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Ambit stochastics

Barndorff-Nielsen, Ole E.

Ambit stochastics

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Ambit stochasticsby Ole E. Barndorff-Nielsen, Fred Espen Benth, Almut E. D. Veraart.
作者:	Barndorff-Nielsen, Ole E.
其他作者:	Benth, Fred Espen.
出版者:	Cham :Springer International Publishing :2018.
面頁冊數:	xxv, 402 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Stochastic processes.
電子資源:	https://doi.org/10.1007/978-3-319-94129-5
ISBN:	9783319941295$q(electronic bk.)

Ambit stochastics
Barndorff-Nielsen, Ole E.

Ambit stochastics[electronic resource] /by Ole E. Barndorff-Nielsen, Fred Espen Benth, Almut E. D. Veraart. - Cham :Springer International Publishing :2018. - xxv, 402 p. :ill., digital ;24 cm. - Probability theory and stochastic modelling,v.882199-3130 ;. - Probability theory and stochastic modelling ;v.70..

Part I The purely temporal case -- 1 Volatility modulated Volterra processes -- 2 Simulation -- 3 Asymptotic theory for power variation of LSS processes -- 4 Integration with respect to volatility modulated Volterra processes -- Part II The spatio-temporal case -- 5 The ambit framework -- 6 Representation and simulation of ambit fields -- 7 Stochastic integration with ambit fields as integrators -- 8 Trawl processes -- Part III Applications -- 9 Turbulence modelling -- 10 Stochastic modelling of energy spot prices by LSS processes -- 11 Forward curve modelling by ambit fields -- Appendix A: Bessel functions -- Appendix B: Generalised hyperbolic distribution -- References -- Index.

Drawing on advanced probability theory, Ambit Stochastics is used to model stochastic processes which depend on both time and space. This monograph, the first on the subject, provides a reference for this burgeoning field, complete with the applications that have driven its development. Unique to Ambit Stochastics are ambit sets, which allow the delimitation of space-time to a zone of interest, and ambit fields, which are particularly well-adapted to modelling stochastic volatility or intermittency. These attributes lend themselves notably to applications in the statistical theory of turbulence and financial econometrics. In addition to the theory and applications of Ambit Stochastics, the book also contains new theory on the simulation of ambit fields and a comprehensive stochastic integration theory for Volterra processes in a non-semimartingale context. Written by pioneers in the subject, this book will appeal to researchers and graduate students interested in empirical stochastic modelling.

ISBN: 9783319941295$q(electronic bk.)

Standard No.: 10.1007/978-3-319-94129-5doiSubjects--Topical Terms:

181874
Stochastic processes.

LC Class. No.: QA274 / .B376 2018

Dewey Class. No.: 519.2

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505 0 $a Part I The purely temporal case -- 1 Volatility modulated Volterra processes -- 2 Simulation -- 3 Asymptotic theory for power variation of LSS processes -- 4 Integration with respect to volatility modulated Volterra processes -- Part II The spatio-temporal case -- 5 The ambit framework -- 6 Representation and simulation of ambit fields -- 7 Stochastic integration with ambit fields as integrators -- 8 Trawl processes -- Part III Applications -- 9 Turbulence modelling -- 10 Stochastic modelling of energy spot prices by LSS processes -- 11 Forward curve modelling by ambit fields -- Appendix A: Bessel functions -- Appendix B: Generalised hyperbolic distribution -- References -- Index.
520 $a Drawing on advanced probability theory, Ambit Stochastics is used to model stochastic processes which depend on both time and space. This monograph, the first on the subject, provides a reference for this burgeoning field, complete with the applications that have driven its development. Unique to Ambit Stochastics are ambit sets, which allow the delimitation of space-time to a zone of interest, and ambit fields, which are particularly well-adapted to modelling stochastic volatility or intermittency. These attributes lend themselves notably to applications in the statistical theory of turbulence and financial econometrics. In addition to the theory and applications of Ambit Stochastics, the book also contains new theory on the simulation of ambit fields and a comprehensive stochastic integration theory for Volterra processes in a non-semimartingale context. Written by pioneers in the subject, this book will appeal to researchers and graduate students interested in empirical stochastic modelling.
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