1 Introduction

To date the cryptocurrency market has experienced a rapid development, being amongst the fastest growing world financial markets (Almeida & Gonçalves, 2023a; Białkowski, 2020; Fang et al., 2021), and considered as a very popular investment asset among investors (Almeida, 2021; Li et al., 2021). Thus, attracting high attention from the media, regulators, institutional and individual investors, and also as an important and actual topic of academic research (Almeida & Gonçalves, 2022, 2023b, 2023c; Angerer et al., 2020; R. Li et al., 2021).

Due to this increasing popularity and topicality, new empirical evidence is being produced very fast (Angerer et al., 2020; Corbet, Lucey, et al., 2019). However, this literature provides heterogeneous conclusions regarding the cryptocurrency market microstructure. Some studies indicate that the cryptocurrency market is inefficient (Akyildirim et al., 2021; Grobys et al., 2020; Sapkota & Grobys, 2021; Vidal-Tomás et al., 2019a); others, point out the opposite (Alvarez-Ramirez & Rodriguez, 2021; Burggraf & Rudolf, 2020; Caporale & Plastun, 2019; Kaiser, 2019; Lim et al., 2016); further studies, suggest the interconnectedness of the cryptocurrency market (Corbet et al., 2018; Huynh et al., 2018; Luu Duc Huynh, 2019; Shahzad et al., 2021; Tiwari et al., 2020); others the contrary (Kostika & Laopodis, 2020; Sifat et al., 2019); and others still, that the cryptocurrency market is connected to other assets (Kalyvas et al., 2021; Kurka, 2019; Luu et al., 2020; Thampanya et al., 2020); while others suggest the opposite (Corbet et al., 2018; Gil-Alana et al., 2020).

It is thus evident, the great need to synthesize, aggregate, and identify literature gaps on the existing knowledge in cryptocurrencies’ literature (Angerer et al., 2020; Corbet, Lucey, et al., 2019).

Accordingly, we answer the call of Angerer et al. (2020) and Corbet et al., (2019a, 2019b), and develop a systematic literature review on cryptocurrency’s market microstructure. The study’s objective is threefold: 1) to consolidate and map the knowledge of the growing academic literature on cryptocurrency market microstructure; 2) to ease future research by identifying literature gaps; and 3) provide useful research outcomes for investors, academics, researchers, and regulators.

This study contributes to the unconsolidated cryptocurrency literature, with a systematic literature review focused on cryptocurrency market microstructure,Footnote 1 revealing complex network associations, and a detailed integrative analysis. We provide extended insights from previous research (Al-Amri et al., 2019; Almeida, 2021; Amsyar et al., 2020; Angerer et al., 2020; Badawi & Jourdan, 2020; Bariviera & Merediz-Solà, 2021; Corbet, Lucey, et al., 2019; Eigelshoven et al., 2021; Flori, 2019; Hairudin et al., 2020; Haq et al., 2021; Herskind et al., 2020; Huynh et al., 2020a, 2020b; Jalal et al., 2021; Kyriazis et al., 2020; Morisse, 2015; Rahardja et al., 2021; Rejeb et al., 2021; Sarpong, 2022; Silva & Silva, 2022; Sousa et al., 2022) by making use of a powerful and accurate methodology—the bibliographic coupling; also, by only considering ABS academic journals; using a wider keyword scope, and not enforcing any restrictions regarding areas of knowledge, we enhance the contribution of our literature review by allowing the insights of more peripheral studies on the subject, and thus making a more comprehensive and integrative contribution to cryptocurrency literature system than previous studies.

Our findings are of extreme importance for researchers, investors, regulators, and the academic community in general. Our findings provide researchers with structured networking and clear information for research outlets and literature strands for future studies on cryptocurrency investment. Our study also presents valuable insights for crypto investors hel** them to better understand the cryptocurrency market microstructure, and thus hel** them minimizing risks and maximizing returns. Additionally, it delivers insightful information for regulators to effectively regulate cryptocurrencies.

This paper is organized as follows: in Sect. 2, we present the data and the methodology used. In Sect. 3, we perform a quantitative analysis of the literature. Section 4 presents the integrative analysis of the literature and points out some future research venues. Lastly, in Sect. 5, we provide some concluding remarks.

2 Methodology

Our paper presents a systematic review process. Our aim is to cover all cryptocurrency related literature since Satoshi Nakamoto first published his whitepaper in late 2008, up until the present day. With this goal in mind, and following the works of Almeida and Gonçalves, (2022), (2023a), (2023b); Liang, Yang and Wang (2016); Linnenluecke, Marrone and Singh (2020); Jiang, Li and Wang (2021) and Yue et al. (2021), we decided to use the Web of Science database (WoS)Footnote 2 as our main search engine, searching for academic journals between 01-01-2009 and 04-11-2021.

Using a different approach from the ones used by other authors such as Flori (2019a); Kyriazis et al. (2020); Haq et al. (2021); and Jalal, Alon and Paltrinieri (2021), we consider a wider keyword scope, not restricting our research to cryptocurrency market microstructure specific words. Also, we do not enforce any restrictions regarding areas of knowledge. Therefore, using these approaches, we enhance the contribution of our literature review by allowing the insights of more peripheral studies on the subject, and thus making a higher contribution to cryptocurrency literature than previous studies.

We considered the following keywords: “Cryptocurrency”, “Cryptocurrencies”, “Bitcoin”, “Portfolio diversification”, “Investment”, “Investor”, “investors”, “Alternative investment”. Applying the Boolean operators and the wildcard characters to the keywords, the following research equation emerges: “cryptocurrenc* OR Bitcon AND diversification AND portfolio AND invest* AND alternative”.

The quality criterions chosen for this paper follow three main guidelines: 1) the articles must be English-written academic journals; 2) they must address the topic of cryptocurrencies market microstructure from the investor/investment perspective; and 3) the journals must belong to the Academic Journal Guide ABSFootnote 3 (Association of Business Schools) list of 2021. We excluded all other research that did not meet our selection criteria, and as a result of this systematic review process our final sample included 138 articles.

In our analysis we use VOSviewer 1.6.17 software (Almeida & Gonçalves, 2022; Bartolacci et al., 2020; Ding et al., 2014; Galvao et al., 2019; Rialti et al., 2019; Sadeghi Moghadam et al., 2021; van Eck & Waltman, 2017). Different from other cryptocurrency literature analysis (Aysan et al., 2021; Bariviera & Merediz-Solà, 2021; García-Corral et al., 2022; Jalal et al., 2021; Liang et al., 2016; Merediz-Solá & Bariviera, 2019) we opted for the bibliographic coupling option, since it aggregates the articles by clusters based on the number of references they share (Bartolacci et al., 2020; Ding et al., 2014; Galvao et al., 2019; Rialti et al., 2019; Sadeghi Moghadam et al., 2021; van Eck & Waltman, 2017). This option allows for a very powerful and accurate analysis of the literature, since it is based on the number of references where relationships between the articles do not change over time, unlike other options based on the number of citations where the relationships between the articles may change (Bartolacci et al., 2020; Ding et al., 2014; Galvao et al., 2019; Rialti et al., 2019; Sadeghi Moghadam et al., 2021; van Eck & Waltman, 2017). Hence, the bibliographic coupling option in VOSviwer allows for a rigorous replication of our analysis (Bartolacci et al., 2020; Caputo et al., 2019).

Consequently, using the bibliographic coupling, a cryptocurrency market microstructure cluster naturally emerges. Which we analyze in Sect. 3 and 4 of this study.

3 Bibliometric analysis

In our first analysis, Fig. 1 presents the number of publications and citations regarding cryptocurrency market microstructure. 2019 is the year when more articles were published (46) and, also the year with the highest number of citations (1,530). On the other hand, 2017 is the year when less articles were published (2). As expected, recent years present fewer citations, given that older articles have more probabilities of having more citations. Additionally, we evidence a very low correlation (0.07) coefficient between publications and citations over time. These results highlight the novelty of this field of knowledge and, also a growing interest of the academia in the cryptocurrency market.

Fig. 1
figure 1

Citations and publications over time

Full size image

In Fig. 2 we evidence that Bitcoin, efficiency, market, price, and volatility are the five most frequent frequent words (both in article title and abstract) in the analyzed studies.

Fig. 2
figure 2

Titles and Abstracts word cloud

Full size image

3.1 Cryptocurrency market top articles

Table 1 presents the top 10 most cited articles regarding cryptocurrency market microstructure. Corbet et al. (2018) is the most cited article with 348 citations, followed by Katsiampa (2017) with 346 citations, and Demir et al. (2018) with 173 citations.

Table 1 Shows the top 10 articles by number of citations
Full size table

Additionally, we reveal that of the 138 analyzed studies 18.84% were solo-authored and 81.16% were co-authored. The solo-authored studies contributed with 27.76% of citations (1070) and the co-authored with 72.24% (2785). This shows evidence that solo-authored studies present a higher citations per publications ratio (41.15) compared with the co-authored studies (24.86).

3.2 Cryptocurrency market authors network

Table 2 presents the top 10 most cited authors regarding the cryptocurrency market microstructure literature. Paraskevi Katsiampa and Shaen Corbet are the most cited authors in our dataset with 522 and 450 citations respectively. The most productive author is Andrew Urquhart with 8 published articles. Nonetheless, Andrew Meegan is the author that presents the highest citation per publication ratio (348.00).

Table 2 Shows the top 10 authors by number of citations
Full size table

Figure 3 evidence a high structured and complex author’s network. Where Katsiampa, Gozgore, Demir and Lau were the most cited authors in the year 2018. In the beginning of 2019 Vidal-Tomas and Larkin were the most cited authors, however in the end of the same year Urquhart and Corbet took their place as the most cited authors. Later on, in the beginning 2020 the most cited authors were Gorbys and Sapkota, by the end of the year were Chan, Chu and Zhang. Hence, revealing that the most recently cited authors are not present in the general top 10.

Fig. 3
figure 3

Cluster’s network of the most cited authors by year (average publication per year)

Full size image

3.3 Cryptocurrency market journals network

Table 3 evidence the most productive journals regarding cryptocurrency market microstructure studies in our dataset. Economics Letters is the most cited journal with 1,651 citations and is also the journal that has the highest citation per publication ratio in our dataset (78.62). However, in second place with 1,222 citations appears the Finance Research Letters, which is by far the most productive journal in this research field with 48 publications.

Table 3 Shows the top 10 journals by number of citations
Full size table

In Fig. 4 we present the analysis of the most contributive research areas to our field of knowledge, and as expected finance is the research area with more contributions, followed by the economic area. With this analysis we also highlight how other areas of knowledge have contributed to the better understanding of the cryptocurrency market microstructure.

Fig. 4
figure 4

Most contributive research areas

Full size image

Figure 5 highlights a relatively structured network of journals. Regarding average publications per year, the Economic Letters is the most cited Journal around the year 2019. Finance Research Letters and the Research in International Business and Finance are the most cited journals in the year 2020, and in 2021 the journal Annals of Operations Research and the Journal of Futures Markets are the most cited journals in our research field.

Fig. 5
figure 5

Cluster’s network of the most cited journals by year (average publication per year)

Full size image

3.4 Cryptocurrency market institutions network

Table 4 presents the analysis of the most productive institutions to the cryptocurrency market microstructure literature. Sheffield Hallam University is the most cited institution in our dataset with 507 citations, followed by Dublin City University (450) and Trinity College Dublin (420). University Southampton, University Reading, and Bilkent University are the institutions with more published articles in our dataset. Nonetheless, Anglia Ruskin University is the institution that presents the highest citation per publication ratio (184.00). Additionally, we find that the number of publications by university and the ranking THE (Times Higher Education) present a very low correlation of -0.082, evidencing that the number of publications is not positively correlated with the university rank.

Table 4 Shows the top 10 institutions by number of citations
Full size table

Figure 6 shows a highly structured and complex institutions’ network. Regarding average publications per year, Sheffield Hallam University was the most cited institution by the end of 2018. In 2019 the Trinity College Dublin and the Anglia Ruskin University were the most cited institutions. In the end of 2020, the University of Economics Ho Chi Minh City and the Whu Otto Beisheim School of Management were the most cited institutions. Thus, revealing that the most recently cited institutions are not present in the general top 10.

Fig. 6
figure 6

Cluster’s network of the most cited institution by year (average publication per year)

Full size image

3.5 Cryptocurrency market countries network

Table 5 and Fig. 7 show the most productive countries in our research field. England is the country that stands out as the most important country with 1,920 citations and 35 published articles. Turkey (554) is the second most cited country followed by Ireland (450). The country that has the highest citation per publication ratio in our top 10 countries is North Ireland (173.00). However, if we consider the number of citations of a country by the number of universitiesFootnote 4 presented in Table 6, we realize that Ireland presents the highest ratio (56.25) followed by Austria (52.75), Greece (24.00), United Kingdom (17.74), and Turkey (10.26). On the other hand, if we consider the number of publications of a country by the number of universities Ireland also present the highest ratio (0.875) followed by Austria (0.750), Greece (0.625), United Kingdom (0.305), Australia (0.297), and China (0.272). In both analysis the United States of America present the lowest ratios of the top 10 most cited countries.

Table 5 shows the top 10 countries by number of citations
Full size table
Fig. 7
figure 7

Publications by country world map

Full size image
Table 6 shows Countries’ publications and citation scaled by number of universities
Full size table

Figure 8 reveals a highly structured and complex countries’ network. Regarding average publications per year, Australia was the most cited country in the beginning of 2019. However, in 2020 England, Turkey and Ireland appear as the most cited countries in this research field. In the beginning of 2021, China was the most cited country, however by the end of the year, Lebanon, Pakistan, Kosovo, Kenya, and Mexico were the countries with more citations. Consequently, revealing that more recently, the most cited countries are not present in the overall top 10.

Fig. 8
figure 8

Cluster’s network of the most cited country by year (average publication per year)

Full size image

4 Literature findings on cryptocurrency market microstructure

4.1 Is the cryptocurrency market efficient?

4.1.1 Cryptocurrency market efficiency

This literature review addresses the efficiency in the cryptocurrency market. We found evidence supporting the existence of efficiency in the cryptocurrency market. For instance, evidence reveals a significant low volatility premium, indicating that the cryptocurrency market is more efficient than expected (Burggraf & Rudolf, 2020), and becoming more efficient over the years (Alvarez-Ramirez & Rodriguez, 2021). Evidence also shows that the average price delay tends to decrease, implying that the efficiency in the cryptocurrency market is improving (Köchling et al., 2019b).

Nonetheless, evidence also reports that there are heterogeneous patterns of efficiency in the cryptocurrency market (Brauneis & Mestel, 2018), that there are seasonality patterns in cryptocurrency returns supporting a weak-form efficient market hypothesis (Caporale & Plastun, 2019; Kaiser, 2019; Lim et al., 2016). Additionally, it is revealed that there are no significant momentum payoffs in the cryptocurrency market, that the cross-sectional momentum even present negative payoffs, thus supporting the hypothesis that the cryptomarket presents some efficiency (Grobys & Sapkota, 2019). It is also found that the turnover ratio as a measure of liquidity positively affects efficiency, evidencing that cryptocurrencies become more efficient as liquidity decreases (Brauneis & Mestel, 2018).

Regarding the Bitcoin market in specific, we found that it presents signs of efficiency (Wei, 2018a). In fact, there is evidence that Bitcoin is the most efficient cryptocurrency (Brauneis & Mestel, 2018). Future Bitcoin values are unpredictable, fact that is suggested by the presence of a random walk in the returns of cryptocurrencies, which supports the efficient market hypothesis (EMH) (Yaya et al., 2021). Furthermore, evidence shows that the multifractal degree in Bitcoin time series is related to market efficiency in a non-linear manner (Takaishi & Adachi, 2020). Moreover, making use of the Strongly Typed Genetic Programming (STGP)-based learning algorithm, evidence reveals that Bitcoin market populated with high frequency traders (HFTs) at one-minute frequency is efficient (Manahov & Urquhart, 2021).

Further evidence on Bitcoin efficiency reveals that after the introduction of Bitcoin futures, Bitcoin spot market became more efficient (Kim et al., 2020; Köchling et al., 2019a). Thus, Bitcoin futures seem to have affected the informational efficiency in Bitcoin spot market, turning them more informational efficient after the introduction of Bitcoin futures (Shynkevich, 2021). Both Bitcoin spot and future markets have responded to substantial regulatory and fraudulent events, presenting therefore evidence of market efficiency. In addition, it is revealed that information flows and price discovery suffered a reversion, and now they are transmitted from future market to spot markets, possibly by the influx of sophisticated and institutional investors (Akyildirim, Corbet, Katsiampa, et al., 2020).

The evaluation of Bitcoin efficiency during times of market stress highlights that Bitcoin market kept efficient during the COVID-19 pandemic (Wu et al., 2021). The comparison of these results with other assets revealed that during the pandemic Bitcoin was more efficient than Ethereum, Binance Coin, and S&P500; and presented similar efficiency with spot Gold market (Wu et al., 2021). These results highlight that Bitcoin seem to be efficient during times of market stress (Wu et al., 2021).

Additional evidence reports that specific transactions registered on the Bitcoin blockchain are able to predict short-term Bitcoin returns (Ante & Fiedler, 2021). Therefore, evidencing that the Bitcoin market reacts to certain large Bitcoin transfers, pricing in the new information. Thus, these specific large Bitcoin transfers can be considered as relevant aspects in the informational efficiency of Bitcoin, as well as in its market structure (Ante & Fiedler, 2021).

4.1.2 Cryptocurrency market inefficiency

In our literature review we also documented evidence that supports the inefficiency of the cryptocurrency market (Aggarwal et al., 2020; Akyildirim et al., 2021; Caporale et al., 2018; Grobys et al., 2020; Sapkota & Grobys, 2021; Takaishi & Adachi, 2018; Vidal-Tomás et al., 2019a). For instance, evidence suggests that after an event, the information is not immediately fully reflected in the price, thus implying inefficiency (Hashemi Joo et al., 2020). Furthermore, it is highlighted that simple announcements of any type of plan related to a cryptocurrency increases dramatically companies shares value, thus evidencing a new form of information asymmetry, such as the example of KODAKCoin on Kodak stocks (Corbet et al., 2020).

Further evidence reveals presence of a cross-section dependence amongst the most popular cryptocurrencies; evidencing that the cryptomarket is inefficient, specially the top ranked cryptocurrencies (Hu et al., 2019a). It is also revealed that reversal effects are more evident among cryptocurrencies with less liquidity and smaller market capitalization (Kozlowski et al., 2021). Nonetheless, these effects are also evidenced for cryptocurrencies with larger market capitalization and more liquidity; however, at shorter holding periods (Kozlowski et al., 2021). These effects are driven by market inefficiency as well as a compensation for liquidity (Kozlowski et al., 2021). Consequently, it is evident the presence of reversal effects in the cryptocurrency market for daily, weekly, and monthly holding periods (Kozlowski et al., 2021).

In addition, investigating the efficiency in the cryptocurrency market from a structural break perspective, and volatility spillovers, evidence reveals that the cryptocurrency market systematically present structural breaks (Canh et al., 2019). Additionally, it reveals causality effects among large cryptocurrencies, especially in Bitcoin, Litecoin, Ripple, Stellar, Monero, Dash, Bytecoin. Furthermore, it is shown that cryptocurrencies are correlated in a whole with higher volatility spillover among them (Canh et al., 2019).

Further evidence reveals that even after controlling for past volatility and skewness, size and volume, there is evidence of a strong presence of small price bias in cryptocurrency investors. Thus, indicating the presence of inefficiency in the cryptocurrency market (Aloosh & Ouzan, 2020). It is also shown that the cryptocurrency market is weak-form inefficient, and that its inefficiency seems to increase over time (Vidal-Tomás et al., 2019b).

There is also evidence highlighting inefficiency in the specific case of Bitcoin (Aggarwal et al., 2020; Chevapatrakul & Mascia, 2019) For instance, it is revealed that there is presence of dual long memory and structural changes in Bitcoin and Ethereum, suggesting that these markets are inefficient (Mensi, Al-Yahyaee, et al., 2019). Furthermore, it is revealed a delayed response of Bitcoin’s volatility to a volatility shock in Ethereum returns, hence, indicating that the Bitcoin market is inefficient (Beneki et al., 2019).

Additionally, evidence reveals that there are large arbitrage opportunities during Bitcoin market crashes, between the Bitcoin spot and futures market (Hattori & Ishida, 2020). Further evidence reveals that Bitcoin presents information inefficiency, for 115- and 60-min returns. Therefore, evidencing that it is possible to generate abnormal profits for these cryptocurrencies with the use of algorithmic trading strategies at 1 min or 60 min trading (Aslan & Sensoy, 2020). In addition, evidence also reveals that a Bitcoin market populated with high frequency traders (HFTs) at five-minute frequency, reveals to be inefficient (Manahov & Urquhart, 2021). Hence, the higher the frequencies, the lower the pricing efficiency of Bitcoin is (Guégan & Renault, 2021).

In addition, evidence reveals that the daily returns of Bitcoin Investment Trust fund (BIT), whose shares have been trading at a significant premium over its net asset value (NAV), reveal significant positive autocorrelation in shorter lags, thus evidencing that the market for Bitcoin Investment Trust fund (BIT) seem to be inefficient (Shynkevich, 2020).

4.1.3 Adaptive market hypothesis

Other studies evidenced that the inefficiency/efficiency of the cryptocurrency market is time varying (Caporale et al., 2018; Keshari Jena et al., 2020). They reveal that there are still periods of inefficiency that alternate with periods of efficiency, thus supporting the Adaptive Market Hypothesis (AMH) (Chu et al., 2019; Duan et al., 2021; López-Martín et al., 2021; Mensi et al., 2019a, 2019b, 2019c; Noda, 2021; Tran & Leirvik, 2020; Vidal-Tomás et al., 2019b). For instance, evidence reveals that the cryptocurrency market presents multifractality and long-memory properties, thus evidencing inefficiency; however it is revealed that this inefficiency varies across time (Al-Yahyaee et al., 2020; Charfeddine & Maouchi, 2019; Khuntia & Pattanayak, 2020). Moreover, the calendar effects in the cryptocurrency market are also time varying. For instance, Bytecoin appears to be the more inefficient in case of Monday anomalies; Bitcoin presents the January anomalies; Monero the turn-of-the-month (TOTM) effects; and Verge for the Saturday and Sunday (S&S) anomalies (Khuntia & Pattanayak, 2021). Additionally, evidence highlights that when the cryptocurrency market faces a downturn, the inefficiency seems to be higher; however, when the market is upwards the inefficiency level seems to decrease. This fact highlights that the level of inefficiency is time varying (Mensi et al., 2019a, 2019b, 2019c), thus supporting the adaptive market hypothesis (AMH).

4.2 The role of liquidity in the cryptocurrency market

This strand of literature also addresses the liquidity issues in the cryptocurrency market. We found evidence revealing the important role of liquidity in cryptocurrency market efficiency (Wei, 2018a), which is highlighted when in liquid markets, volatility is lower and efficiency is higher, since traders arbitraged away the return predictability (Al-Yahyaee et al., 2020; Wei, 2018a).

Further evidence shows that the liquidity in the cryptocurrency market decreases after negative news announcements, whereas increases after positive news announcements (Yue et al., 2021). Yet, regarding Bitcoin intraday dynamics, evidence highlights that liquidity is highest during the opening times of major global exchanges, and that the markets seem to be more illiquid during the early morning (Eross et al., 2019). Furthermore, liquidity presents a positive and significant effect on Bitcoin informational efficiency, unlike volatility that presents a negative effect (Sensoy, 2019).

It is also shown that Bitcoin returns and volatility present significant positive relationship with liquidity uncertainty. However, on the other hand, trade volume, market capitalization and transaction fees, present a significant negative relationship (Koutmos, 2018b). It is also highlighted that as intraday volatility rises, liquidity uncertainty also rises. Conversely, when trade volume and market capitalization rise, liquidity uncertainty will tend to decrease (Koutmos, 2018b). Nonetheless, the period where liquidity was highest for Bitcoin investors was around 2013 and 2014 (Koutmos, 2018b).

In addition, the reviewed literature present evidence showing that reversal effects are more evident among cryptocurrencies with less liquidity and smaller market capitalization (Kozlowski et al., 2021). These effects are driven by market inefficiency as well as a compensation for liquidity (Kozlowski et al., 2021). It is also evidenced that liquidity factors, contribute to the explanation of excess returns (Lim et al., 2016). Furthermore, the existence of a weak positive correlation between returns and volume suggests that a misinterpretation among investors may cause extreme price movements, and illiquidity in the cryptocurrency markets (Chan et al., 2022).

Other studies reveal that the turnover ratio as a measure of liquidity positively affects efficiency, similarly as size (market capitalization) (Brauneis & Mestel, 2018); that there is a high correlation between delays, liquidity and size (Köchling et al., 2019b); and that returns and liquidity also seem to have some impact on the size effect (Li et al., 2020a, 2020), including the Terra-Luna stablecoin meltdown and the FTX Scandal, and more specifically, to assess the stability of stable coins (Almeida & Gonçalves, 2023b; Grobys & Huynh, 2022; Huynh, 2022).