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Book Chapter: Filtering with Counting Process Observations and Other Factors: Applications to Bond Price Tick Data

TitleFiltering with Counting Process Observations and Other Factors: Applications to Bond Price Tick Data
Authors
KeywordsUltra high frequency data
Markov chain approximation method
Bayes parameter estimation
Price discreteness
Price clustering
Issue Date2011
PublisherWorld 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?
AbstractIn 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 Identifierhttp://hdl.handle.net/10722/218407
ISBN

 

DC FieldValueLanguage
dc.contributor.authorHu, X-
dc.contributor.authorKuipers, DR-
dc.contributor.authorZeng, Y-
dc.date.accessioned2015-09-18T06:36:29Z-
dc.date.available2015-09-18T06:36:29Z-
dc.date.issued2011-
dc.identifier.citationFiltering 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.isbn9789814355704-
dc.identifier.urihttp://hdl.handle.net/10722/218407-
dc.description.abstractIn 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.languageeng-
dc.publisherWorld Scientific-
dc.relation.ispartofStochastic Analysis, Stochastic Systems, and Applications to Finance-
dc.subjectUltra high frequency data-
dc.subjectMarkov chain approximation method-
dc.subjectBayes parameter estimation-
dc.subjectPrice discreteness-
dc.subjectPrice clustering-
dc.titleFiltering with Counting Process Observations and Other Factors: Applications to Bond Price Tick Data-
dc.typeBook_Chapter-
dc.identifier.emailHu, X: gracexhu@hku.hk-
dc.identifier.authorityHu, X=rp01554-
dc.identifier.doi10.1142/9789814355711_0006-
dc.identifier.hkuros250507-
dc.identifier.spage115-
dc.identifier.epage144-
dc.publisher.placeSingapore-

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