Analysis of the trading interval duration for the Bitcoin market using high-frequency transaction data
Analyzing the trading interval durations of cryptocurrencies is important both academically and practically, but there has been no previous research using tick data. Therefore, we conducted a time series analysis on the duration of the trading interval between consecutive transactions in the Bitcoin...
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| Format: | Article |
| Language: | English |
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AIMS Press
2025-03-01
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| Series: | Quantitative Finance and Economics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/QFE.2025007 |
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| author | Makoto Nakakita Teruo Nakatsuma |
| author_facet | Makoto Nakakita Teruo Nakatsuma |
| author_sort | Makoto Nakakita |
| collection | DOAJ |
| description | Analyzing the trading interval durations of cryptocurrencies is important both academically and practically, but there has been no previous research using tick data. Therefore, we conducted a time series analysis on the duration of the trading interval between consecutive transactions in the Bitcoin market to identify similarities and differences with conventional financial assets such as stocks and commodities. We applied high-frequency transaction tick data from the Bitcoin market to a stochastic conditional duration (SCD) model and estimated the effects of trade price changes and volumes on the trading interval duration simultaneously with the intraday seasonality of the durations. As a result, we captured the effects of the direction of price movements and trading volume on trading interval durations. We also found that the trading interval duration is strongly persistent for Bitcoin similar to conventional financial assets. In contrast, we could not find any clear pattern of intraday seasonality for duration in the Bitcoin market. |
| format | Article |
| id | doaj-art-d25baebf53414c80b87897674cf1b63d |
| institution | OA Journals |
| issn | 2573-0134 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Quantitative Finance and Economics |
| spelling | doaj-art-d25baebf53414c80b87897674cf1b63d2025-08-20T02:34:10ZengAIMS PressQuantitative Finance and Economics2573-01342025-03-019120224110.3934/QFE.2025007Analysis of the trading interval duration for the Bitcoin market using high-frequency transaction dataMakoto Nakakita0Teruo Nakatsuma1Center for Advanced Intelligence Project, RIKEN, Nihonbashi 1-Chome Mitsui Building, 15th Floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, JapanFaculty of Economics, Keio University, 2-15-45 Mita, Minato-ku, Tokyo, JapanAnalyzing the trading interval durations of cryptocurrencies is important both academically and practically, but there has been no previous research using tick data. Therefore, we conducted a time series analysis on the duration of the trading interval between consecutive transactions in the Bitcoin market to identify similarities and differences with conventional financial assets such as stocks and commodities. We applied high-frequency transaction tick data from the Bitcoin market to a stochastic conditional duration (SCD) model and estimated the effects of trade price changes and volumes on the trading interval duration simultaneously with the intraday seasonality of the durations. As a result, we captured the effects of the direction of price movements and trading volume on trading interval durations. We also found that the trading interval duration is strongly persistent for Bitcoin similar to conventional financial assets. In contrast, we could not find any clear pattern of intraday seasonality for duration in the Bitcoin market.https://www.aimspress.com/article/doi/10.3934/QFE.2025007bayesian methodsfinancial time serieshigh-frequency datacryptocurrencymarkov chain monte carlo methodancillary-sufficiency interweaving strategyduration model |
| spellingShingle | Makoto Nakakita Teruo Nakatsuma Analysis of the trading interval duration for the Bitcoin market using high-frequency transaction data Quantitative Finance and Economics bayesian methods financial time series high-frequency data cryptocurrency markov chain monte carlo method ancillary-sufficiency interweaving strategy duration model |
| title | Analysis of the trading interval duration for the Bitcoin market using high-frequency transaction data |
| title_full | Analysis of the trading interval duration for the Bitcoin market using high-frequency transaction data |
| title_fullStr | Analysis of the trading interval duration for the Bitcoin market using high-frequency transaction data |
| title_full_unstemmed | Analysis of the trading interval duration for the Bitcoin market using high-frequency transaction data |
| title_short | Analysis of the trading interval duration for the Bitcoin market using high-frequency transaction data |
| title_sort | analysis of the trading interval duration for the bitcoin market using high frequency transaction data |
| topic | bayesian methods financial time series high-frequency data cryptocurrency markov chain monte carlo method ancillary-sufficiency interweaving strategy duration model |
| url | https://www.aimspress.com/article/doi/10.3934/QFE.2025007 |
| work_keys_str_mv | AT makotonakakita analysisofthetradingintervaldurationforthebitcoinmarketusinghighfrequencytransactiondata AT teruonakatsuma analysisofthetradingintervaldurationforthebitcoinmarketusinghighfrequencytransactiondata |