The transmission and influence mechanism of bitcoin, green bonds, renewable energy, and gold: a quantile connectedness approach

The study examined the connectedness among bitcoin, green bonds (represented by the US S&P Green Bond Index), renewable energy (represented by the OMX Biofuel Index), and gold, utilizing a novel quantile connectedness approach from 14 November 2017 to 30 May 2024. This approach contributes to understanding the transmission mechanisms, influence, and connectedness among the bitcoin, green bond, renewable energy, and gold markets. The result indicates that significant values appear at specific intervals. A significant spike was observed at specific intervals around 2019, mainly due to the trade war between the U.S. and China. A subsequent shock occurred between 2020 and 2021, driven by the COVID-19 pandemic. Moreover, the US credit crisis exacerbated volatility spillovers and financial contagion across markets, worsening these effects in 2023 and intensifying volatility spillovers and financial contagion across markets, exacerbating their outcomes. Additionally, the results suggest that Bitcoin primarily serves as a receiver of shocks. At the same time, the green bond transmits the shocks, and renewable energy and gold have switched between transmission and receiving shock roles during the period. The findings offer valuable insights into sustainable portfolio construction, highlighting that green bonds serve as primary transmitters of shocks and suggest a role as diversification anchors during market stress. Additionally, recognizing Bitcoin as a shock absorber and the shifting roles of renewable energy and gold help investors optimize risk-hedging strategies and enhance portfolio resilience across varying market conditions. This indicates that understanding how these assets correlate across various market scenarios is crucial to maximizing portfolio performance while accounting for sustainability constraints.

1 Introduction

Financial markets have changed substantially in recent years. Technological shifts, a stronger sustainability focus, and other factors all contribute (Hong et al., 2024; Mishra and Zhao, 2021; Xie et al., 2020; Roucham et al., 2025). Four asset classes now occupy a central place in both research and practice: Bitcoin, gold, green bonds, and stocks tied to renewable energy. Each has distinct characteristics. Taken together, they modify portfolio construction by reshaping risk, return, and the scope for diversification (Baker et al., 2022; Brière et al., 2015; Flammer, 2020; Hưng, 2021).

For investors, policymakers, and scholars concerned with portfolio variance optimization, downside protection, and financial stability, the central question is whether co-movement over time dependence is not fixed. It varies with market phases and across investment horizons (Afzal et al., 2021; Hafner and Franses, 2009; Oktavianto et al., 2025; Danila et al., 2021). The quantile time–frequency framework is appropriate here because it traces dependence across quantiles and time scales, thereby offering a granular view of connectedness under different market conditions and time scales (Baruník and Kley, 2015; Li, 2019; Kley et al., 2016).

The recent crisis justifies this focus on dynamics. The global financial crisis of 2007 and the European sovereign debt crisis in 2009 exposed vulnerabilities that called for stricter risk monitoring and more disciplined management. The COVID-19 shock was even more disruptive. It resulted in an abrupt halt to global activity and substantial losses, as reported by Zhou et al. (2022). Lockdowns generated sharp, sometimes short-lived contractions. Price series swung widely. Fundamentals were affected on both the demand and the supply side. Additionally, the rise of cryptocurrencies as a distinct financial asset class presents a unique opportunity to investigate the uncharted aspects of volatility spillover (Fang et al., 2022).

Quantile time–frequency analysis extends classical econometric tools by examining interdependencies at multiple quantile levels and across time (Chatziantoniou et al., 2021; Aguiar-Conraria and Soares, 2013; Vácha and Baruník, 2012; Agyei, 2022). Standard correlation methods tend to emphasize linear relations. By contrast, the quantile time–frequency approach can reveal nonlinear links that become salient during periods of heightened fluctuation (Baruník and Kley, 2015). Given the inherent instability of financial markets, applying this method to Bitcoin, gold, green bonds, and renewable energy stocks can yield more informative evidence on their interdependence, providing direct insights for efficient portfolio decisions.

Each of these asset classes serves a specific function in financial markets. Bitcoin, a decentralized digital asset, has become a speculative tool and a possible inflation hedge (Zhou, 2019; Corbet et al., 2019; Rudolf et al., 2021; Alamaren et al., 2024).

Green bonds fund projects that support environmentally sustainable activities, and their risk-return profile is low but positive, thereby progressively increasing demand for sustainable investments (Taghizadeh-Hesary et al., 2021; Nguyen et al., 2022; Oktavio and Riyanti, 2021; Lefilef, 2024). Green bonds direct funding to projects with explicit environmental objectives. Their risk–return profile is typically low yet positive, a combination that has gradually shifted demand toward sustainability-focused instruments. The label “green bond” covers a wide range of project types and underlying assets. Renewable-energy equities provide equity exposure within this set, reflecting the broader shift toward clean power and offering growth prospects shaped by public policy and technological progress. For portfolio construction under sustainability constraints, understanding how these assets co-move across market states is essential (Janda et al., 2023; Tolliver et al., 2019; Liaw, 2020; Zhang et al., 2022; Ramakrishnan et al., 2023; Gyamerah et al., 2022; Belabbas et al., 2025).

Analyzing quantile time–frequency interactions among Bitcoin, gold, green bonds, and renewable-energy stocks serves several aims. It allows investors to assess diversification capacity. Under stressed conditions in the distribution tails, Bitcoin and gold have at times exhibited weak or negative correlations with green bonds and renewable-energy stocks; in these states, they can operate as hedges. When dependence becomes strongly positive at specific quantiles or horizons, allocation rules should be adjusted to mitigate exposure and manage risk.

Moreover, the policy dimension aligns with these concerns. Regulators and financial institutions require a clear map of cross-asset connections. As digital assets become intertwined with sustainable finance instruments, the system faces new challenges and opportunities. Given the ongoing promotion of green finance by governments and international bodies, a quantile time–frequency lens can inform sturdier trading rules and institutional arrangements that support sustainable growth without destabilizing markets. The web of connections also matters for macroeconomic analysis. Time-varying relationships among Bitcoin, gold, green bonds, and renewable-energy stocks are influenced by inflation pressures, shifts in monetary policy, and the durability of pro-sustainability investor sentiment. This study will provide a more comprehensive perspective on the financial market and economic trends, with examples of evolution.

This study aims to investigate the time-frequency relationships among Bitcoin, gold, green bonds, and renewable energy stocks at different quantiles. By utilizing cutting-edge econometric methods, we aim to illuminate the dynamic nature of these linkages, providing insights for investors, policymakers, and financial scholars alike. This study also contributes to the existing literature on financial markets, their complexities, and the relevance of integrating sustainable finance principles into portfolio management practices for cryptocurrency investments. Furthermore, we can conclude the study question as follows: How do the interconnections between Bitcoin, green bonds, renewable energy, and gold vary across market conditions?

The contributions of this research can be summarized in three key aspects. Firstly, this study establishes an analytical framework integrating Bitcoin, green bonds, renewable energy, and gold. This framework emphasises quantile-level risk spillovers across these markets, offering a novel perspective on the study of market spillover effects. This study focuses on the U.S. market, which has the broadest coverage in the literature to date. That choice strengthens inferences about how risk is transmitted across cryptocurrency, green bonds, renewable-energy equities, and metals; it also clarifies the distinct portfolio functions these assets can serve and provides practical guidance on hedging and diversification for investors (Chang et al., 2018). Methodologically, we introduce a quantile-frequency spillover index that tracks time-varying spillovers and their evolutionary patterns across these markets. Unlike standard spillover measures, the proposed index maps connectedness simultaneously across quantiles (capturing normal, bull, and bear states) and across frequencies that correspond to short, medium, and long horizons (Khalfaoui et al., 2021). The result is a unified view of dependence that is sensitive to both market conditions and investment horizons. These findings inform portfolio construction, enabling investors to refine diversification strategies and enhance risk management across targeted asset classes.

The remainder of this paper is organized as follows. Section 2 reviews the relevant literature on the interactions among Bitcoin, green bonds, renewable energy, and gold, and identifies the research gap that this work seeks to address. Section 3 presents the data and outlines the methodological framework, based on the quantile connectedness approach. Section 4 presents and discusses the empirical results, supported by diagnostic and robustness analyses. Section 5 concludes by summarizing the key findings, drawing policy implications, and suggesting possible directions for future research.

2 Literature review

The recurrence of global financial crises has intensified interest in measuring their effects, tracing how shocks travel between markets, and examining cross-sector interactions within the financial system. Numerous studies utilize various estimation models to investigate quantile connectedness across markets (Sheikh et al., 2024; Ando et al., 2022; Billah et al., 2022; Chatziantoniou et al., 2021). Wu and Huang (2025) investigate the risk transmission mechanism. Patel et al. (2024) highlighted the significance of assessing financial market integration in guiding portfolio diversification, hedging, and asset allocation decisions. COVID-19 affects all sectors around the word Alamaren and Khaliq (2021). Yousaf et al. (2022) discovered that, before the COVID-19 pandemic, there was a volatility spillover from Bitcoin to the energy market; however, during the pandemic’s peak, such spillover was absent. Ji et al. (2019) report that cryptocurrencies can function as a hedge when paired with crude oil. Evidence has since broadened. Patel et al. (2023) argue that sector-specific cryptocurrencies may diversify sectoral equity portfolios, though the magnitude and persistence of the benefit depend on sector conditions. Elsayed et al. (2022) extend the discussion by introducing a policy–pricing channel: uncertainty about cryptocurrency regulation and price formation alters how returns and volatility co-move between cryptocurrency and gold markets. Studying blockchain is important Eyadat et al., 2025. The effect is not static; shifts in perceived regulatory risk or price uncertainty can tighten or loosen that linkage. A technological divide runs through this literature as well. Gallersdörfer et al. (2020) document that leading coins (Bitcoin) use Proof-of-Work consensus, a design that entails substantial energy consumption for both mining and transaction validation. By contrast, so-called green cryptocurrencies adopt more energy efficient mechanisms (Proof-of-Stake), the Ripple Protocol, or the Stellar Protocol—thereby reducing energy intensity. For instance, Ren and Lucey (2022) formalize this divide by classifying coins into “clean” and “dirty” categories based on energy consumption. Two results stand out. First, during certain intervals, clean energy assets can serve as a haven for dirty cryptocurrencies. Second, dynamic conditional correlations between the clean/dirty categories and clean-energy assets are, for the most part, positive over time. The implication follows: neither clean nor dirty coins provide a simple, state-invariant hedge. Ren and Lucey (2022) further indicate that green cryptocurrencies consume less energy precisely because they rely on more efficient consensus algorithms, notably Proof-of-Stake.

Le (2023) examined the interconnectedness between cryptocurrency and renewable energy. During the COVID-19 pandemic and the Russia-Ukraine military conflict, both cryptocurrency and renewable energy acted as net receivers and transmitters, highlighting the significant impact of global uncurtaining on this connection. Additionally, Lee et al. (2023) identified a strong interconnectedness among green bonds, sustainable equities, and cryptocurrency. This suggests that there are limited opportunities for portfolio diversification across these selected markets. He et al. (2025) investigated carbon pricing spillover effects on green asset price volatility based on the Time-Varying Parameter Vector Autoregression TVP-VAR with the Diebold-Yilmaz Spillover Index model. They discovered that the carbon market significantly influences the green bond market, yet it primarily acts as a “risk taker” with overall spillover effects being negative. Duan et al. (2023) explore the relationships among various assets and discover that cryptocurrencies are strongly linked to both traditional and green assets, while clean cryptocurrencies show weak integration with both categories. This suggests that clean cryptocurrencies offer viable opportunities for portfolio diversification. Fernandes et al. (2023) highlight that green bonds can serve as a hedge investment for bonds related to consumer staples, information technology, and real estate sector equities. Huang et al. (2023) find that green bonds maintain a positive correlation with conventional bonds, but a negative correlation with stocks and commodities. The evidence points to diversification gains from portfolios that combine green bonds, equities, and commodities. Huang et al. (2023) further report a consistent hedging role for green assets against Bitcoin, a result that holds irrespective of the pandemic episode.

Fluctuations in energy and metals transmit along supply chains to firms and sectors, shape investor sentiment and expectations, and, in turn, amplify overall market volatility (Liu and Gong, 2020). The literature converges on the presence of spillovers between these two markets, though their strength is context dependent. Using a TVP-VAR framework, Mandacı et al. (2020) document time-varying volatility spillovers across commodities and show pronounced interconnectedness between energy and metals, with peaks during the financial crisis. A different lens yields a complementary picture. Yahya et al. (2020), employing cross-quantilogram and quantile Granger-causality tests, find that links between non-ferrous metals and clean-energy assets are asymmetric, evolve over time, and display marked tail dependence. Taken together, these results indicate that conditional dependence shifts with market states, which has direct implications for risk management and allocation. Furthermore, Chen et al. (2022) emphasized the critical need to consider tail risk transmission between markets, as lower quantile conditions are intrinsically linked to extreme financial risks. By integrating quantile regression with the DY method, they convincingly explored extreme spillovers among fossil energy, clean energy, and metal markets, with their findings indicating that market interconnectedness varies across different quantile levels, and both extremely positive and negative events intensify this interconnectedness.

3 Data and methodology

3.1 Data

This study utilized daily price data from 14 November 2017 to 30 May 2024, sourced from the DataStream database. As shown in Table 1, the analysis focuses on bitcoin, the US green bond (specifically, the S&P Green Bond Index), US renewable energy (represented by the OMX Biofuel Index), and gold. The chosen start date for this data reflects its availability. The sample period encompasses significant economic events, including the China-United States trade war, the COVID-19 pandemic, the Ukraine-Russia conflict, and the US credit crisis. The study period from November 2017 to May 2024 was selected primarily based on data availability and the presence of major global and regional economic events that significantly influenced financial markets. This period encompasses several key shocks, including the U.S.–China trade war (2018–2019), the COVID-19 pandemic (2020–2021), and the U.S. credit and banking crises (2023), which provide a rich context for examining dynamic interconnections and volatility spillovers. As highlighted in recent literature, analyzing connectedness during periods of heightened uncertainty enhances the relevance of adopting a time-varying quantile connectedness specification, which allows the model to capture structural breaks, asymmetric responses, and evolving transmission mechanisms across different market states.

Table 1

Table 1. Data descriptive.

Table 2 presents a statistical comparison of Bitcoin, the US green bond (as represented by the S&P Green Bond Index), US renewable energy (as represented by the OMX Biofuel Index), and Gold. Regarding mean returns, Bitcoin has the highest mean of 0.140811. However, Bitcoin’s standard deviation is significantly higher, at 4.48684, highlighting its higher volatility. The US S&P Green Bond has the lowest mean return at −0.003992, with a standard deviation of 0.400245. Bitcoin has the highest returns with 24.37657. Skewness is negative for all assets, suggesting that negative returns occur more frequently or are more pronounced. The kurtosis, which assesses the prevalence of extreme values, is exceptionally high for Bitcoin 13.38102, indicating potential tail risks associated with this asset. The Jarque-Bera test is utilized to determine whether the return distributions conform to a regular pattern. The results lead us to reject the normality hypothesis for all return series at the 1% significance level. The unit root test and stationarity tests for the study sample indicate that there is no unit root for all samples, and for the KPSS, the variables are stationary.

Table 2

Table 2. Statistics descriptive.

3.2 Methodology

This study uses Ando et al. (2018) suggested quantile connectedness method to examine the quantile connectedness approach propagation. The quantile connectedness approach provides several methodological advantages over conventional connectedness frameworks. Unlike traditional mean-based models, the quantile connectedness method captures dependence structures across different parts of the return distribution (lower, median, and upper quantiles). This enables a more comprehensive analysis of how shock transmission varies under extreme market conditions, such as high volatility or downturns, thereby revealing nonlinear and asymmetric connectedness patterns that conventional models often overlook. Furthermore, this approach enhances the robustness of the empirical results by accommodating tail dependencies, heterogeneous responses of assets to shocks, and time-varying risk transmission mechanisms, offering more profound insights into systemic risk and market interdependence. An estimate of the quantile vector autoregression, referred to as QVAR(p), is conducted prior to calculating any association metrics. This estimation is performed as follows on Equation 1:

$Xt=\delta \left(\tau \right)+{\displaystyle \sum _{j=1}^r}{\mathrm{\Phi }}_j\left(\tau \right)x_{t-j}+u_t\left(\tau \right)(1)$

$Xt$ and $Xt-j$ are $m\ast 1$ dimensional endogenous variable vectors consisting of the first differenced, $\tau$ is between 0, 1, and represents the quantile of return, Indicate the Quantile VAR models lag length, $\delta \left(\tau \right)$ is $m\ast 1$ dimensional conditional mean vector, ${\mathrm{\Phi }}_j$ is $m\ast m$ dimensional quantile VAR coefficient matrix, while the illustration depicts the $m\ast 1$ error vector, which is associated $m\ast m$ dimensional variance-covariance matrix $\sum \left(\tau \right)$, denoted as $u_t\left(\tau \right)$. This study applied

We apply Wold’s theorem to transform the quantile VAR(p) to the quantile VMA ($\infty$) representation into QVMA ($\infty$) on Equation 2:

$Xt=\delta \left(\tau \right)+{\displaystyle \sum _{j=1}^r}{\mathrm{\Phi }}_j\left(\tau \right)x_{t-j}+u_t\left(\tau \right)=\delta \left(\tau \right)+{\displaystyle \sum _{j=1}^{\infty }}{\gamma }_i\left(\tau \right)u_t\left(\tau \right)(2)$

The impact of a shock in variable j on variable i is illustrated by the A-step forward Generalized Forecast Error Variance Decomposition (GFEVD) introduced by Koop et al. (1996) and Pesaran and Shin (1998) as follows on Equations 3, 4:

${\gamma }_{ij}^c\left(A\right)=\frac{\sum {\left(\tau \right)}_{ii}^{-1}{\sum }_{a=0}^{A-1}{\left(e_i^{\prime }{\gamma }_a\left(\tau \right)\sum \left(\tau \right)e_j\right)}^2}{{\sum }_{a=0}^{A-1}\left(e_i^{\prime }{\gamma }_a\left(\tau \right)\sum \left(\tau \right){\gamma }_a{\left(\tau \right)}^{\prime }{e}_j\right)}(3)$

${\check{\gamma }}_{ij}^c\left(A\right)=\frac{{\gamma }_{ij}^c\left(A\right)}{{\displaystyle {\sum }_{j=1}^m}{\partial }_{ij}^d\left(A\right)}(4)$

At the $ith$ position, $e_i$ indicate a zero vector with unity. Normalization contributes to two equalities, which are as follows on Equation 5:

${\displaystyle \sum _{j=1}^m}{\check{\gamma }}_{ij}^c\left(A\right)=1and{\displaystyle \sum _{i,j=1}^m}{\check{\gamma }}_{ij}^c\left(A\right)=m(5)$

To derive the information considering the overall impact that variable i has on all other variables j, the total directional interconnectedness To others is denoted on Equation 6:

$C_{i\to j}^d\left(A\right)={\displaystyle \sum _{j=1,i\ne j}^m}{\check{\gamma }}_{ij}^c\left(A\right)(6)$

The valuation of the shocks of variables j on variable i is fixed by the overall directional interconnectedness From other variables is denoted as follows on Equation 7:

$C_{i\leftarrow j}^d\left(A\right)={\displaystyle \sum _{j=1,i\ne j}^m}{\check{\gamma }}_{ji}^c\left(A\right)(7)$

The net total interconnectedness is computed by deducing the directional connectedness TO and FROM other variables. This estimate on Equation 8 that shows the net impact that variable i exerts on the overall network being analyzed.

$C_i^d\left(A\right)=C_{i\to j}^d\left(A\right)-C_{i\to j}^d\left(A\right)(8)$

The modified Total connectedness Index (TCI), presented by Chatziantoniou and Gabauer (2021) and evolved by Gabauer (2021), serves as the final measure of connectedness on Equation 9. This index ranges between 0 and 1, offering a standardized metric for evaluating the degree of inter-association within the network.

$TCI\left(A\right)=\frac{{\sum }_{i,j=1,i\ne j}^m{\check{\gamma }}_{ij}^c\left(A\right)}{m-1}(9)$

The total interconnectedness index is commonly utilized to indicate market risk, as a higher total connectedness index value is associated with increased network interconnectedness.

4 Findings and discussion

This study examines the temporal and frequency relationships among Bitcoin, the US Green Bond Index, the Renewable Energy Index, and gold. Utilizing the methodology established by Chatziantoniou et al. (2022), we implement the QVAR approach with one lag, as dictated by the Bayesian Information Criterion. The study analysis employs a 200-day rolling window along with a 100-step generalized forecast error variance decomposition to assess connectedness at the mean, median (Q = 0.50), lower quantile (Q = 0.05), and upper quantile (Q = 0.95).

Figure 1 illustrates the results for total dynamic connectedness, where warmer shades on the plot signify higher levels of connectedness. The data reveals that connectedness is particularly strong for significant negative return changes (below the 20th percentile) and notable positive changes (above the 80th percentile), indicating a symmetric impact. Furthermore, the 50th percentile reflects the average connectedness over the entire period. Notably, significant values appear at specific intervals, including before 2020, between 2020 and 2020, and in 2023. This indicates a somewhat cyclical pattern of interconnectedness over time, shaped by various events. Notably, a significant spike was observed at specific intervals around 2019, largely due to the trade war between the U.S. and China. A subsequent shock occurred between 2020 and 2021, driven by the COVID-19 pandemic, which resulted in widespread closures across all social and economic life, affecting stock and cryptocurrency prices. Spillover effects receded during 2021; a pattern widely linked to the vaccine announcements in 2020 Q4. By contrast, events in 2023 pulled in the opposite direction. The failure of Silicon Valley Bank in March and the acute stress at Credit Suisse amplified cross-market volatility spillovers and heightened financial contagion, deepening the transmission of shocks. The evidence therefore points to asymmetric transmission: dependence structures differ between periods of strongly positive returns and drawdowns, with systemic risk and market uncertainty rising sharply in crisis states relative to expansionary phases.

Figure 1

Figure 1. Total dynamic quantile connectivity between variables.

To probe these dynamics, a novel quantile–frequency association method is employed. The approach examines propagation mechanisms simultaneously across frequencies and quantiles, yielding connectedness measures that vary along two axes. First, it traces how dependence changes with frequency at a fixed quantile. Second, it tracks how dependence shifts across quantiles at a given frequency. This two-dimensional view offers a more granular account of market co-movements and their evolution over time.

Notably, the interconnection among market entities—often referred to as market intersection—is most pronounced at the extreme lower and upper quantiles along the horizontal axis, suggesting that these outlier ranges are crucial for comprehending the underlying relationships and influences within the market system. By concentrating on these extremes, we can gain valuable insights into the behavior of asset returns and their correlations during periods of significant price movements. This heatmap depicts the net total directional connectedness across various quantiles. It visualizes the market’s behavior in terms of net transmission and reception. The color gradient ranges from deep blue, signifying net receipt, to deep red, indicating a net contributor.

As illustrated in Figure 2, Bitcoin has consistently been a net recipient of shocks throughout the analyzed period, indicating that shock absorbers are positioned at higher and lower quantiles. During significant events such as the US-China trade war, the COVID-19 pandemic, the Ukrainian-Russian War, the collapse of Silicon Valley Bank, and the crisis at Credit Suisse, Bitcoin has demonstrated sensitivity to global crises, functioning as a “net recipient of shocks” during incidents like the trade war and geopolitical tensions. This behavior suggests that Bitcoin acts more as a speculative asset than a reliable value store amid uncertainty. Despite pronounced volatility, Bitcoin appears subject to bounds on price deviations during turbulent episodes. This behavior is consistent with Khalfaoui et al. (2022).

Figure 2

Figure 2. Net total directional association for Bitcoin.

Figure 3 indicates that the U.S. S&P Green Bond Index is a persistent net transmitter of shocks over the sample. Net sending arises at both upper and lower quantiles. The index is highly sensitive to global stress: during trade disputes and geopolitical tensions it behaves as a “net sender of shocks,” pushing disturbances into other asset classes rather than absorbing them. In other words, green bonds have, in crisis windows, propagated volatility and contributed to instability instead of providing insulation. When markets are exceptionally calm, they may still deliver diversification benefits—an observation aligned with Naeem et al. (2022).

Figure 3

Figure 3. Net total directional connectedness for the U.S. S&P Green Bond Index.

Figure 4 shows a mixed pattern for renewable energy. The index alternates between net transmitting and net receiving roles. During the COVID-19 period, transmission intensifies in the tails—net sending at high and low quantiles—while, on balance across the full horizon, the renewable energy index tends to act as a net receiver. The renewable energy index plays two important roles in the financial system—it can be a source of volatility during certain crises while absorbing external shocks in more stable times. For example, the renewable energy industry likely faced unique challenges in the pandemic, from supply chain disruptions to policy changes and shifts in demand. This could have also contributed to inducing increased instability in other markets. Meantime, over the long term, renewable energy performance is more correlated to wider economic conditions, suggesting its sensitivity to outside factors such as energy prices, the regulatory environment, and investor risk attitude. This duality reflects the shifting relationship of renewable energy with globalizing economic dynamics.

Figure 4

Figure 4. Net total directional connectedness for the renewable energy index.

Figure 5 illustrates the total directional connectedness of gold, which confirms its overwhelming impact on other assets in the long run. There have been different periods in which gold played the role of both transmitter and receiver. It exhibited a net receiving position during the COVID-19 pandemic at higher and lower quantiles, but otherwise, it predominantly served as a net transmitter. The change in gold total directional connectedness reflects gold’s dual character as a safekeeping asset and a commodity driven by macroeconomic variables. In addition, gold acted as a net receiver during the pandemic where increased market uncertainty and gold’s ability to absorb shocks in other assets were reflected. On the other hand, during periods of stability or inflation, gold tends to act on a net transmitter basis, influencing other asset classes as a hedge against systemic and inflation volatility. Understanding these trends enable investors to strengthen portfolio strategies and effective risk management. This analysis ultimately bring forth gold’s dynamic interlinking in the financial system.

Figure 5

Figure 5. Net total directional association for gold.

5 Conclusion

This study examines the connectedness among Bitcoin, green bonds (U.S. S&P Green Bond Index), renewable energy (OMX Biofuel Index), and gold using a novel quantile-connectedness approach over 14 November 2017–30 May 2024. Mapping connectedness across quantiles and horizons matters for practice. It enables investors and policymakers to gauge contagion risk, identify transmission mechanisms between markets, and detect conditions under which diversification remains feasible. The same lens also speaks to market efficiency by revealing how information and shocks propagate across assets with different fundamentals. In turn, these insights inform risk-management design oriented toward more stable, sustainability-aligned allocation across Bitcoin, green bonds, renewable energy, and gold.

The time profile is not uniform. Around 2019, connectedness surged, with trade tensions between the United States and China as a principal driver. During 2020–2021, widespread pandemic shutdowns disrupted pricing in equities and cryptocurrencies, and spillovers shifted accordingly. A moderation followed in 2021, consistent with the late-2020 vaccination announcements and the resulting improvement in expectations. The pattern reversed in 2023: the Silicon Valley Bank failure and acute stress at Credit Suisse elevated volatility and rekindled risk transmission across markets.

Taken together, the results indicate asymmetric shock propagation. Routes of transmission differ between strong-return phases and drawdowns, and systemic risk rises markedly in crisis windows relative to periods of exuberant performance. For portfolio construction and oversight, the implication is straightforward but conditional: opportunities for risk diversification exist, but they are state-dependent. They must be evaluated with respect to both the quantile (market state) and the frequency (investment horizon) captured by the proposed approach.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

AA: Data curation, Methodology, Writing – original draft, Writing – review and editing, Investigation, Formal Analysis, Funding acquisition, Visualization, Supervision. AL: Data curation, Supervision, Methodology, Writing – review and editing. TK: Visualization, Writing – review and editing, Validation, Formal Analysis. SB: Resources, Writing – review and editing, Investigation. OZ: Software, Conceptualization, Methodology, Data curation, Investigation, Validation, Resources, Formal Analysis, Funding acquisition, Visualization, Project administration, Writing – review and editing, Supervision.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This scientific research was funded by the Deanship of Scientific Research, Vice Presidency of Graduate Studies and Scientific Research, grant no. KFU253920, King Faisal University.

Acknowledgements

We are grateful to have received financial assistance from the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, under Grant KFU253920.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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