Volatility Dynamics of Non-Linear Volatile Time Series and Analysis of Information Flow: Evidence from Cryptocurrency Data
This paper aims to empirically examine long memory and bi-directional information flow between estimated volatilities of highly volatile time series datasets of five cryptocurrencies. We propose the employment of Garman and Klass (GK), Parkinson’s, Rogers and Satchell (RS), and Garman and Klass-Yang...
| المؤلفون الرئيسيون: | , , |
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| التنسيق: | مقال |
| اللغة: | English |
| منشور في: |
MDPI AG
2022-10-01
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| سلاسل: | Entropy |
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://www.mdpi.com/1099-4300/24/10/1410 |
| _version_ | 1827650330272202752 |
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| author | Muhammad Sheraz Silvia Dedu Vasile Preda |
| author_facet | Muhammad Sheraz Silvia Dedu Vasile Preda |
| author_sort | Muhammad Sheraz |
| collection | DOAJ |
| description | This paper aims to empirically examine long memory and bi-directional information flow between estimated volatilities of highly volatile time series datasets of five cryptocurrencies. We propose the employment of Garman and Klass (GK), Parkinson’s, Rogers and Satchell (RS), and Garman and Klass-Yang and Zhang (GK-YZ), and Open-High-Low-Close (OHLC) volatility estimators to estimate cryptocurrencies’ volatilities. The study applies methods such as mutual information, transfer entropy (TE), effective transfer entropy (ETE), and Rényi transfer entropy (RTE) to quantify the information flow between estimated volatilities. Additionally, Hurst exponent computations examine the existence of long memory in log returns and OHLC volatilities based on simple R/S, corrected R/S, empirical, corrected empirical, and theoretical methods. Our results confirm the long-run dependence and non-linear behavior of all cryptocurrency’s log returns and volatilities. In our analysis, TE and ETE estimates are statistically significant for all OHLC estimates. We report the highest information flow from BTC to LTC volatility (RS). Similarly, BNB and XRP share the most prominent information flow between volatilities estimated by GK, Parkinson’s, and GK-YZ. The study presents the practicable addition of OHLC volatility estimators for quantifying the information flow and provides an additional choice to compare with other volatility estimators, such as stochastic volatility models. |
| first_indexed | 2024-03-09T20:14:47Z |
| format | Article |
| id | doaj.art-a3953e959280455c9a0ecf9cdb395f95 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T20:14:47Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-a3953e959280455c9a0ecf9cdb395f952023-11-24T00:03:14ZengMDPI AGEntropy1099-43002022-10-012410141010.3390/e24101410Volatility Dynamics of Non-Linear Volatile Time Series and Analysis of Information Flow: Evidence from Cryptocurrency DataMuhammad Sheraz0Silvia Dedu1Vasile Preda2Department of Mathematical Sciences, Institute of Business Administration, The School of Mathematics and Computer Science, Karachi 75270, PakistanDepartment of Applied Mathematics, Bucharest University of Economic Studies, 010734 Bucharest, RomaniaFaculty of Mathematics and Computer Science, University of Bucharest, Academiei 14, 010014 Bucharest, RomaniaThis paper aims to empirically examine long memory and bi-directional information flow between estimated volatilities of highly volatile time series datasets of five cryptocurrencies. We propose the employment of Garman and Klass (GK), Parkinson’s, Rogers and Satchell (RS), and Garman and Klass-Yang and Zhang (GK-YZ), and Open-High-Low-Close (OHLC) volatility estimators to estimate cryptocurrencies’ volatilities. The study applies methods such as mutual information, transfer entropy (TE), effective transfer entropy (ETE), and Rényi transfer entropy (RTE) to quantify the information flow between estimated volatilities. Additionally, Hurst exponent computations examine the existence of long memory in log returns and OHLC volatilities based on simple R/S, corrected R/S, empirical, corrected empirical, and theoretical methods. Our results confirm the long-run dependence and non-linear behavior of all cryptocurrency’s log returns and volatilities. In our analysis, TE and ETE estimates are statistically significant for all OHLC estimates. We report the highest information flow from BTC to LTC volatility (RS). Similarly, BNB and XRP share the most prominent information flow between volatilities estimated by GK, Parkinson’s, and GK-YZ. The study presents the practicable addition of OHLC volatility estimators for quantifying the information flow and provides an additional choice to compare with other volatility estimators, such as stochastic volatility models.https://www.mdpi.com/1099-4300/24/10/1410volatilitytransfer entropymutual informationflow of informationfinancial time series |
| spellingShingle | Muhammad Sheraz Silvia Dedu Vasile Preda Volatility Dynamics of Non-Linear Volatile Time Series and Analysis of Information Flow: Evidence from Cryptocurrency Data Entropy volatility transfer entropy mutual information flow of information financial time series |
| title | Volatility Dynamics of Non-Linear Volatile Time Series and Analysis of Information Flow: Evidence from Cryptocurrency Data |
| title_full | Volatility Dynamics of Non-Linear Volatile Time Series and Analysis of Information Flow: Evidence from Cryptocurrency Data |
| title_fullStr | Volatility Dynamics of Non-Linear Volatile Time Series and Analysis of Information Flow: Evidence from Cryptocurrency Data |
| title_full_unstemmed | Volatility Dynamics of Non-Linear Volatile Time Series and Analysis of Information Flow: Evidence from Cryptocurrency Data |
| title_short | Volatility Dynamics of Non-Linear Volatile Time Series and Analysis of Information Flow: Evidence from Cryptocurrency Data |
| title_sort | volatility dynamics of non linear volatile time series and analysis of information flow evidence from cryptocurrency data |
| topic | volatility transfer entropy mutual information flow of information financial time series |
| url | https://www.mdpi.com/1099-4300/24/10/1410 |
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