Estimating Tail Risk in Ultra-High-Frequency Cryptocurrency Data
Understanding the density of possible prices in one-minute intervals provides traders, investors, and financial institutions with the data necessary for making informed decisions, managing risk, optimizing trading strategies, and enhancing the overall efficiency of the cryptocurrency market. While h...
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MDPI AG
2024-10-01
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| Series: | International Journal of Financial Studies |
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| Online Access: | https://www.mdpi.com/2227-7072/12/4/99 |
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| author | Kostas Giannopoulos Ramzi Nekhili Christos Christodoulou-Volos |
| author_facet | Kostas Giannopoulos Ramzi Nekhili Christos Christodoulou-Volos |
| author_sort | Kostas Giannopoulos |
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| description | Understanding the density of possible prices in one-minute intervals provides traders, investors, and financial institutions with the data necessary for making informed decisions, managing risk, optimizing trading strategies, and enhancing the overall efficiency of the cryptocurrency market. While high accuracy is critical for researchers and investors, market nonlinearity and hidden dependencies pose challenges. In this study, the filtered historical simulation is used to generate pathways for the next hour on the one-minute step for Bitcoin and Ethereum quotes. The innovations in the simulation are standardized historical returns resampled with the method of block bootstrapping, which helps to capture any hidden dependencies in the residuals of a conditional parameterization in the mean and variance. Ordinary bootstrapping requires the feed innovations to be free of any dependencies. To deal with complex data structures and dependencies found in ultra-high-frequency data, this study employs block bootstrap to resample contiguous segments, thereby preserving the sequential dependencies and sectoral clustering within the market. These techniques enhance decision-making and risk measures in investment strategies despite the complexities inherent in financial data. This offers a new dimension in measuring the market risk of cryptocurrency prices and can help market participants price these assets, as well as improve the timing of their entry and exit trades. |
| format | Article |
| id | doaj-art-100c4c4aae7a4c51985c9cf91dac46f5 |
| institution | Kabale University |
| issn | 2227-7072 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | International Journal of Financial Studies |
| spelling | doaj-art-100c4c4aae7a4c51985c9cf91dac46f52024-12-27T14:29:56ZengMDPI AGInternational Journal of Financial Studies2227-70722024-10-011249910.3390/ijfs12040099Estimating Tail Risk in Ultra-High-Frequency Cryptocurrency DataKostas Giannopoulos0Ramzi Nekhili1Christos Christodoulou-Volos2Department of Accounting and Finance, Neapolis University, Pafos P.O. Box 8042, CyprusDepartment of Accounting and Finance, Applied Science University, Al-Eker P.O. Box 5055, BahrainDepartment of Economics and Business, Neapolis University, Pafos P.O. Box 8042, CyprusUnderstanding the density of possible prices in one-minute intervals provides traders, investors, and financial institutions with the data necessary for making informed decisions, managing risk, optimizing trading strategies, and enhancing the overall efficiency of the cryptocurrency market. While high accuracy is critical for researchers and investors, market nonlinearity and hidden dependencies pose challenges. In this study, the filtered historical simulation is used to generate pathways for the next hour on the one-minute step for Bitcoin and Ethereum quotes. The innovations in the simulation are standardized historical returns resampled with the method of block bootstrapping, which helps to capture any hidden dependencies in the residuals of a conditional parameterization in the mean and variance. Ordinary bootstrapping requires the feed innovations to be free of any dependencies. To deal with complex data structures and dependencies found in ultra-high-frequency data, this study employs block bootstrap to resample contiguous segments, thereby preserving the sequential dependencies and sectoral clustering within the market. These techniques enhance decision-making and risk measures in investment strategies despite the complexities inherent in financial data. This offers a new dimension in measuring the market risk of cryptocurrency prices and can help market participants price these assets, as well as improve the timing of their entry and exit trades.https://www.mdpi.com/2227-7072/12/4/99filtered historical simulationrisk managementhigh-frequency cryptocurrency marketsblock bootstrappingforecastingtail risks |
| spellingShingle | Kostas Giannopoulos Ramzi Nekhili Christos Christodoulou-Volos Estimating Tail Risk in Ultra-High-Frequency Cryptocurrency Data International Journal of Financial Studies filtered historical simulation risk management high-frequency cryptocurrency markets block bootstrapping forecasting tail risks |
| title | Estimating Tail Risk in Ultra-High-Frequency Cryptocurrency Data |
| title_full | Estimating Tail Risk in Ultra-High-Frequency Cryptocurrency Data |
| title_fullStr | Estimating Tail Risk in Ultra-High-Frequency Cryptocurrency Data |
| title_full_unstemmed | Estimating Tail Risk in Ultra-High-Frequency Cryptocurrency Data |
| title_short | Estimating Tail Risk in Ultra-High-Frequency Cryptocurrency Data |
| title_sort | estimating tail risk in ultra high frequency cryptocurrency data |
| topic | filtered historical simulation risk management high-frequency cryptocurrency markets block bootstrapping forecasting tail risks |
| url | https://www.mdpi.com/2227-7072/12/4/99 |
| work_keys_str_mv | AT kostasgiannopoulos estimatingtailriskinultrahighfrequencycryptocurrencydata AT ramzinekhili estimatingtailriskinultrahighfrequencycryptocurrencydata AT christoschristodoulouvolos estimatingtailriskinultrahighfrequencycryptocurrencydata |