Study on high-frequency price-discovery processes of financial assets in data-driven approach
| Project/Area Number |
16K03602
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Economic statistics
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| Research Institution | Keio University |
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Principal Investigator |
NAKATSUMA Teruo 慶應義塾大学, 経済学部(三田), 教授 (90303049)
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| Research Collaborator |
NAKAKITA Makoto
TOYABE Tomoki
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 金融高頻度データ / 取引間隔 / ボラティリティ / 日中季節性 / 板情報 / ベイズ推定 / マルコフ連鎖モンテカルロ法 / モデル選択 / 粒子フィルター / 計量ファイナンス / 高頻度データ分析 / ベイズ統計学 |
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| Outline of Final Research Achievements |
In this study, we propose a novel estimation technique for time series models of financial high-frequency data. Specifically, we consider two types of time series models; one is a model of duration between executions of financial transactions while the other is a model of time-varying volatility (variance) in very short intervals. To make these models more realistic, we propose to incorporate intraday seasonality (a cyclical pattern of duration or volatility during trading hours) explicitly into both models and estimate it simultaneously with the model parameters. Since the proposed models are too complex to be estimated with traditional maximum likelihood estimation, we developed an efficient Bayesian Markov chain Monte Carlo (MCMC) method for these models. We applied our new method to real-world high-frequency data (commodity futures and stock prices) and demonstrated their advantage over the conventional models.
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| Academic Significance and Societal Importance of the Research Achievements |
近年、金融市場においてミリ秒、マイクロ秒、さらに短い間隔で高速に取引を行って利益を狙うHFT (High-Frquency Trading、高速取引) と呼ばれる手法が急速に普及しており、その影響力が金融市場の安定性を脅かすのではないかという懸念が広がっている。本研究は、より現実的な設定の下で高頻度データの時系列モデルを構築することで、金融市場における資産価格形成メカニズムの理解を深めるとともに、高速取引における新しいリスク管理手法の発展のための一助となることを目指すものである。そして、提案モデルが従来使われてきたモデルよりも現実の高頻度データに対する当てはまりがよいことを示すことに成功した。
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Report
(4 results)
Research Products
(14 results)
