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On the choice of GARCH parameters for efficient modelling of real stock price dynamics
journal contribution
posted on 2016-06-09, 08:58 authored by K.A. Pokhilchuk, Sergey SavelievSergey SavelievWe propose two different methods for optimal choice of GARCH(1,1) parameters for the efficient modelling of stock prices by using a particular return series. Using (as an example) stock return data for Intel Corporation, we vary parameters to fit the average volatility as well as fourth (linked to kurtosis of data) and eighth statistical moments and observe pure convergence of our simulated eighth moment to the stock data. Results indicate that fitting higher-order moments of a return series might not be an optimal approach for choosing GARCH parameters. In contrast, the simulated exponent of the Fourier spectrum decay is much less noisy and can easily fit the corresponding decay of the empirical Fourier spectrum of the used return series of Intel stock, allowing us to efficiently define all GARCH parameters. We compare the estimates of GARCH parameters obtained by fitting price data Fourier spectra with the ones obtained from standard software packages and conclude that the obtained estimates here are deeper in the stability region of parameters. Thus, the proposed method of using Fourier spectra of stock data to estimate GARCH parameters results in a more robust and stable stochastic process but with a shorter characteristic autocovariance time.
Funding
The second author acknowledges support from the Leverhulme Foundation
History
School
- Science
Department
- Physics
Published in
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONSVolume
448Pages
248 - 253 (6)Citation
POKHILCHUK, K.A. and SAVEL'EV, S., 2016. On the choice of GARCH parameters for efficient modelling of real stock price dynamics. Physica A-Statistical Mechanics and its Applications, 448, pp. 248 - 253.Publisher
© ElsevierVersion
- VoR (Version of Record)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Publication date
2015-12-29Copyright date
2016Notes
This paper is closed access.ISSN
0378-4371Publisher version
Language
- en