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Seminar on Probability and Statistics
Seminar information archive ～04/17｜Next seminar｜Future seminars 04/18～
| Organizer(s) | Nakahiro Yoshida, Teppei Ogihara, Yuta Koike |
|---|
2006/08/22
15:30-16:40 Room #128 (Graduate School of Math. Sci. Bldg.)
Jeannette H.C. WOERNER (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 (University of Gottingen)
A unifying approach to inference in semimartingale and long-memory models
[ Abstract ]
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.
[ Reference 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
