Modeling of Returns Volatility using GARCH(1,1) Model under Tukey Transformations

This study proposed two new classes of GARCH(1,1) model by applying the Tukeytransformations to the returns and to the lagged variance. The behavior of return volatility was investigated on the basis of models with normal and Student-t distributions for return error. The competing models were estima...

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Main Authors: Didit Budi Nugroho, Bambang Susanto, Kezia Natalia Putri Prasetia, Rebecca Rorimpandey
Format: Article
Language:Indonesian
Published: Petra Christian University 2019-05-01
Series:Jurnal Akuntansi dan Keuangan
Subjects:
Online Access:http://jurnalakuntansi.petra.ac.id/index.php/aku/article/view/21359
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spelling doaj-7909481ebb934899b6c30422e0292ae52020-11-25T02:12:18ZindPetra Christian UniversityJurnal Akuntansi dan Keuangan1411-02882338-81372019-05-01211122010.9744/jak.20.1.12-2019532Modeling of Returns Volatility using GARCH(1,1) Model under Tukey TransformationsDidit Budi Nugroho0Bambang Susanto1Kezia Natalia Putri Prasetia2Rebecca Rorimpandey3Department of Mathematics, Satya Wacana Christian University, IndonesiaDepartment of Mathematics, Satya Wacana Christian University, IndonesiaDepartment of Mathematics, Satya Wacana Christian University, IndonesiaDepartment of Mathematics, Satya Wacana Christian University, IndonesiaThis study proposed two new classes of GARCH(1,1) model by applying the Tukeytransformations to the returns and to the lagged variance. The behavior of return volatility was investigated on the basis of models with normal and Student-t distributions for return error. The competing models were estimated by using the Excel Solver and Matlab tools. The empirical analysis is based on simulated data, daily exchange rates of the IDR/USD, and daily stock indices of FTSE100 and TOPIX. This study recommends the use of Excel Solver for finance academics and practitioners working on volatility using GARCH(1,1) models. Our empirical findings conclude that GARCH(1,1) models under Tukey transformations should be considered in risk management decisions since the models are more appropriate than standard for describing returns and volatility of financial time series and its stylized facts including fat tails and mean reverting. The Tukey transformed returns imply a shorter volatility half-life, and thus this study suggests that investors should invest the observed assets in a shorter time period to obtain higher returns.http://jurnalakuntansi.petra.ac.id/index.php/aku/article/view/21359tukey transformationexcel solvergarchmatlabvolatility
collection DOAJ
language Indonesian
format Article
sources DOAJ
author Didit Budi Nugroho
Bambang Susanto
Kezia Natalia Putri Prasetia
Rebecca Rorimpandey
spellingShingle Didit Budi Nugroho
Bambang Susanto
Kezia Natalia Putri Prasetia
Rebecca Rorimpandey
Modeling of Returns Volatility using GARCH(1,1) Model under Tukey Transformations
Jurnal Akuntansi dan Keuangan
tukey transformation
excel solver
garch
matlab
volatility
author_facet Didit Budi Nugroho
Bambang Susanto
Kezia Natalia Putri Prasetia
Rebecca Rorimpandey
author_sort Didit Budi Nugroho
title Modeling of Returns Volatility using GARCH(1,1) Model under Tukey Transformations
title_short Modeling of Returns Volatility using GARCH(1,1) Model under Tukey Transformations
title_full Modeling of Returns Volatility using GARCH(1,1) Model under Tukey Transformations
title_fullStr Modeling of Returns Volatility using GARCH(1,1) Model under Tukey Transformations
title_full_unstemmed Modeling of Returns Volatility using GARCH(1,1) Model under Tukey Transformations
title_sort modeling of returns volatility using garch(1,1) model under tukey transformations
publisher Petra Christian University
series Jurnal Akuntansi dan Keuangan
issn 1411-0288
2338-8137
publishDate 2019-05-01
description This study proposed two new classes of GARCH(1,1) model by applying the Tukeytransformations to the returns and to the lagged variance. The behavior of return volatility was investigated on the basis of models with normal and Student-t distributions for return error. The competing models were estimated by using the Excel Solver and Matlab tools. The empirical analysis is based on simulated data, daily exchange rates of the IDR/USD, and daily stock indices of FTSE100 and TOPIX. This study recommends the use of Excel Solver for finance academics and practitioners working on volatility using GARCH(1,1) models. Our empirical findings conclude that GARCH(1,1) models under Tukey transformations should be considered in risk management decisions since the models are more appropriate than standard for describing returns and volatility of financial time series and its stylized facts including fat tails and mean reverting. The Tukey transformed returns imply a shorter volatility half-life, and thus this study suggests that investors should invest the observed assets in a shorter time period to obtain higher returns.
topic tukey transformation
excel solver
garch
matlab
volatility
url http://jurnalakuntansi.petra.ac.id/index.php/aku/article/view/21359
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