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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Petra Christian University
2019-05-01
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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 |
work_keys_str_mv |
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1724910109403906048 |