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- Publisher Website: 10.1142/9789814355711_0006
- Scopus: eid_2-s2.0-84973414920
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Book Chapter: Filtering with Counting Process Observations and Other Factors: Applications to Bond Price Tick Data
Title | Filtering with Counting Process Observations and Other Factors: Applications to Bond Price Tick Data |
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Authors | |
Keywords | Ultra high frequency data Markov chain approximation method Bayes parameter estimation Price discreteness Price clustering |
Issue Date | 2011 |
Publisher | World Scientific |
Citation | Filtering with Counting Process Observations and Other Factors: Applications to Bond Price Tick Data. In Allanus Tsoi, David Nualart & George Yin (Eds.), Stochastic Analysis, Stochastic Systems, and Applications to Finance, p. 115-144. Singapore: World Scientific, 2011 How to Cite? |
Abstract | In this paper, we propose an extended filtering micromovement model. The model captures the two main stylized facts of the bond price tick data: random trading times and trading noises. In the intrinsic value process for the transaction price of 5-year U.S. Treasury note, we extend the volatility part by adding the buyer-seller initiation dummy. For the extended model, we present the normalized and un-normalized filtering equations, a robustness theorem and the consistency of Bayes estimates. Based on the robustness theorem, we employ the Markov chain approximation method to construct a robust recursive algorithm for computing the posteriors and Bayes estimates. We present a Monte Carlo example to demonstrate that the computed Bayes estimates converge to their true values. The algorithm is applied to one and an half month of intraday transaction prices of 5-year Treasury notes. Bayes estimates are obtained. Especially, the sign of the buyer-seller initiation dummy is significantly negative, supporting that the inventory theory dominates in the bond trading.
Read More: http://www.worldscientific.com/doi/abs/10.1142/9789814355711_0006 |
Persistent Identifier | http://hdl.handle.net/10722/218407 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Hu, X | - |
dc.contributor.author | Kuipers, DR | - |
dc.contributor.author | Zeng, Y | - |
dc.date.accessioned | 2015-09-18T06:36:29Z | - |
dc.date.available | 2015-09-18T06:36:29Z | - |
dc.date.issued | 2011 | - |
dc.identifier.citation | Filtering with Counting Process Observations and Other Factors: Applications to Bond Price Tick Data. In Allanus Tsoi, David Nualart & George Yin (Eds.), Stochastic Analysis, Stochastic Systems, and Applications to Finance, p. 115-144. Singapore: World Scientific, 2011 | - |
dc.identifier.isbn | 9789814355704 | - |
dc.identifier.uri | http://hdl.handle.net/10722/218407 | - |
dc.description.abstract | In this paper, we propose an extended filtering micromovement model. The model captures the two main stylized facts of the bond price tick data: random trading times and trading noises. In the intrinsic value process for the transaction price of 5-year U.S. Treasury note, we extend the volatility part by adding the buyer-seller initiation dummy. For the extended model, we present the normalized and un-normalized filtering equations, a robustness theorem and the consistency of Bayes estimates. Based on the robustness theorem, we employ the Markov chain approximation method to construct a robust recursive algorithm for computing the posteriors and Bayes estimates. We present a Monte Carlo example to demonstrate that the computed Bayes estimates converge to their true values. The algorithm is applied to one and an half month of intraday transaction prices of 5-year Treasury notes. Bayes estimates are obtained. Especially, the sign of the buyer-seller initiation dummy is significantly negative, supporting that the inventory theory dominates in the bond trading. Read More: http://www.worldscientific.com/doi/abs/10.1142/9789814355711_0006 | - |
dc.language | eng | - |
dc.publisher | World Scientific | - |
dc.relation.ispartof | Stochastic Analysis, Stochastic Systems, and Applications to Finance | - |
dc.subject | Ultra high frequency data | - |
dc.subject | Markov chain approximation method | - |
dc.subject | Bayes parameter estimation | - |
dc.subject | Price discreteness | - |
dc.subject | Price clustering | - |
dc.title | Filtering with Counting Process Observations and Other Factors: Applications to Bond Price Tick Data | - |
dc.type | Book_Chapter | - |
dc.identifier.email | Hu, X: gracexhu@hku.hk | - |
dc.identifier.authority | Hu, X=rp01554 | - |
dc.identifier.doi | 10.1142/9789814355711_0006 | - |
dc.identifier.scopus | eid_2-s2.0-84973414920 | - |
dc.identifier.hkuros | 250507 | - |
dc.identifier.spage | 115 | - |
dc.identifier.epage | 144 | - |
dc.publisher.place | Singapore | - |