A Novel Methodology to Calculate the Probability of Volatility Clusters in Financial Series: An Application to Cryptocurrency Markets

Abstract

One of the main characteristics of cryptocurrencies is the high volatility of their exchange rates. In a previous work, the authors found that a process with volatility clusters displays a volatility series with a high Hurst exponent. In this paper, we provide a novel methodology to calculate the probability of volatility clusters with a special emphasis on cryptocurrencies. With this aim, we calculate the Hurst exponent of a volatility series by means of the FD4 approach. An explicit criterion to computationally determine whether there exist volatility clusters of a fixed size is described. We found that the probabilities of volatility clusters of an index (S&P500) and a stock (Apple) showed a similar profile, whereas the probability of volatility clusters of a forex pair (Euro/USD) became quite lower. On the other hand, a similar profile appeared for Bitcoin/USD, Ethereum/USD, and Ripple/USD cryptocurrencies, with the probabilities of volatility clusters of all such cryptocurrencies being much greater than the ones of the three traditional assets. Our results suggest that the volatility in cryptocurrencies changes faster than in traditional assets, and much faster than in forex pairs.

Investor attention and cryptocurrency: Evidence from the Bitcoin market

“…Dimitrova et al [ 30 ] investigated the efficiency of the Bitcoin market and pointed out the existence of anti-persistent memory in the BTC-USD series. A recent study done by Nikolova et al [ 31 ] further pointed out that the volatility in cryptocurrencies changed faster than in traditional assets, and much faster than in forex pairs.…”

Time-varying properties of asymmetric volatility and multifractality in Bitcoin

“…Fig 2(d) shows the volatility series s t defined by , where r t and stand for the return at t and the average of r t , respectively. The volatility series introduced in [ 58 , 59 ] can be utilized to identify the volatility clustering. Namely, the increasing (decreasing) trend of the volatility series implies the existence of the high (low) volatility clustering.…”

The dynamics of cryptocurrency market behavior: sentiment analysis using Markov chains

“…While corporate insolvency could result in a stock losing all its value instantly, a cryptocurrency can lose its value only if the network ceased to function or if all users decided it was suddenly worthless (McCoy and Rahimi, 2020). Nikolova et al (2020) noted that the volatility in cryptocurrencies changed faster than in traditional assets and much faster than in forex pairs. Shahzad et al (2019) and Wang et al (2019) argued that Bitcoin and other cryptocurrencies might act as "safe heaven" commodities in financial markets.…”