*5.2. Scenario Analysis of Cryptocurrencies*

A major significance of using factors for computing MD is its effectiveness in performing scenario analysis [48]. The two most important components of scenario analysis are the construction of meaningful scenarios and the probability of occurrence for the scenarios. Even though it is difficult to construct scenarios directly at the cryptocurrency level (e.g., it is challenging to form an outlook on short-term returns for a certain currency), it is more intuitive to form a logical outlook on risk factors such as the growth in total transactions or users. Furthermore, since the likelihood of a scenario is proportional to *e*−*MD*/2, these values can be rescaled to sum to one when estimating the probability of several scenarios [48].

Here, an example is presented to demonstrate how scenarios can be formed with cryptocurrency factors when anomaly scores are computed with robust MD. Table 2 shows mean and standard deviation of weekly growth for the four factors, and the growth in weekly transactions appear to be near zero on average since the beginning of 2018. Suppose scenario analysis is performed based on the view that transactions are going to increase in the coming week; consider growth in transactions to be realized within the set {0.001%, 0.5%, 1.0%, 1.5%, ... , 10.0%}. Thus, 11 scenarios are generated where transaction takes one of the 11 values, whereas the growth of the other three factors are assumed to stay unchanged (i.e., mean values from Table 2). The advantage of scenario generation from factors is clearly evident in this case. Expressing market outlook through growth in the number of transactions is intuitive even for an investor not familiar with the cryptocurrency market. More rational and detailed views can be expressed with factors.

Next, anomaly scores of these scenarios provide the likelihood (probability) of occurrence for each scenario, and Figure 7 plots the likelihood for the 11 scenarios in this example. The probability of growth in weekly transaction being at least 6% is less than 5%. Thus, even though scenarios are included for cases with large transaction growth, incorporating likelihood through anomaly scores controls the influence on future outcome that are considered outliers. Finally, based on the scenario analysis of traditional assets proposed by [48], the scenarios for the crypto market can be performed as summarized in Figure 8 by applying machine learning models to identify significant factors for efficiently forming rational outlook. These scenarios can be combined with anomaly scores for simulating portfolios invested in cryptocurrencies.

**Figure 7.** Likelihood of example scenarios on transaction.

**Figure 8.** Framework for performing scenario analysis of the crypto market.


**Table 2.** Statistics of weekly growth (from January 2018 to February 2022).
