Adoption of cryptocurrencies by financial institutions: challenges and opportunities in the digital economy

In recent years, the development of China’s digital economy has been an important part of the development of the national economy, and its position in the national economy has been further emphasized [1]. Finance is the core of the modern economy; the economy determines finance, and the level of economic development determines the level of financial development [2-3]. In the current era of the digital economy, modern information technology, network technology and traditional finance are deeply integrated, and financial science and technology have become a trend. Economic digitization and financial digitization have been the general trend, and money, as the core element of finance, has also developed from symbolization to informatization, with the emergence of various currency forms such as e-money, virtual currency and digital currency. In this context, the study of legal tender digital currency1 has strong practical significance and theoretical significance [4-6]. The pursuit of digital currency coin value stability is not the value of the cryptocurrency itself but rather the stability of the coin value and, thus, its use in the transactions of other cryptocurrencies or even the transactions of the real economy [7-8]. Because transactions are conducted online, this brings many conveniences to transactions in the real economy, such as low latency, low fees, no need to go through traditional banking channels, and physical funds can be transferred to any corner of the world [9-11].

Digital cryptocurrencies with quantity caps as mining proceeds and all pre-defined currencies will be mined out in the future, and in the end, the mining behavior can only be incentivized by relying solely on transaction fees [12-13]. Some digital cryptocurrencies with no cap on the amount of cryptocurrencies that can be mined through mining incentives will continue to put new cryptocurrencies into the market for that cryptocurrency, all of which circulate in the marketplace without an effective way to manage the circulation of the currency [14-16]. The problem highlighted in the Bitcoin whitepaper as one that needs to be solved is the dual payment problem, also known as the “double-spend” problem, where the digital nature of the currency is utilized to make payments using the “same amount of money” two or more times. In traditional financial and monetary systems, cash is a physical entity that naturally avoids double payments. The main way to accomplish the double spend attack is the 51% arithmetic attack [17-20].

This paper firstly discusses the conceptual issues of cryptocurrency, its development history, its application in international trade settlement and its risks, and further clarifies the opportunities and challenges that cryptocurrency brings to the financial market by sorting out these issues. Then, it quantitatively analyzes the development of the cryptocurrency market and the market share of cryptocurrencies. Finally, statistical analyses are selected and conducted to explain the reasons for sample selection and data sources, and Pearson correlation analysis and Granger causality test are conducted on the research sample variables to explore the mechanism of the association between the cryptocurrency market and the financial market. Taking the risk of the New Crown Pneumonia epidemic as an example, the impact of emergencies on the digital economy, the overall financial market, and the specific cryptocurrency market is analysed in detail.

2

Opportunities and challenges of cryptocurrencies

2.1

Development history of cryptocurrencies

Bitcoin is a digital cryptocurrency that works like any other digital cryptocurrency, constructing a distributed ledger system that proposes a decentralised electronic currency based on cryptographic algorithms, which are characterised by anonymity and cannot be tracked. It was found that in order to ensure the openness and scarcity of Bitcoin, the Bitcoin system provides an open source code protocol that enables the Bitcoin system to work well. Bitcoin’s innovative issuance and operation model has had a significant impact on the current monetary system.

The evolution of cryptocurrencies is illustrated in Figure 1. Global Stablecoin is a privately owned cryptocurrency issued by technology giants and is essentially a cryptocurrency with an “anchoring” property, the goal of which is to anchor an off-chain asset and maintain the same additive value as it. Global Stablecoin has the potential to be widely adopted due to the wide audience of tech giants. Global Stablecoin differs from the Chinese tech giant’s third-party payments in that it is not the same as traditional third-party payment platforms and uses cryptocurrencies for payments. The central bank cryptocurrency is conceived in such a way that although Bitcoin has brought much impact, the distributed ledger and blockchain technology in it has a lot of application value, and the central bank cryptocurrency can adopt all the features of Bitcoin except decentralisation, which not only contributes to the stability of the currency value and improves the regulatory capacity of the central bank, but also solves the problem of the zero-interest-rate floor. The Bank of England defined cryptocurrencies in 2016 as universally recognized and interest-bearing central bank liabilities implemented through a distributed ledger.

2.2

Cryptocurrencies and international trade settlements

The introduction of cryptocurrencies will facilitate the improvement of existing cross-border payment systems. In the digital age, the United States will likely dominate the digital stablecoin and central bank cryptocurrency space, reinforcing the dollar’s international leadership position. China should actively support the participation of Bigtech companies in cross-border payments competition and provide fair and effective regulation based on the principle of “same business, same risk, same regulation”. Central bank cryptocurrencies have great potential for cross-border payments, and their peer-to-peer payment behavior can effectively improve the shortcomings of the current time-consuming and costly international payments and settlements. Moreover, the construction of cross-border payment systems based on central bank cryptocurrencies can facilitate the transformation of the highly centralised international trade settlement system, which was previously controlled exclusively by developed countries, into a moderately centralised international trade settlement system in which developing countries can participate on an equal footing and freely. Challenges to the US dollar-dominated international monetary system have begun with cross-border payments, from the substitution of traditional models by some countries to the rise of cryptocurrencies and their application in international trade settlement. The promotion of digital RMB will facilitate the process of RMB internationalisation and improve the traditional international monetary system. The central bank cryptocurrency is a product of financial technology innovation, especially the development of blockchain, and the central bank cryptocurrency will become a key tool for the reform of the international monetary system and the governance of the global financial system, and the digital RMB issued by China can play an important role in the payment and settlement of international trade, cross-border capital flows and global industrial investment, and can prompt the digital RMB to become an important international reserve currency. The issuance, circulation and internationalisation of sovereign cryptocurrencies represented by the digital RMB will change the deficiencies and shortcomings of the traditional US dollar-dominated international monetary system and promote the formation of a new international monetary system that is fairer and more efficient.

2.3

Cryptocurrency Risk Avoidance Strategies

The global liquidity of Bitcoin and the use of the Proof-of-Work mechanism (POW algorithm) have resulted in the price of Bitcoin being susceptible to a wider range of factors. Since Bitcoin’s inception, it has experienced a sharp rise in price and rapid growth, as well as a great deal of controversy, even involving law enforcement agencies, with various claims being made about Bitcoin, claiming that it is characterised as a bubble that could burst at any time. The usual economic factors, such as supply and demand and price levels of Bitcoin have a significant impact on the long-term development of Bitcoin, and the regulatory attitudes of individual countries have a decisive influence on the development of Bitcoin.

China’s attitude towards the issuance of crypto-cryptocurrencies is positive. A dedicated research team was set up in 2014 to conduct in-depth research on various topics, such as the issuance framework, liquidity, technology, etc., on 20 January 2016 at the People’s Bank of China Cryptocurrency Symposium. Currently, the Bank of England has indicated that it is considering whether cryptocurrencies should be introduced. Cryptocurrencies can help reduce the incidence of money laundering, tax evasion, and other offenses. Most financial institutions have many internal ledgers. If a national unified ledger can be established, every transaction behavior can be traced back, and tax evasion and money laundering will be greatly curbed. The function of automatic tax deduction will be added in due course. Cryptocurrencies have the anonymity of paper money, the traceability of electronic money, and, at the same time, they are programmable currencies. If blockchain technology is used, it can greatly reduce the cost of use, have more efficient management, and be more secure. The problem of excessive price volatility in cryptocurrencies can be solved by a central bank issuing cryptocurrencies. The cost of issuance of currency and regulatory costs would greatly decrease.

However, issuing cryptocurrencies is difficult and problematic. The normal cycle should be 3 to 5 years, and it requires repeated and full justification before it can be introduced to the market. At present, it is still in the primary stage: 1)

Technical aspects: how to solve the conflict between the centralised credit of the central bank and the decentralisation of the blockchain, and make the combination of the two and many other issues still need to be studied.

2)

Legal aspects: it will take time to formulate and improve relevant forward-looking financial laws.

3)

Financial system: how to match and integrate with the traditional financial system, while there is also the question of whether the stability of the financial system can be maintained.

4)

The customer side: raising awareness and acceptance of cryptocurrency in order to popularise it and bring its true value into play.

3

Research on economic effects and risk analysis of cryptocurrencies

3.1

Current Development of Cryptocurrency Market

The cryptocurrency market can be traced back to the very beginning of Bitcoin, which came out in 2008. In turn, many cryptocurrencies based on blockchain technology were born, and the cryptocurrency market in 2023 is shown in Figure 2, which shows the overall market capitalisation of the cryptocurrency market in Figure 2(a) and the trading volume in Figure 2(b). It entered an explosive phase of development in 2017. Since then, the cryptocurrency boom has attracted a large amount of money into the cryptocurrency market, driving the development of various types of cryptocurrencies. The daily trading volume of the cryptocurrency market had reached $450 billion, and the overall market capitalisation had reached about $3 trillion.

The cryptocurrency market has had the following very distinctive features since its inception: 1)

Explosive increase in market capitalisation: The total market capitalisation of the cryptocurrency market has experienced significant growth in recent years. Despite fluctuations, the overall trend is upward.

2)

Institutional investor participation: the cryptocurrency market has attracted an increasing number of institutional investors in recent years.

3)

More Diverse Markets: In the beginning, the cryptocurrency market was almost exclusively Bitcoin available for investment and trading, and then more and more cryptocurrency projects were created. More and more cryptocurrency projects have been created, providing investors with more investment projects and trading mediums to choose from.

4)

The evolution of regulations: various countries and regions are targeting digital.

Cryptocurrency market share is shown in Figure 3, with Bitcoin having the largest market share at 39.8 per cent, followed by other cryptocurrencies’ share at 20.4 per cent. The other cryptocurrency that is currently doing well would be Ether, which currently has almost half the market share of Bitcoin. The booming cryptocurrency market has also fueled the rapid development of blockchain technology. Blockchain has elements such as distributed ledgers, smart contracts, and other components. Distributed bookkeeping allows data to be stored in a database maintained by multiple computers in the system in a decentralized manner, so that even if one of the computers is damaged, the data remains secure. This idea is a better direction for data security in the future. However, the defeat is very detrimental to the regulation of the economy due to the peer-to-peer transaction method used by blockchain. Cryptocurrencies are experiencing more drastic changes in value, and their protocols are being slowly modified to ensure that the entire system can continue to function. The prospects for targeting this sector are still not very clear.

3.2

Analysis of the economic effects of cryptocurrencies

3.2.1

Methods of correlation analysis

In this paper, Pearson’s correlation coefficient is used to calculate the correlation between the sample variables, with coefficients taking values ranging from -1 to 1, greater than zero indicating a positive correlation and less than zero indicating a negative correlation.

Correlation analysis (GRA) is an effective statistical analysis tool that can help to better understand the structure, function, behaviour, and operation of a system, as well as their interrelationships, and can help to identify more accurately the key factors that influence the development of a system.

Correlation analysis has a wider field of application than traditional mathematical and statistical analysis techniques, such as regression analysis, analysis of variance and principal component analysis, and can help to better understand and predict future trends. It solves the problems associated with the use of mathematical statistical analysis by adjusting the parameters of the analysis according to the size, characteristics and other factors of the sample and by reducing the amount of computation so that the results of qualitative analysis do not deviate from the results of quantitative analysis. The research process can be made more concise and clearer because it doesn’t require a lot of sample data. Through correlation analysis, not only can consistency between data be ensured, but also the degree of influence of various factors on the research object can be clearly demonstrated. Through the methodology of this paper, it is possible to accurately select the most representative few of the many factors affecting property prices, and further explore the mechanism of how they affect the changes in property prices.

The steps of correlation calculation are as follows: 1)

Determine the reference and comparison sequences.

Set the reference sequence to X₀ = {x₀(k), k = 1, 2, 3…, n}.

Set the comparison sequence to X_i = {x_i(k), k = 1, 2, 3…, n; i = 1, 2, 3…, m}.

2)

Data standardisation treatment

In order to eliminate the influence of the magnitude, it is necessary to standardise the original data, and the processing formula is: (1) $X_{i}^{'} = \frac{X_{i}}{X_{1}} = {X_{i}^{'} (1), X_{i}^{'} (2), X_{i}^{'} (3) \dots X_{i}^{'} (k)}$ $X_i^\prime = \frac{{{X_i}}}{{{X_1}}} = \{ X_i^\prime (1),X_i^\prime (2),X_i^\prime (3) \cdots X_i^\prime (k)\}$

3)

Find the absolute value, maximum difference and minimum difference. The formula calculates the absolute value: (2) $Δ_{i} = | X_{0}^{'} (k) - X_{i}^{'} (k) |, k = 1, 2, 3 \dots n$ ${\Delta _i} = |X_0^\prime (k) - X_i^\prime (k)|,k = 1,2,3 \cdots n$

The maximum value of the absolute value is the maximum difference and the minimum value is the minimum difference. 4)

Find the correlation coefficient, calculated as: (3) $ξ_{i} (k) = \frac{\min_{i} \min_{k} Δ_{i} (k) + α \max_{i} \max_{k} Δ_{i} (k)}{Δ_{i} (k) + α \max_{i} \max_{k} Δ_{i} (k)}, 0 < α < 1$ ${\xi _i}(k) = \frac{{{{\min }_i}{{\min }_k}{\Delta _i}(k) + \alpha {{\max }_i}{{\max }_k}{\Delta _i}(k)}}{{{\Delta _i}(k) + \alpha {{\max }_i}{{\max }_k}{\Delta _i}(k)}},\:0 < \alpha < 1$

Style: $* m i n_{i} \min_{k} Δ_{i} (k) - minimum difference: \max_{i} \max_{k} Δ_{i} (k) - maximum difference .$ $*mi{n_i}{\min _k}{\Delta _i}(k) \quad{\text -} \hbox{minimum difference: }\quad\max_i\max_k \Delta_i(k)\quad{\text -} \hbox{maximum difference.}$

5)

From the table of correlation coefficients, the degree of correlation is calculated according to formula (4), which is calculated as: (4) $r_{i} = \frac{1}{n} \sum_{k = 1}^{n} ξ_{i} (k)$ ${r_i} = \frac{1}{n}\sum\limits_{k = 1}^n {{\xi _i}} (k)$

The most commonly used correlation coefficient analysis method Pearson correlation, also called Pearson rank correlation, Pearson correlation is both an upgrade of the Euclidean distance and an improvement of the cosine distance when dimensionality is missing. It is usually used to measure the similarity of the linear relationship between two variables, and the range of Pearson’s correlation coefficient values is usually {-1,1}, and its formula can be expressed in equations (5) and (6): (5) $cov (x, y) = \frac{\sum_{i = 1}^{n} (x_{i} - x_{e}) (y_{i} - y_{e})}{n - 1}$ $\operatorname{cov} (x,y) = \frac{{\sum\limits_{i = 1}^n {({x_i} - {x_e})} ({y_i} - {y_e})}}{{n - 1}}$ (6) $ρ_{x, y} = \frac{cov (x, y)}{σ_{x}, σ_{y}}$ ${\rho _{x,y}} = \frac{{\operatorname{cov} (x,y)}}{{{\sigma _x},{\sigma _y}}}$

Where x, y is the time series of the two selected reference variables, cov(x, y) denotes the covariance of the sequence x with the reference sequence y, x_e, y_e is the mean of the x, y sequence, respectively, and i represents the time series, each corresponding to an actual value.

3.2.2

Results of correlation coefficient analyses

In this section, Bitcoin, Litecoin, Ether, Ripple, and Tera, which are the top-ranked cryptocurrency markets in terms of market capitalisation, are selected, and the sample period of the empirical part spans from 10 October 2016 to 10 February 2024. Because cryptocurrencies trade seven consecutive days per week, while traditional financial markets are closed on weekends and holidays, redundant trading data in cryptocurrencies is eliminated to make the time series more compatible, and each time series ultimately contains 1,710 daily-frequency trading data. The results of the correlation coefficients between cryptocurrencies and the financial market stress index are shown in Figure 4, which shows that the four traditional non-stable coins, Bitcoin, Litecoin, Ether, and Ripple, have a strong positive correlation, while the stablecoin USDT exhibits a weak negative correlation with the four non-stable coins. From the results of the cryptocurrency-financial market correlation, the four traditional non-stable coins show a weak negative correlation with the Chinese financial market. Bitcoin, lite coin, Ethereum, and ripple have correlation coefficients of -0.0138, -0.0225, -0.0114, and -0.0143 with the CFSI index, respectively, and the stablecoin, USDT, has a weak positive correlation with the CFSI index, with a The correlation coefficient is 0.0051.

The results of the Granger causality test of the financial market stress index on cryptocurrencies are shown in Table 1. None of the Chinese credit market stress indices are Granger causes of the five cryptocurrencies. China’s foreign exchange market stress index is a Granger cause of ethereum price volatility at the 1% significance level. None of the Chinese stock market stress indices are Granger causes of the five cryptocurrencies. The Chinese Bond Market Stress Index is a Granger cause for Bitcoin (3.283***), Ripple (3.272**), and Tide (3.427***), suggesting that cryptocurrencies have begun to be sensitive in recent years to the reactions of macroeconomic variables such as interest rates. *, ** and *** represent significant at the 10%, 5% and 1% levels, respectively.

Table 1.

The granger causality test of the encrypted currency

	BTC	LTC	ETH	XRP	USDT
CFSI	0.512	0.254	1.200	0.801	0.305
CFSI_credit	0.754	0.030	0.520	2.074	0.814
CFSI_stock	0.485	0.392	0.914	0.534	0.322
CFSI_exchange	0.802	0.402	5.051	1.173	0.648
CFSI_bond	3.283**	1.504	0.735	3.272**	3.427***

3.3

Risk analysis of cryptocurrencies

Non-statutory digital currencies are subject to greater market risk, and major emergencies can have a significant impact on the price of speculative non-statutory digital currencies. The complexity lies in the impact of various public events and their responses on the global economy and financial markets. The outbreaks of major public health events such as SARS in 2003, Influenza A (H1N1) in 2009 and the new Crown Pneumonia in 2020 (COVID-19) have all had a major impact on the global economy and way of life.

The impact of the Covi epidemic on the digital economy, the overall financial market, and the specific fixed cryptocurrency market is analysed, and the main mechanisms of the impact of the Covi epidemic on the fixed cryptocurrency market are proposed. From the descriptive statistics analysis as shown in Table 2, the average value of the New Crown Pneumonia Epidemic (Covi) composite index is 107.122, the average value of the Chinese New Crown Pneumonia Epidemic Index (Covic) is 108.14, the average value of the Overseas New Crown Pneumonia Epidemic Index (Covio) is 107.217, the average value of the Cryptocurrency Price Index (Dcpi) is 100.274, the average value of the US Dollar against the Renminbi ( Edyi). The foreign exchange rate price index (Eri) averaged 98.927, the currency-based futures price index (Fpi) averaged 98.584, the major currency exchange rate price index (Fepi) averaged 95.528, the gold futures price index (Fgpi) averaged 100.118 and the cryptocurrency futures price index (Fdpi) averaged 100.084.

Table 2.

Descriptive statistics

Variable	Observed quantity	Mean	Standard deviation	The ADF test of the original sequence	The ADF test of the first order difference
Covi	190	107.122	10.735	-3.120**
Covic	190	108.14	21.174	-2.532	-12.342***
Covio	190	107.217	9.240	-4.242***
Dcpi	190	100.274	17.894	-1.884	-9.645***
Eri	190	98.927	5.325	-7.263***
Edyi	190	100.053	1.023	-1.604	-9.763***
Fpi	190	98.584	6.854	-3.452***
Fepi	190	95.528	24.839	-9.874***
Feti	190	90.415	1.125	-5.032***
Fgpi	190	100.118	90.253	-1.652	-8.345***
Fdpi	190	100.084	57.954	-1.932	-10.028***

The VAR model is used to estimate the autocorrelation and its interactions between the development of the New Crown Pneumonia epidemic and the fixed cryptocurrency market, to determine the lag order and sign of the VAR model, and to test the relationship between the impact of the New Crown Pneumonia epidemic shocks on the fixed cryptocurrency market by using the impulse response and the analysis of variance (ANOVA) methods.

The VAR autovector regression model is an effective dynamic model that can effectively predict economic time series and can effectively analyse the impact of random disturbances on economic variables, so as to better understand the relationship between economic variables and effectively control and regulate them. The most distinctive feature of the model is that it can replace the traditional econometric method, which can be based on actual economic data rather than relying solely on the theory of economics, thus better describing the dynamics of the economy. However, the number of parameters required and the period are more complicated when fitting using a VAR model.

In this study, VAR models are constructed to determine the role of cryptocurrency prices in relation to various influencing factors using cointegration tests, impulse response tests, variance decomposition, and Granger causality tests. A VAR autovector regression model is used to analyze the time series and ensure that they are semi-stationary and cointegrated.

The general expression of the VAR model is given below: (7) $Y_{t} = β_{0} + \sum_{i = 1}^{n} β_{n} Y_{t - n} + \sum_{i = 1}^{m} β_{m} X_{t - m} + ε_{t}$ ${Y_t} = {\beta _0} + \sum\limits_{{\text{i = 1}}}^{\text{n}} {{\beta _{\text{n}}}} {Y_{{\text{t - n}}}} + \sum\limits_{{\text{i = 1}}}^{\text{m}} {{\beta _{\text{m}}}} {X_{{\text{t - m}}}} + {\varepsilon _t}$

The steps of the testing procedure are as follows: 1)

Smoothness test: to make the variables fit the selected model better, the time series of all indicator variables are checked for smoothness, and a valid model is constructed.

2)

Cointegration test: to exclude the existence of pseudo-regression in the model.

3)

VAR analysis: determine the optimal lag order by AIC and SC information criteria.

4)

Impulse response function analysis: clearly shows the behaviour of one variable in response to an external shock to another variable.

5)

Granger causality test: analyses whether the lagged term of one variable can affect other variables.

6)

Variance decomposition: to explore the impact of different factors on the price of cryptocurrencies and assess the contribution of these explanatory variables.

The empirical results, as shown in Table 3, show that the relationship between the Composite Index of the New Crown Pneumonia Epidemic (Covi) and the Cryptocurrency Price Index is insignificant (-0.038), and the Currency-Based Futures Price Index is positively correlated with the Cryptocurrency Price Index, which suggests that the impact of the New Crown Pneumonia Epidemic on the cryptocurrency market is complex and that there is an intrinsic consistency in the impacts of market fluctuations of the cryptocurrency and the Currency-Based Futures. Specifically, the cryptocurrency price index has a negative correlation with the Chinese CKP epidemic index and a positive correlation with the foreign exchange rate index and the futures price index. That is to say, the higher the CCPI, the higher the cryptocurrency price.

Table 3.

VAR model estimates

Model	(1-1) VAR	(1-2) VAR
Variable	Dcpi	Dcpi
L.dcpi	0.812*** (0.084)	0.814*** (0.062)
L2.dcpi	0.163** (0.085)	0.156** (0.086)
L.covi	-0.038 (0.065)
L2.covi	0.036 (0.056)
L.dcpi	-0.082 (0.098)	0.057 (0.082)
L2.dcpi	0.072 (0.098)	-0.054 (0.076)
L.covi	0.510*** (0.070)
L2.covi	0.354*** (0.07)
Constant	4.224 (4.420)	7.473 (7.514)
L.eri		0.022 (0.092)
L2.eri		-0.049 (0.093)
L.eri		0.784*** (0.074)
L2.eri		-0.234*** (0.076)
Constant	16.320*** (5.843)	44.122*** (6.304)
Log likehood	-1145.32	-1063.76
AIC	12.60324	11.71196
Observations	190	190

The VAR model shows significant 2nd-order autocorrelation effects for key variables such as cryptocurrency prices. The impulse responses of the VAR model of the new coronary pneumonia epidemic on the cryptocurrency market are shown in Figure 5: 1)

The self-shock, as well as the mutual impact of the XKP epidemic, is characterised by high volatility and slow convergence. Specifically, the self-shock effect of China’s XKP epidemic index is stronger, and there is a longer lag effect, while the self-shock effect of the overseas XKP index is relatively smaller, but the lag is longer, and the mutual impact effect of the two is not obvious. The variance of the self-shock first-order effect of China’s Xin Guan Pneumonia Epidemic Index is obvious, and it converges smoothly later.

2)

The shock effect of the NCPI on the cryptocurrency market index is relatively flat overall, and there is a long-lagged effect. The first-order variance becomes more pronounced after the shock.

3)

The self-shock effect on cryptocurrency prices is more pronounced, and there are longer-lagged effects with more pronounced first-order variance.

4)

Foreign exchange rates and currency-based futures prices are more self-inflicted shocks with long lag effects. Both have less impact on the cryptocurrency market.

4

Conclusion

This paper first analyzes the development overview of the cryptocurrency market and the market share of different cryptocurrencies. Based on these basic data, the transaction data of cryptocurrencies is selected as the research object, and the interaction mechanism between cryptocurrencies and the financial market is explored using relevant analysis methods. The impact of the cryptocurrency market on the risk of unexpected events is assessed using the VAR model. The results show: 1)

The cryptocurrency market reached $3 trillion in 2023, with Bitcoin having the largest market share.

2)

Bitcoin, Litecoin, Ethereum, and Ripple show a weak negative correlation with the Chinese financial market.

3)

Cryptocurrency prices rose in response to an increase in the NKP outbreak index. The overall impact of the New Crown Pneumonia Outbreak on the cryptocurrency market is relatively muted, and there is a long lag effect.

Język:: Angielski

Częstotliwość wydawania:: 1 razy w roku
Dziedziny czasopisma:: Nauki biologiczne, Nauki biologiczne, inne, Matematyka, Matematyka stosowana, Matematyka ogólna, Fizyka, Fizyka, inne

Kanał RSS czasopisma

Adoption of cryptocurrencies by financial institutions: challenges and opportunities in the digital economy

Qianyi Luo

Data publikacji: 05 lut 2025

Otrzymano: 30 sie 2024

Przyjęty: 19 gru 2024

DOI: https://doi.org/10.2478/amns-2025-0073

Słowa kluczoweCryptocurrency, Correlation analysis, VAR model, Impulse analysis

© 2025 Qianyi Luo, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Cryptocurrency, Correlation analysis, VAR model, Impulse analysis