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統計数学セミナー
過去の記録 ～04/19｜次回の予定｜今後の予定 04/20～
| 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. |
2006年08月22日(火)
15:30-16:40 数理科学研究科棟(駒場) 128号室
Jeannette H.C. WOERNER 氏 (University of Gottingen)
A unifying approach to inference in semimartingale and long-memory models
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2006/07.html
Jeannette H.C. WOERNER 氏 (University of Gottingen)
A unifying approach to inference in semimartingale and long-memory models
[ 講演概要 ]
Over the recent years classical stochastic volatility models based on Brownian motion have been generalized in different ways, either replacing the Brownian motion by a pure jump Levy process, which leads to a pure jump model, or by a fractional Brownian motion, which makes it possible to model both long memory or turbulent behaviour. We consider robust and easily computable estimators for the inte- grated volatility, providing insight in the level of volatility, as needed for risk measurement and pricing of variance and volatility swaps. We discuss consistency and distributional results for the power and multi- power variation estimates based on high frequency data. Furthermore, we consider robustness against additive components and compare the results for the different classes of semimartingale and fractional Brow- nian motion models.
[ 参考URL ]Over the recent years classical stochastic volatility models based on Brownian motion have been generalized in different ways, either replacing the Brownian motion by a pure jump Levy process, which leads to a pure jump model, or by a fractional Brownian motion, which makes it possible to model both long memory or turbulent behaviour. We consider robust and easily computable estimators for the inte- grated volatility, providing insight in the level of volatility, as needed for risk measurement and pricing of variance and volatility swaps. We discuss consistency and distributional results for the power and multi- power variation estimates based on high frequency data. Furthermore, we consider robustness against additive components and compare the results for the different classes of semimartingale and fractional Brow- nian motion models.
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2006/07.html
