**2. Literature Review**

As previously identified, there is a clear interest in the cryptocurrency market, not only among the general public and investors but also among researchers in several different areas. Corbet et al. (2019) made a very interesting survey of work involving cryptocurrencies, dividing research in non-quantitative and quantitative and dividing papers in five different areas: bubble dynamics, regulation, cybercrime, diversification and efficiency.

Regarding quantitative research, which is our focus, this kind of work seems to originate in 2013. For example, Kristoufek (2013) related the increased valuation of Bitcoin to the increased interest in that currency. Gronwald (2014) used a GARCH (Generalized Autoregressive Conditional Heteroskedasticity) approach and concluded that Bitcoin was marked by extreme price movements, probably because it was still an immature market. Bornholdt and Sneppen (2014) compared Bitcoin with other cryptocurrencies in terms of their importance, and concluded at that time that Bitcoin did not show special advantages over other cryptocurrencies. Using complex networks, Kondor et al. (2014) identified that cryptocurrencies in general were evolving and accumulating value owing to their increasing acceptance. Kristoufek (2015) studied the potential drivers of Bitcoin prices, identifying that it has properties of a standard financial asset as well as properties of a speculative asset.

These are some prior papers devoted to analyzing the quantitative aspects of cryptocurrencies. However, as mentioned, the literature on cryptocurrencies is constantly growing, and recently many studies have been found to analyze cryptocurrencies. One of the most researched issues concerns the efficiency of the cryptocurrency market (Urquhart 2016; Bariviera 2017; Nadarajah and Chu 2017; Tiwari et al. 2018, among others), with most studies concluding on the inefficiency of these assets (although with some evidence that the market is becoming more efficient—see, for example, Khuntia and Pattanayak (2018)). A comprehensive recent survey about the efficiency of cryptocurrencies is found in Kyriazis (2019).

Some studies analyzed bubbles in the cryptocurrency market. For example, Cheah and Fry (2015), Corbet et al. (2018a) and Chaim and Laurini (2019) all found evidence of the possible existence of bubbles in cryptocurrencies.

We also found studies on the similarities of cryptocurrencies and other assets. For example, Baumöhl (2018) analyzed the connection between forex and cryptocurrencies and found evidence of a low connection between those markets. Corbet et al. (2018b) analyzed three different cryptocurrencies and other financial assets, and found some segmentation between them, concluding that investment in cryptocurrencies could offer diversification opportunities to investors, mainly in short time horizons. Symitsi and Chalvatzis (2018) analyzed the linkages between Bitcoin and energy and technology companies and also found some relationship between those markets. Kristjanpoller and Bouri (2019) also analyzed the cross-correlations between cryptocurrencies and conventional currencies, pointing out significant asymmetric characteristics in the cross-correlations. Ji et al. (2019a) applied network methodologies to analyze linkages between cryptocurrencies and other commodities, finding connections with some of those commodities. Bouri et al. (2019a) analyzed linkages between cryptocurrencies, focusing on the relationship between volatility measures and discriminating between transitory and permanent causalities. The authors concluded that permanent shocks are more important. Ji et al. (2019b) also analyzed the information interdependence between cryptocurrencies and commodities, with a time-varying approach, and concluded that cryptocurrencies are integrated within broadly defined commodity markets.

Previous work analyzed comovements between cryptocurrencies and other assets, while several studies analyzed comovements among di fferent cryptocurrencies. For example, Beneki et al. (2019) centered their analysis on the relationship between Bitcoin and Ethereum, and determined that correlations exist, with each cryptocurrency influencing the other with a time-varying kind of influence. Mensi et al. (2019) used wavelet methodologies to study the comovements among major cryptocurrencies. Despite the existence of comovements, the authors concluded that mixed portfolios with di fferent cryptocurrencies could be interesting for diversification purposes. Besides analyzing the linkages with commodities, Ji et al. (2019a) studied the connectedness of return and volatility in major cryptocurrencies, finding that Litecoin and Bitcoin are in the center of the connectedness in terms of returns, and that negative returns have larger e ffects than positive returns. In the case of volatility, Bitcoin is the cryptocurrency with greatest influence.

Bouri et al. (2019b) analyzed the possibility of herding behavior in the cryptocurrency market. While with a static approach, the evidence does not confirm that e ffect, with a dynamic analysis, due to the existence of breaks and non-linearities in data, that e ffect seems to be found. In another study, Bouri et al. (2019c) analyzed whether explosivity in one cryptocurrency can lead to explosivity in other cryptocurrencies, finding evidence of connections between those assets.

These two studies could be considered as a starting point for our analysis, because they draw attention to the need to study contagion in the cryptocurrency market. Analyzing the existence of a contagion effect in the cryptocurrency market is also present in other studies. For example, Antonakakis et al. (2019) investigated the transmission mechanism among the top nine cryptocurrencies between 15 August 2015 and 31 May 2018, finding that periods of high (low) uncertainty were characterized by strong (low) connectivity, with Bitcoin a ffecting the market's connectivity the most. In another analysis, Silva et al. (2019) analyzed 50 cryptocurrencies with greater liquidity, identifying the contagion e ffect of Bitcoin in almost all cases.

In this study, to analyze the possibility of a contagion e ffect, we used the ΔρDCCA proposed by Da Silva et al. (2016), based on Zebende (2011) cross-correlation coe fficient. The main contribution of this work lies in the analysis of multiscale contagion applied to the cryptocurrency market. Moreover, and as mentioned by Bouri et al. (2019b), this market seems to su ffer from non-linearities, so the use of the proposed methodology was appropriate. We employed the methodology proposed by Guedes et al. (2018a, 2018b) to test the statistical significance of the contagion e ffect.
