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Modeling Time-Varying Unconditional Variance by Means of a Free-Knot Spline-GARCH Model: Gabler Theses

Autor Oliver Old
en Limba Engleză Paperback – 28 iul 2022
The book addresses the problem of a time-varying unconditional variance of return processes utilizing a spline function. The knots of the spline functions are estimated as free parameters within a joined estimation process together with the parameters of the mean, the conditional variance and the spline function. With the help of this method, the knots are placed in regions where the unconditional variance is not smooth. The results are tested within an extensive simulation study and an empirical study employing the S&P500 index.
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Specificații

ISBN-13: 9783658386177
ISBN-10: 3658386177
Pagini: 237
Ilustrații: XXII, 237 p. 57 illus. in color.
Dimensiuni: 148 x 210 mm
Greutate: 0.31 kg
Ediția:1st ed. 2022
Editura: Springer Fachmedien Wiesbaden
Colecția Springer Gabler
Seria Gabler Theses

Locul publicării:Wiesbaden, Germany

Cuprins

Introduction.- Financial time series.- Smoothing long term volatility.- 4 Free-knot spline-GARCH model.- Simulation study.- Empirical study.- Conclusion.

Notă biografică

The dissertation was written at the Chair of Applied Statistics and Methods of Empirical Social Research at the Faculty of Economics and Business Administration of the FernUniversität in Hagen. From 2021 Oliver Old researched in the field of applied statistics, machine learning and data science at two EU-Horizon projects at the Department of Anesthesiology, Intensive Care and Pain Therapy at the University Hospital Frankfurt.

Textul de pe ultima copertă

The book addresses the problem of a time-varying unconditional variance of return processes utilizing a spline function. The knots of the spline functions are estimated as free parameters within a joined estimation process together with the parameters of the mean, the conditional variance and the spline function. With the help of this method, the knots are placed in regions where the unconditional variance is not smooth. The results are tested within an extensive simulation study and an empirical study employing the S&P500 index.

About the author:
The dissertation was written at the Chair of Applied Statistics and Methods of Empirical Social Research at the Faculty of Economics and Business Administration of the FernUniversität in Hagen. From 2021 Oliver Old researched in the field of applied statistics, machine learning and data science at two EU-Horizon projects at the Department of Anesthesiology, Intensive Care and Pain Therapy at the University Hospital Frankfurt.