**2. Literature Review**

To define cryptocurrency and its role, conceptual aspects related to virtual currencies are available in the literature review section.

Cryptocurrency emerged around 2014 along with the FinTech concept, and it is now a part of the larger digitalization process. Up to 2018, the literature has revolved around the concept of cyptocurrency and diversified classification. However, post-2018, authors have chosen fascinating niches of the literature.

In 2019, Corbet et al. (2019) mentioned cryptocurrencies as a financial asset. The paper presented a review of the literature from an empirical point of view of the aspects associated with cryptocurrency as a financial asset, since its appearance. Based on previous papers, the author chose the topic of cryptocurrency because of existing questions about market e fficiency, asset pricing bubbles, contagion, and decoupling hypotheses or volatility clustering. Situated at the intersection between regulatory oversight, the potential for illicit use through its anonymity within a young under-developed exchange system and infrastructural breaches influenced by the growth of cyber criminality, the role of cryptocurrency proves to be influenced by each of these. Furthermore, in 2019, Bouri et al. (2019) studied the similar movements of cryptocurrencies. They set Bitcoin as the reference currency and discovered the presence of concurrent movements, in the same direction, of 12 types of cryptocurrencies. As a basis for research, they used daily data on the price of cryptocurrencies. The study found that the movement of one cryptocurrency determines, in a large proportion, the movement of other cryptocurrencies in the same direction. The process is called co-jumping. The study concluded that their trading volume highlighted the volatility of cryptocurrencies.

In 2019, Chu et al. (2019) investigated the adaptive market hypothesis regarding the markets of two popular cryptocurrencies (Bitcoin and Ethereum) against the Euro and the U.S. dollar where the results were consistent with this theory. The authors also discussed that events could coincide with significant changes in market e fficiency. The sentiment of these market e fficiency factors was verified using a simple analysis of events to investigate whether these actions a ffected market efficiency/ine fficiency. The bottom line is that sentiment and events cannot be a significant factor in determining the e ffectiveness of cryptocurrency markets. The collected data involved logged hours

with high-frequency Bitcoin and Ethereum prices against the Euro (EUR) and the U.S. dollar (USD). It followed transactions listed on the Kraken cryptocurrency exchange starting 11:00 a.m. on 1 July 2017 until 12 a.m. on 1 September 2018. The particular timespan was selected so that we could analyze the intervals in which prices for the two cryptocurrencies faced huge spikes (before January 2018) and slumps (after January 2018). The results appeared to be consistent with the hypothesis, where the efficiency of the markets varied over time.

In 2018, Zhang et al. (2018) also wrote about the 'stylized facts.' Cryptocurrencies were investigated as a financial asset. They analyzed the stylized facts in terms of the Hurst exponent by using the Detrended Fluctuation Analysis (DFA) and R/S Analysis, of the four most popular cryptocurrencies ranked according to their market capitalization. The datasets contained historical high-frequency prices of those cryptocurrencies versus the U.S. dollar, from 25 February until 17 August 2017. The top four chosen cryptocurrencies for our analysis involved Bitcoin, Ethereum, Ripple, and Litecoin. The study was conducted on high-frequency returns data with varying lags. It also considered features of dependence between the di fferent cryptocurrencies. These features provide academics and industrial practitioners with information about the structure and characteristics of these four popular cryptocurrencies and may also be useful in developing models of pricing cryptocurrencies.

Other authors such as Xu et al. (2019) have measured the tail-risk interdependence between 23 cryptocurrencies using previous methods. They estimated the value at risk (VaR) for each cryptocurrency by using quantile regression, also called the Tail-Event driven NETwork (TENET) framework. With the help of this study, it was identified that a significant risk spillover e ffect exists and that the degree of the total connectedness of all the sampled cryptocurrencies increased steadily over time. Bitcoin seems to be the most significant systemic risk receiver, and Ethereum the largest systemic risk emitter. Like Bouri did, Grobys also developed a study in (Grobys et al. 2019) based on the daily data of the price of cryptocurrencies. This time, their processing involved determining the moving average trading strategies employ. The 11 most traded currencies from 2016–2018 were used for this purpose. The result indicated that a variable moving average strategy is successful when using a 20-day moving average trading strategy. In addition, the results revealed that cryptocurrency markets were ine fficient. Another study by Corbet et al. (2020) showed the destabilizing e ffects of cryptocurrency and cyber criminality. The purpose of the article was to discover what the financial market e ffects of recent cybercrime were in cryptocurrency markets. Corbet used data from the Bitfinex exchange at a 60-min frequency for the eight most liquid cryptocurrencies. The results led to the conclusion that hacking events also increased both the price volatility of the targeted cryptocurrency and cross-cryptocurrency correlations. Cybercrime events reduce the price discovery sourced within the hacked currency relative to other cryptocurrencies. In 2019, Koerhuis et al. (2019) conducted a forensic analysis of privacy-oriented cryptocurrencies and found that criminals used cryptocurrencies that had built-in anonymity and privacy features that made them nearly impossible to trace funds back to a particular user in di fferent kinds of malware to launder money. The author investigated Monero and Verge and studied which valuable forensic artifacts the software of these cryptocurrencies left behind on a computer system. Di fferent sources of potential evidence were also examined in this paper.

In another article, Caporale et al. (2019) wrote about non-linearities, cyber-attacks, and cryptocurrencies. For this purpose, he used a Markov-switching non-linear specification to analyze the e ffects of cyber-attacks on the returns of four cryptocurrencies from 2015 to 2019. The analysis considered cyber-attacks and targeting cryptocurrencies. The results sugges<sup>t</sup> the existence of the significant adverse e ffects of cyber-attacks on the probability of cryptocurrencies staying in the low volatility regime. This reveals the importance of knowing how to deal with cybercriminals to prevent the disruptions of the markets.
