhillipsAePerron test statistics for unit roots considering both constant and trend at their levels.
Standard deviation is denoted as Std.
Dev.
The Akaike Information Criteria (AIC) is employed for assessing optimal lag lengths.
The significance levels at 1%, 5%, and 10% are marked by ***, **, and *, respectively.
Figure 1 illustrates the daily prices of all digital currencies examined in our The figure reveals considerable fluctuations in the prices of these assets over the sample period.
Most of the assets show an upward trend in the early part of the sample, peaking around 2021.
However, following this period, there is a notable decline in the prices of the majority of these assets, continuing until the end of the sample period (June 1, 2.
This pattern reflects significant volatility and a general downward trend in the latter part of the period.
Our sample period encompasses several major economic crises, such as the COVID-19 pandemic from 2020 to 2021 and the ongoing Russia-Ukraine war starting in 2022, which have likely contributed to substantial volatility in the digital currency markets (Hasan et al.
, 2021.
Boubaker et al.
, 2.
These crises have led to heightened uncertainty, affecting investor behavior and market stability, which in turn may have influenced the observed price dynamics.
Journal of Islamic Monetary Economics and Finance.
Vol.
Number 2, 2025
OGC
X8X
i i ADA i i i i i TRX
i i PAXG
2,100
2,000
1,900
1,900
1,800
1,800
1,700
1,700
i i THETA
i XAUT
1,600
i MANA 2,100 2,000 1,600 i i Figure 1.
Plots of Price Dynamics of Assets i Risk-adjusted Returns and Spillover Dynamics Among Emerging Digital Currencies LINK
6,000
4,000
2,000
MKR
8,000
i BTC
70,000
i i i ETH
5,000
60,000
i 4,000 50,000 3,000 40,000 2,000 30,000 1,000 20,000 10,000 i i i Figure 1.
Plots of Price Dynamics of Assets (Continue.
The correlation matrix among the variables is reported in Table 3.
We notice that all assets, except OGC, are positively and significantly correlated to each Notably.
OGC exhibits a negative but statistically insignificant correlation with most assets, except for PAXG and XUAT Table 3.
Correlation Matrix
OGC
X8X
ADA
TRX
PAXG
XAUT
THETA
MANA
LINK
MKR
BTC
ETH
OGC
X8X
ADA
TRX
PAXG
XAUT THETA MANA LINK
260***
278***
207***
239***
273***
256***
267***
300***
647***
141***
082***
596***
564***
725***
617***
700***
752***
130***
075***
558***
529***
648***
564***
676***
710***
674***
106***
080***
109***
107***
171***
154***
079***
076***
104***
103***
561***
627***
523***
642***
628***
592***
506***
590***
599***
MKR
BTC
ETH
652*** 1
710*** 0.
660*** 1
792*** 0.
746*** 0.
842*** 1
Notes: The table presents the correlation matrix.
The significance levels at 1%, 5%, and 10% are marked by ***, **, and *, respectively.
Journal of Islamic Monetary Economics and Finance.
Vol.
Number 2, 2025 Quantile VAR Specifications Given that our data series exhibit several issues, including non-normality, autocorrelation, and heteroscedasticity .
s detailed in the data sectio.
, employing simple linear models is inappropriate.
To effectively address these data challenges, we utilize the Quantile Vector Autoregression (Quantile VAR) approach.
This method is particularly advantageous as it is robust to outliers and provides a nuanced understanding of connectedness across various market conditions (Ando et al.
, 2.
By examining multiple quantiles, we gain additional insights into tail dependencies between diverse digital currencies and can thoroughly investigate time-domain connectivity under different market scenarios.
The Quantile VAR model thus provides a robust framework for analyzing the complex dependencies in our data, allowing for a more accurate and detailed understanding of market We begin our analysis with quantile regression, following the framework by Koenker & Ng .
and Jena et al.
Specifically, we examine the dependency of the variable t on xt at each quantile (E) of the conditional distribution of t/xt as specified below:
where QE represents the Eth conditional quantile function of t where the range of every quantile between 0 and 1 is signified by E.
xt denotes a vector of explanatory variables and (E) signifies the affiliation between xt and the Eth conditional quantile function of t.
The parameter vector ((E)) at the Eth quartile (E) is estimated as .
Then, the pth order of the n-variable for the Quantile VAR method is evaluated as follows:
where, t depicts the n-vector of the dependent variable, and c(E) and et(E) represent the n-vector of constants and residuals at quantile (E), respectively.
The matrix of lagged coefficients of the dependent variable at E is denoted by Bi(E), where i=1,A,P.
Then, are calculated by simulating the residuals, subject to the quantile constraints.
Subsequently, we represent the repercussion y for the population of the Eth conditional quantile in Equation .
, allowing us to approximate an equation-by-equation analysis at each quantile E.
Risk-adjusted Returns and Spillover Dynamics Among Emerging Digital Currencies Measuring Connectedness at Every Quantile Next, to compute multiple estimates of return interconnectivity at each quantile (E), we employ a methodology introduced by Ando et al.
, building upon Diebold & YilmazAos .
mean-based estimates.
This technique has also been utilized by Su .
and Jena et al.
Initially.
Equation .
is first adjusted as an unspecified order vector moving average (MA) process to evaluate interconnectedness across quantiles:
and yt is the summation of residuals et(E).
Secondly, diverging from Su .
, we adopt methodologies proposed by Koop et al.
and Pesaran & Shin .
, which demonstrate robustness in terms of variable ordering.
For a forecast horizon H, the generalized forecast error variance decomposition (GFEVD) of a variable resulting from shocks originating from various variables is as follows:
where ijg(H) refers to the impact of the jth variable on the variance of the forecast error of the variable ith at horizon H.
Then, is used for the vector of error variance matrix in the equation, and the jth diagonal component of the matrix is symbolized by Ejj.
ei signifies a vector worth 1 for the ith component and 0 otherwise.
Then, to standardize each component of the variance decomposition matrix, we proceed with:
Third, we consider GFEVD, which allows us to formulate four estimations of connectivity at each quantile.
Thus, we signify the total spillover index (TSI) at quantile E using the following:
Journal of Islamic Monetary Economics and Finance.
Vol.
Number 2, 2025 We use AuTOAy to symbolize the total directional spillover index from index i to indices j at quantile yua as follows:
On the other hand, the term AuFROMAy is used to depict the total directional spillover index from indices j to index i at quantile yua, as follows:
Finally, the net total directional spillover index (NSI) at quantile E is as follows.
The lag length parameters recommend one lag for the empirical study depending on the AIC, and the forecast horizon is 10.
To assess the time-varying spillover of different return spillover indicators, a 200-day rolling window estimation is utilized.
Hedge Ratio.
Optimal Portfolio Weights, and Hedging Effectiveness This study further estimates the hedge ratio (HR), optimal portfolio weights, and hedging effectiveness (HE) to provide enhanced hedging strategies and portfolio insights for investors and portfolio managers (Antonakakis et al.
, 2.
We employ the HR proposed by Kroner and Sultan .
, which is based on the conditional variance and covariance of the DCC-GARCH t-Copula model.
The HR calculates the cost of hedging a $1 long position in asset i with a ijt USD short position in asset j, encompassing various cryptocurrencies.
The calculation is defined as:
Here, larger conditional variances translate to lower long-position hedging costs, while higher conditional covariances result in higher long-position hedging Additionally, we estimate the optimal portfolio weights, following the methodology proposed by Kroner & Ng .
, based on the DCC-GARCH t-Copula framework.
The optimal portfolio weights between pairs of digital assets are determined by:
Risk-adjusted Returns and Spillover Dynamics Among Emerging Digital Currencies .
where Wijt may exceed one or fall below zero.
We set the following constraints to account for this shortcoming:
Finally, we utilize EderingtonAos .
technique to evaluate hedging effectiveness and various portfolio strategies among digital assets.
This can be expressed as follows:
Here r,w represents the hedged portfolio returns, which can be computed as:
HEi indicates the percent reduction in the unhedged positionAos variance.
The variance of the unhedged position of asset i is denoted by V.
denotes the hedged portfolio variance either from the optimal HR or weight strategy.
The greater risk reduction in the portfolio is associated with higher HEi.
Value at Risk (VaR) and Conditional Value at Risk (CVaR) To measure the tail risk, we employ both Value at Risk (VaR) and Conditional Value at Risk (CVaR), following Harris & Shen .
, and Demiralay et al.
VaR measures the potential downside risk of investments at different quantile levels over specified time intervals.
We use the historical VaR simulation approach to estimate potential losses in our digital currency assets.
We apply the historical VaR simulation approach in our research.
The formula is:
where Vt denotes the number of variables at time t and m indicates the number of days from which the past returns are taken.
Journal of Islamic Monetary Economics and Finance.
Vol.
Number 2, 2025 CvaR, also known as expected shortfall, complements VaR by quantifying the tail risk of investments beyond the VaR cut-off point.
CvaR endeavors to overcome the loopholes of the VaR model.
The CvaR is given by:
where p.
dx indicates the probability concentration of receiving a return with value x.
c represents the cut-off VaR breakpoint and VaR is the pre-specified VaR IV.
EMPIRICAL ANALYSIS OF RESULTS
Risk-adjusted Returns Performance We begin our analysis by evaluating the risk-adjusted return performance of Table 4 exhibits the results of risk-adjusted returns for all assets under investigation, based on three widely used metrics: the Sharpe ratio.
Treynor ratio, and Jensen Alpha.
The findings indicate that all assets, except Islamic gold-backed cryptocurrencies, exhibit positive Sharpe ratios, suggesting that the majority of the assets provide returns that are sufficient to compensate for the risk undertaken.
Regarding the Treynor ratio, which measures returns in relation to systematic risk, the majority of assets show positive Treynor ratios, indicating that their returns justify their market risk.
However.
OGC.
TRX.
PAXG.
THETA, and MANA display negative Treynor ratios, signifying that their returns do not adequately compensate for their exposure to systematic risk, making them less attractive from a risk-adjusted perspective.
Consistent with the Sharpe ratio findings.
JensenAos Alpha outcomes Indicate that except for Islamic gold-backed cryptocurrencies, all assets have positive JensenAos Alphas.
This implies that, when compared to the expected returns based on the Capital Asset Pricing Model, most assets provide returns above their expected risk-adjusted benchmark.
It is also noticed that conventional cryptocurrencies and DeFis demonstrate the most consistent positive risk-adjusted returns based on all three metrics, while Islamic gold-backed cryptocurrencies reveal the negative and lowest risk-adjusted returns.
These findings align with Platanakis & Urquhart .
, who reveal substantial positive risk-adjusted returns in traditional cryptocurrencies.
Additionally, our outcomes are somewhat supported by Aloui et al.
, who exhibit negative mean returns for Islamic gold-backed The notably low risk-adjusted return profile of Islamic gold-backed cryptocurrencies is a significant finding.
Table 2 and 4 reveal that gold-backed cryptocurrencies, specifically PAXG and XAUT, achieve markedly superior average and risk-adjusted returns compared to their Islamic counterparts despite both being backed by gold.
The inferior risk-return profiles of OGC and X8X are primarily attributable to their relatively low market capitalizations, especially when contrasted to conventional gold-backed cryptocurrency like PAXG and XAUT.
Siswantoro et al.
identify substantial disagreements regarding the halal status of cryptocurrencies from Sharia perspective, contributing to Muslim Risk-adjusted Returns and Spillover Dynamics Among Emerging Digital Currencies investorsAo reluctance to adopt Islamic gold-backed cryptocurrencies as viable investment instruments.
Moreover, no Muslim-majority country has yet provided official support for cryptocurrencies, either as a medium of exchange or investment vehicles, further limiting their adoption.
Table 4.
Risk-adjusted Return Estimations Variables Sharpe ratio
Rank
Treynor ratio
Rank
JensonAos Alpha
Rank
OGC
X8X
ADA
TRX
PAXG
XAUT
THETA
MANA
LINK
MKR
BTC
ETH
Notes: The table presents the results of the Sharpe ratio.
Treynor ratio, and JensenAos alpha using the US 10-year treasury bond yield index as a proxy of the risk-free rate.
Downside Risk and Expected Shortfall Analysis Table 5 presents the estimates of assetsAo downside risk and expected shortfall using VaR and CVaR, respectively, at three quantiles .
%, 5%, and 1%).
The analysis reveals that gold-backed stablecoins (XUAT and PAXG) and conventional cryptocurrencies (BTC and ETH) exhibit relatively lower downside risk across all quantiles compared to other digital currencies, as indicated by both VaR and CVaR metrics.
This suggests that these assets may offer a more stable investment option with a lower potential for extreme losses, likely stemming from their established market presence and underlying asset backing (Dyaz et al.
, 2.
Green cryptocurrencies (TRX and ADA) also show lower downside risk across most quantiles.
Conversely.
Islamic gold-backed cryptocurrencies (OGC and X8X).
NFTs (THETA and MANA), and DeFi assets (LINK and MKR) demonstrate significantly higher downside risk across most quantiles, according to both VaR and CVaR estimates.
This highlights the elevated risk associated with these assets under adverse market conditions, making them less suitable for risk-averse investors (Osman et al.
, 2.
It is essential to note that the magnitude of potential loss for digital asset classes increases with higher confidence levels in both VaR and CVaR estimations.
This underscores the importance of considering higher confidence levels in risk assessment to account for potential worst-case scenarios more effectively.
Journal of Islamic Monetary Economics and Finance.
Vol.
Number 2, 2025 Table 5.
VaR and CVaR estimations Variables OGC
X8X
ADA
TRX
PAXG
XUAT
THETA
MANA
LINK
MKR
BTC
ETH
VaR CVaR Notes: The table highlights the results of examined historical VaR and CVaR estimations at three quantile levels: 10%, 5%, and 1% These findings align with Dyaz et al.
, who reveal that gold-backed stablecoins are relatively stable and exhibit lower downside risk in portfolios.
Additionally, our results are partly supported by Yousaf & Yarovaya .
, who document that NFTs and DeFi assets exhibit greater volatility, though they may offer higher returns.
Quantile Spillover Connectedness Analysis Static Quantile Spillover Connectedness Table 6 presents the outcomes of the static spillover connectedness .
irectional, pairwise, and Total Connectedness Index (TCI)) among digital currency assets under three different market conditions: bear .
05 quantil.
, normal .
5 quantil.
, and bullish .
95 quantil.
, shown in Panels A.
B, and C, respectively.
The findings reveal that the TCI values, located at the bottom right corner of the table, are 95.
27%, and 96.
18% for the lower, middle, and upper quantiles, respectively.
This indicates that the connectedness between digital currency assetsAo returns is robust, with stronger spillovers in extreme market conditions .
ower and upper quantile.
compared to normal market conditions.
As return shocks intensify, the spillover between digital currencies strengthens, suggesting that the degree of return spillovers is symmetric in both bull and bear markets.
When examining the transmission of shocks .
ndicated in the penultimate rows labeled AoTO othersAo in Panels A.
B, and C), the analysis reveals that during bearish and normal markets.
ETH and LINK play the most significant roles in transmitting shocks throughout the system.
In the upper quantile.
MANA and ETH transmit the highest spillover to the system.
Other assets tend to spread shocks more in the extreme lower and upper quantiles compared to the normal quantile.
It is also notable that conventional cryptocurrencies (BTC and ETH) dominate other classes of digital currencies in spreading spillovers across all market conditions.
Risk-adjusted Returns and Spillover Dynamics Among Emerging Digital Currencies Regarding spillover reception from other assets, the last column.
AoFROM othersAo in Panels A.
B, and C of Table 6, demonstrates that ETH receives the most significant spillover from the system in lower and normal market states, while ADA receives the most in the upper market.
Interestingly, all assets, except OGC, receive comparable shocks from the system in the extreme markets .
ower and upper quantile.
However, in the normal quantile, conventional digital currencies (BTC and ETH) receive the highest shocks from the system.
Overall, the findings indicate that the magnitude of spillover, in terms of both receiving and spreading, is higher in extreme market states than in normal periods among most of the digital assets considered.
The net spillover outcomes, shown by the last AoNETAo row, suggest that in the lower quantile.
OGC.
X8X.
PAXG.
XUAT.
MANA, and MKR are net spillover receivers .
egative value.
, while ADA.
TRX.
THETA.
LINK.
BTC, and ETH are net spillover spreaders .
ositive value.
In the normal market.
X8X.
TRX.
PAXG.
XUAT.
THETA.
MANA, and MKR act as net spillover receivers, whereas OGC.
ADA.
LINK.
BTC, and ETH are identified as net transmitters of spillover.
In the upper quantile.
OGC.
X8X.
TRX.
PAXG, and XUAT are net absorbers, while the remaining assets (ADA.
THETA.
MANA.
LINK.
MKR.
BTC, and ETH) turn out to be net spreaders.
It is observed that gold-backed digital currencies .
Islamic Gold-backed Cryptocurrency and Gold-backed Stablecoi.
are consistent spillover receivers across all market conditions.
Conversely, conventional cryptocurrencies (BTC and ETH) are consistent spillover transmitters regardless of market BTC and ETH are the oldest and most popular cryptocurrencies, occupying the largest shares in the digital currency markets and being the most liquid.
Consequently, their shocks tend to spread to other digital currency markets (Yousaf et al.
, 2024.
These findings are consistent with previous studies, such as Yousaf et al.
and Yousaf et al.
, which reveal higher connectedness between digital currencies in extreme market states compared to normal market Given the constantly evolving macroeconomic, financial, and geopolitical crises, examining how return spillovers change over time is crucial.
Therefore, the above static findings are presented dynamically in the next section.
OGC
Variables OGC
X8X
ADA
TRX
PAXG
XUAT
THETA
MANA
LINK
MKR
BTC
ETH
TO Others NET OGC
X8X
ADA
TRX
PAXG
XUAT
THETA
MANA
LINK
X8X
ADA
TRX
THETA
Panel B: Normal return quantile .
XUAT
MANA
Panel A: Lower return quantile .
PAXG
Table 6.
Static Spillover Connectedness LINK
MKR
BTC
ETH
TCI =
FROM
Others Journal of Islamic Monetary Economics and Finance.
Vol.
Number 2, 2025
MKR
BTC
ETH
TO Others NET OGC
X8X
ADA
TRX
PAXG
XUAT
THETA
MANA
LINK
MKR
BTC
ETH
TO Others NET X8X
ADA
TRX
XUAT
THETA
MANA
Panel C: Upper return quantile .
PAXG
LINK
MKR
BTC
ETH
TCI =
TCI =
FROM
Others Notes: The table documents the pairwise directional connectedness among digital currencies for lower, normal, and upper return quantiles .
05, 0.
5, and 0.
95, respectivel.
The total directional connectedness to and from others is represented by the AuTO OthersAy row and the AuFROM OthersAy column, respectively.
NET and TCI refer to the net directional connectedness and total connectedness index, respectively.
In lag length selection.
AIC is employed OGC Variables Table 6.
Static Spillover Connectedness (Continue.
Risk-adjusted Returns and Spillover Dynamics Among Emerging Digital Currencies Journal of Islamic Monetary Economics and Finance.
Vol.
Number 2, 2025 Dynamic Quantile Spillover Connectedness Figure 2 illustrates the dynamic directional spillovers Aufrom othersAy to digital currencies under three different market conditions, corresponding to the AoFrom othersAo column in Table 6.
The figure demonstrates that most assets, with a few exceptions, receive significant and consistent spillovers from other digital currencies across all market circumstances, with more pronounced in extreme market conditions .
ear and bull market.
However, the reception of spillovers for all assets varies substantially over time in the middle quantiles.
This variability suggests that during normal market conditions, the interconnectedness and vulnerability of digital currencies to external shocks are more dynamic and less predictable compared to extreme market conditions.
Notably, during the normal market state.
Islamic gold-backed cryptocurrency (OGC and X8X) receives the lowest spillovers from others.
This could be attributed to their unique structure and underlying assets, which might provide a certain level of insulation from the broader market shocks affecting other digital assets (Yousaf et al.
, 2024.
The dynamic analysis also highlights that during periods of extreme market conditions, the spillover effects become more pronounced.
This is evident from the consistent and significant spillovers observed in both bearish and bullish quantiles.
Such behavior indicates a higher degree of interconnectedness and systemic risk among digital currencies during times of market stress or exuberance, underscoring the importance for investors to be particularly cautious during these periods.
Figure 3 illustrates the spillovers from one asset to others, corresponding to the penultimate row of Table 6.
The results demonstrate that all assets transmit high levels of spillovers, approximately 100%, throughout the entire sample period across all market conditions, with more pronounced effects in extreme This indicates a robust interconnectedness within the digital currency market, where shocks originating from one asset can significantly influence others.
However, the transmission of spillovers varies over time for certain asset classes, such as Islamic gold-backed cryptocurrencies (OGC and X8X) and NFTs (THETA and MANA), across all quantiles.
This variability suggests that while these assets generally exhibit high spillover transmission, their influence fluctuates depending on the market conditions.
Risk-adjusted Returns and Spillover Dynamics Among Emerging Digital Currencies Lower return quantiles .
OGC
X8X
ADA
TRX
PAXG
XAUT
THETA
MANA
LINK
MKR
BTC
ETH
Average return quantiles .
OGC
PAXG
LINK
X8X
XAUT
ADA
THETA
TRX
MANA
BTC
ETH
MKR
Figure 2.
Return Spillovers FROM Others for Three Return Quantiles .
05, 0.
5, and 0.
Journal of Islamic Monetary Economics and Finance.
Vol.
Number 2, 2025 Higher return quantiles .
OGC
PAXG
LINK
X8X
XAUT
ADA
THETA
TRX
MANA
MKR
BTC
ETH
Figure 2.
Return Spillovers FROM Others for Three Return Quantiles .
05, 0.
5, and 0.
(Continue.
Lower return quantiles .
OGC
PAXG
X8X
XAUT
ADA
THETA
MANA
BTC
ETH
TRX
MKR
LINK
Figure 3.
Return Spillovers TO Others for Three Return Quantiles .
05, 0.
5, and 0.
Risk-adjusted Returns and Spillover Dynamics Among Emerging Digital Currencies Average return quantiles .
OGC
PAXG
LINK
X8X
ADA
XAUT
THETA
TRX
MKR
BTC
ETH
MANA
Higher return quantiles .
OGC
PAXG
LINK
X8X
XAUT
ADA
TRX
THETA
BTC
ETH
MANA
MKR
Figure 3.
Return Spillovers TO Others for Three Return Quantiles .
05, 0.
5, and 0.
(Continue.
Journal of Islamic Monetary Economics and Finance.
Vol.
Number 2, 2025 The pronounced spillover effects in extreme market conditions .
earish and bullis.
highlight the heightened systemic risk during these periods.
It suggests that during times of market stress or exuberance, the interdependencies among digital currencies intensify, leading to a more significant transmission of shocks across the market.
In normal market conditions, the dynamic spillover effects are still significant but less predictable, indicating that the interconnectedness is more stable during periods of average market activity but can change rapidly due to emerging shocks or volatility.
Lower return quantiles .
OGC
PAXG
LINK
X8X
XAUT
ADA
THETA
TRX
MANA
MKR
BTC
ETH
Figure 4.
Net Total Directional Spillovers for Three Return Quantiles .
05, 0.
5, and 0.
Risk-adjusted Returns and Spillover Dynamics Among Emerging Digital Currencies Average return quantiles .
OGC
PAXG
LINK
X8X
XAUT
ADA
THETA
TRX
MANA
BTC
ETH
MKR
Higher return quantiles .
OGC
PAXG
X8X
ADA
TRX
LINK
XAUT
THETA
MANA
MKR
BTC
ETH
Figure 4.
Net Total Directional Spillovers for Three Return Quantiles .
05, 0.
5, and 0.
Journal of Islamic Monetary Economics and Finance.
Vol.
Number 2, 2025 Figure 4 presents the net directional return connectedness of digital asset classes at three quantiles.
The results indicate that, consistent with earlier analyses, the net return spillovers for all digital assets are more pronounced and time-varying in extreme market conditions compared to normal markets.
Specifically.
Islamic gold-backed cryptocurrencies and gold-backed stablecoins are net receivers of spillover across all quantiles.
This implies that these assets are more susceptible to absorbing shocks from the broader market, possibly due to their nature as stable investment options, attracting investors during times of market turbulence.
Conversely, conventional cryptocurrencies .
BTC and ETH) are net spillover spreaders, indicating their influential role in transmitting shocks across the digital currency market.
Their established market presence, high liquidity, and significant market capitalization likely contribute to their capacity to affect other assets substantially.
However, the net spillover shocks of the remaining assets are not consistent, varying over time and quantiles.
This variability highlights the dynamic nature of these assetsAo connectedness and their potential to either absorb or transmit shocks depending on market conditions.
This underscores the importance for investors and portfolio managers to continuously monitor the market conditions and adjust their risk management strategies accordingly.
Furthermore, in the middle quantile, the findings suggest that all digital assets tend to both receive and transmit lower spillovers compared to extreme quantiles.
This behavior implies that during normal market conditions, digital assets may act as potential diversifiable investments, offering lower systemic risk and potentially better portfolio diversification benefits.
Figure 5 displays the dynamic pairwise net volatility connectedness among the pairs of digital currencies under three quantiles.
The findings reveal that the majority of digital currency pairs exhibit minimal to no spillover shocks across all quantiles.
This suggests that digital currencies offer substantial potential for hedging and diversification, regardless of market conditions.
These outcomes are somewhat corroborated by previous studies .
Aloui et al.
, 2021.
Karim et al.
Yousaf & Yarovaya, 2022a, .
However, specific exceptions are observed in the lower and middle quantiles.
For instance.
ETH consistently transmits spillovers to MANA.
X8X.
TRX.
XAUT, and MKR across the entire sample period in both lower and middle quantiles, with the magnitude of shock transmission varying over time.
Similarly.
BTC plays a significant role in spreading shocks to XAUT.
X8X.
TRX.
MKR, and MANA in the lower and middle quantiles, regardless of the changing degree of spillover over This indicates that both Bitcoin and Ethereum consistently provide hedging opportunities, as they are not significant recipients of spillover shocks from other digital currencies.
These findings align with previous studies by Aloui et al.
and Yousaf & Yarovaya .
Additionally, other digital currency pairs also exhibit minor spillover transmissions, reinforcing the hedging and diversification potential in the digital currency market.
Risk-adjusted Returns and Spillover Dynamics Among Emerging Digital Currencies Lower return quantiles .
OGC - ADA
OGC - BTC
ADA - BTC
OGC - PAXG
ADA - PAXG
OGC - THETA
OGC - LINK
X8X - TRX
ADA - LINK
X8X - ETH
PAXG -BTC
ADA - ETH
TRX - ETH
LINK - ETH
LINK - BTC
TRX -MANA
TRX - MKR
XAUT - MKR
PAXG - ETH
XAUT - BTC
X8X - MKR
X8X - MANA
MANA - ETH
MKR - ETH
XAUT - ETH
MKR - BTC
THETA - ETH
MANA - BTC
XAUT - MANA
OGC - ETH
PAXG - LINK
TRX -BTC
TRX - XAUT
THETA - BTC
X8X - BTC
X8X - XAUT
PAXG - THETA
ADA - THETA
MANA - MKR
THETA - LINK
Figure 5.
Net Pairwise Directional Volatility Spillovers for Three Return Quantiles .
05, 0.
Journal of Islamic Monetary Economics and Finance.
Vol.
Number 2, 2025 Average return quantiles .
OGC - ADA
OGC - BTC
ADA - BTC
OGC - PAXG
ADA - PAXG
OGC - THETA
OGC - LINK
X8X - BTC
ADA - LINK
TRX - BTC
XAUT - MANA
TRX - MANA
XAUT - MKR
TRX - MKR
PAXG -BTC
XAUT - BTC
THETA - BTC
LINK - BTC
MKR - BTC
X8X - ETH
PAXG - ETH
OGC - ETH
LINK - ETH
MKR - ETH
XAUT - ETH
TRX - ETH
MANA - ETH
ADA - ETH
THETA - ETH
X8X - MKR
X8X - MANA
X8X - XAUT
MANA - BTC
TRX - XAUT
PAXG - LINK
MANA - MKR
PAXG - THETA
ADA - THETA
X8X - TRX
THETA - LINK
Figure 5.
Net Pairwise Directional Volatility Spillovers for Three Return Quantiles .
05, 0.
(Continue.
Risk-adjusted Returns and Spillover Dynamics Among Emerging Digital Currencies Higher return quantiles .
OGC - ADA
OGC - PAXG
OGC - THETA
OGC - LINK
X8X - MKR
ADA - THETA
ADA - LINK
TRX - MKR
PAXG - LINK
PAXG -BTC
XAUT - BTC
LINK - BTC
PAXG - ETH
THETA - ETH
LINK - ETH
X8X - ETH
TRX - ETH
XAUT - ETH
PAXG - ETH
ADA - ETH
MANA - ETH
MKR - ETH
MKR - BTC
XAUT - MKR
THETA - BTC
OGC - ETH
MANA - BTC
XAUT - MANA
TRX - MANA
PAXG - THETA
THETA - LINK
TRX - BTC
TRX - XAUT
X8X - MANA
ADA - PAXG
ADA - BTC
X8X - BTC
X8X - XAUT
X8X - TRX
OGC - BTC
Figure 5.
Net Pairwise Directional Volatility Spillovers for Three Return Quantiles .
05, 0.
(Continue.
Journal of Islamic Monetary Economics and Finance.
Vol.
Number 2, 2025 Pairwise Network Connectedness at Different Quantiles This study utilizes the network connectivity technique developed by Diebold & Yilmaz .
in conjunction with the Quantile VAR process to gain deeper insights into the connectedness of digital currencies under varying market Figure 6 illustrates the results of the pairwise network spillover directional connectivity across three return quantiles.
The Quantile VAR network connectedness map provides critical information on senders and receivers of spillovers and the degree of connectivity, which is essential for making informed decisions under different market circumstances.
The pictorial analysis indicates that the lower and upper quantiles exhibit higher connectivity compared to the normal quantile plot, which supports the findings from the Quantile VAR analysis.
Specifically, in extreme markets .
ower and upper quantile.
Islamic gold-backed cryptocurrencies (X8X and OGC) are the primary receivers of spillover shocks from other asset classes, followed by gold-backed stablecoins (PAXG and XAUT).
In normal market conditions.
X8X and MANA are the predominant shock absorbers.
In terms of spillover transmission.
Bitcoin.
Ethereum, and LINK are the main spreaders of shocks to other assets in the lower and normal quantiles.
However, this pattern reverses in the upper quantile, where MANA becomes the leading shock transmitter, followed by Ethereum.
Gold-backed stablecoins (PAXG and XAUT) neither transmit nor receive significant shocks in the normal market, making them attractive for investors seeking to diversify their portfolios with relatively lower-risk assets.
This finding aligns with Dyaz et al.
, who highlighted that stablecoins have high diversification capacities by systematically mitigating portfolio tail risk.
Lower return quantiles .
Figure 6.
Network Plots for Three Return Quantiles .
05, 0.
5, and 0.
Risk-adjusted Returns and Spillover Dynamics Among Emerging Digital Currencies Normal return quantiles .
Upper return quantiles .
Figure 6.
Network Plots for Three Return Quantiles .
05, 0.
5, and 0.
(Continue.
Journal of Islamic Monetary Economics and Finance.
Vol.
Number 2, 2025 Analysis of Portfolio Implications We further analyze hedge ratios (HR), optimal weights, and hedging effectiveness (HE) for pairs of conventional and digital currencies, as detailed in Table 7.
The HR results reveal that most Bitcoin and other digital currency pairs have lower HRs, ranging from -1% to 49%.
This indicates that a relatively small amount is needed in the short position to hedge a USD 1 long position in Bitcoin.
Exceptions are the BTC/TRX.
BTC/PAXG, and BTC/XAUT pairs, which show higher HRs.
Conversely.
Ethereum pairs generally require higher hedging costs, with few exceptions.
Thus, investors might favor various digital currencies, especially Islamic gold-backed cryptocurrencies, for their lower HRs to mitigate BitcoinAos return variance.
Table 7.
Hedging Effectiveness with Hedge Ratios and Optimal Weights Variables BTC/OGC BTC/X8X BTC/ADA BTC/TRX BTC/PAXG BTC/XAUT BTC/THETA BTC/MANA BTC/LINK BTC/MKR ETH/OGC ETH/X8X ETH/ADA ETH/TRX ETH/PAXG ETH/XAUT ETH/THETA ETH/MANA ETH/LINK ETH/MKR Hedge Ratio
49***
62***
38***
37***
45***
40***
67***
84***
47***
47***
63***
59***
Optimal Weights 53*** 03*** 02*** 46*** 36*** 02*** 01*** Notes: The table presents the hedge ratio, optimal weights, and hedging effectiveness between different classes of digital currencies.
The hedge ratio and optimal weights are symbolized by and w, respectively.
The hedging effectiveness is denoted by HE.
***, **, and * indicate the significance at the 1%, 5%, and 10% levels, respectively The optimal weights findings indicate that most pairs incorporating Bitcoin and Ethereum with other digital currencies have high, though insignificant, optimal portfolio weights.
This implies that a significant portion of funds should be allocated to Bitcoin and Ethereum to minimize return variance.
However, pairs with gold-backed stablecoins (PAXG and XAUT) and both Bitcoin and Ethereum show the lowest and most significant optimal weights.
This means that to maximize HE, ranging from 93% to 97%, over 97% of the portfolio should be Risk-adjusted Returns and Spillover Dynamics Among Emerging Digital Currencies invested in gold-backed stablecoins.
These pairs form optimal portfolios due to their ability to significantly reduce return variance.
This conclusion aligns with our earlier analysis using the Quantile-based network connectedness approach, which demonstrates that gold-backed stablecoins are less susceptible to transmitting and receiving volatility shocks from other asset classes.
Consequently, these assets are robust options for hedging and diversification.
CONCLUSIONS AND POLICY RAMIFICATIONS
The rise of diverse digital currenciesAiconventional cryptocurrencies.
Islamic goldbacked cryptocurrencies, green cryptocurrencies, gold-backed stablecoins.
NFTs, and DeFi assetsAihas significantly reshaped the financial landscape.
This study examines their risk dynamics, interconnectedness, and portfolio implications, addressing existing gaps through a comprehensive comparative analysis.
Our analysis, leveraging advanced econometric techniques, reveals that conventional cryptocurrencies and DeFi assets consistently deliver positive riskadjusted returns, whereas Islamic gold-backed cryptocurrencies demonstrate negative returns and higher tail risks.
These findings imply practical challenges related to market liquidity, regulatory clarity, and investor confidence for Islamic crypto-assets.
Results reveal that conventional cryptocurrencies and DeFi assets consistently provide positive risk-adjusted returns, suggesting their resilience and appeal as investment vehicles.
Conversely.
Islamic gold-backed cryptocurrencies exhibit significantly negative returns and heightened downside risks, indicating underlying challenges such as limited liquidity, market depth, and regulatory These findings have critical implications for Islamic finance and economics.
Practically, investors should exercise caution with Islamic gold-backed cryptoassets, closely monitoring developments in market infrastructure and regulatory From a policy perspective, our results highlight the urgency for establishing clear, supportive regulatory frameworks that enhance Shariah compliance and investor confidence.
Theoretically, our findings challenge Islamic finance scholars to explore effective methods of operationalizing ethical principles, such as assetbacking and avoiding gharar .
xcessive uncertaint.
, within highly innovative and volatile digital markets.
This opens new research avenues into the resilience and stability of ethical-based finance in fintech environments.
Our results carry valuable policy insights.
Investors and portfolio managers should actively consider incorporating conventional cryptocurrencies and DeFi assets due to their consistent positive performance and relatively lower tail risks.
Conversely, cautious engagement with Islamic gold-backed cryptocurrencies.
NFTs, and certain DeFi assets is advisable given their higher risk profiles.
Investors must also remain sensitive to market conditions, adjusting portfolio strategies dynamically to effectively manage increased risks during volatile market states.
For policymakers, the heightened interconnectedness and potential systemic risks identified call for adaptive regulatory frameworks tailored specifically for the dynamic digital currency landscape.
Regulations should emphasize transparency, liquidity enhancement, and effective asset-backing criteria, especially for ethically- Journal of Islamic Monetary Economics and Finance.
Vol.
Number 2, 2025 oriented digital currencies.
This approach would help mitigate systemic risks, foster stability, and enhance investor confidence.
Finally, while our study makes significant strides in understanding the interconnected dynamics of diverse digital currencies, it recognizes several Our dataset comprises six asset classes, each represented by two thus, future research could integrate emerging digital asset classes and additional currencies to provide more extensive insights.
Employing alternative econometric techniques or machine learning models could further elucidate complex relationships between digital assets.
Additionally, assessing the impact of regulatory frameworks on risk profiles and interconnectedness remains a valuable direction for future research, offering practical guidance for policymakers navigating the evolving digital currency ecosystem.
ACKNOWLEDGMENT
Gazi Salah Uddin gratefully acknowledges the Faculty of Economics and Business.
Universitas Indonesia, for the academic appointment as Adjunct and Visiting Professor, and expresses sincere appreciation for the institutional support and research facilities extended during his residency, which significantly contributed to the completion of this work.
REFERENCES