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統計数学セミナー
過去の記録 ～04/18｜次回の予定｜今後の予定 04/19～
| 担当者 | 吉田朋広、荻原哲平、小池祐太 |
|---|---|
| セミナーURL | http://www.sigmath.es.osaka-u.ac.jp/~kamatani/statseminar/ |
| 目的 | 確率統計学およびその関連領域に関する研究発表, 研究紹介を行う. |
2010年03月15日(月)
15:00-16:00 数理科学研究科棟(駒場) 002号室
Cecilia Mancini 氏 (University of Florence)
BROWNIAN COVARIATION AND CO-JUMPS, GIVEN DISCRETE OBSERVATION
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/16.html
Cecilia Mancini 氏 (University of Florence)
BROWNIAN COVARIATION AND CO-JUMPS, GIVEN DISCRETE OBSERVATION
[ 講演概要 ]
We consider two processes driven by Brownian motions plus drift and possibly infinite activity jumps.
Given discrete observations we separately estimate the covariation between the two Brownian parts and the sum of the co-jumps. This allows to gain insight into the dependence structure of the processes and has important applications in finance.
Our estimator is based on a threshold principle allowing to isolate the jumps over a threshold.
The estimator of the continuous covariation is asymptotically Gaussian and converges at speed square root of n when the jump components have finite variation. In presence infinite variation jumps the speed is heavily influenced both by the small jumps dependence structure and by their jump activity indexes.
This talk is based on Mancini and Gobbi (2009), and Mancini (2010).
[ 参考URL ]We consider two processes driven by Brownian motions plus drift and possibly infinite activity jumps.
Given discrete observations we separately estimate the covariation between the two Brownian parts and the sum of the co-jumps. This allows to gain insight into the dependence structure of the processes and has important applications in finance.
Our estimator is based on a threshold principle allowing to isolate the jumps over a threshold.
The estimator of the continuous covariation is asymptotically Gaussian and converges at speed square root of n when the jump components have finite variation. In presence infinite variation jumps the speed is heavily influenced both by the small jumps dependence structure and by their jump activity indexes.
This talk is based on Mancini and Gobbi (2009), and Mancini (2010).
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/16.html
