A Novel Methodology to Calculate the Probability of Volatility Clusters in Financial Series: An Application to Cryptocurrency Markets
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 pr...
| Main Authors: | , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
| Published: |
MDPI
2020
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/8412 |
| _version_ | 1789406544813621248 |
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| author | Nikolova, Venelina Trinidad Segovia, Juan Evangelista Fernández Martínez, Manuel Sánchez Granero, Miguel Ángel |
| author_facet | Nikolova, Venelina Trinidad Segovia, Juan Evangelista Fernández Martínez, Manuel Sánchez Granero, Miguel Ángel |
| author_sort | Nikolova, Venelina |
| collection | DSpace |
| description | 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. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-8412 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2020 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-84122023-04-12T19:07:43Z A Novel Methodology to Calculate the Probability of Volatility Clusters in Financial Series: An Application to Cryptocurrency Markets Nikolova, Venelina Trinidad Segovia, Juan Evangelista Fernández Martínez, Manuel Sánchez Granero, Miguel Ángel volatility cluster Hurst exponent FD4 approach volatility series probability of volatility cluster S& P500 Bitcoin Ethereum Ripple 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. 2020-09-02T10:44:32Z 2020-09-02T10:44:32Z 2020-07-24 info:eu-repo/semantics/article 2227-7390 http://hdl.handle.net/10835/8412 en https://www.mdpi.com/2227-7390/8/8/1216 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | volatility cluster Hurst exponent FD4 approach volatility series probability of volatility cluster S& P500 Bitcoin Ethereum Ripple Nikolova, Venelina Trinidad Segovia, Juan Evangelista Fernández Martínez, Manuel Sánchez Granero, Miguel Ángel A Novel Methodology to Calculate the Probability of Volatility Clusters in Financial Series: An Application to Cryptocurrency Markets |
| title | A Novel Methodology to Calculate the Probability of Volatility Clusters in Financial Series: An Application to Cryptocurrency Markets |
| title_full | A Novel Methodology to Calculate the Probability of Volatility Clusters in Financial Series: An Application to Cryptocurrency Markets |
| title_fullStr | A Novel Methodology to Calculate the Probability of Volatility Clusters in Financial Series: An Application to Cryptocurrency Markets |
| title_full_unstemmed | A Novel Methodology to Calculate the Probability of Volatility Clusters in Financial Series: An Application to Cryptocurrency Markets |
| title_short | A Novel Methodology to Calculate the Probability of Volatility Clusters in Financial Series: An Application to Cryptocurrency Markets |
| title_sort | novel methodology to calculate the probability of volatility clusters in financial series: an application to cryptocurrency markets |
| topic | volatility cluster Hurst exponent FD4 approach volatility series probability of volatility cluster S& P500 Bitcoin Ethereum Ripple |
| url | http://hdl.handle.net/10835/8412 |
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