Modelling exchange rates using regime switching models

The behaviour of many financial time series cannot be modeled solely by linear time series model. Phenomena such as mean reversion, volatility of stock markets and structural breaks cannot be modelled implicitly using simple linear time series model. Thus, to overcome this problem, nonlinear time s...

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Main Authors:	Mohd Tahir Ismail, Zaidi Isa
Format:	Article
Published:	Universiti Kebangsaan Malaysia 2006
Online Access:	http://journalarticle.ukm.my/3990/ http://journalarticle.ukm.my/3990/

id	ukm-3990
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spelling	ukm-39902012-03-28T03:46:07Z http://journalarticle.ukm.my/3990/ Modelling exchange rates using regime switching models Mohd Tahir Ismail, Zaidi Isa, The behaviour of many financial time series cannot be modeled solely by linear time series model. Phenomena such as mean reversion, volatility of stock markets and structural breaks cannot be modelled implicitly using simple linear time series model. Thus, to overcome this problem, nonlinear time series models are typically designed to accommodate these nonlinear features in the data. In this paper, we use portmanteau test and structural change test to detect nonlinear feature in three ASEAN countries exchange rates (Malaysia, Singapore and Thailand). It is found that the null hypothesis of linearity is rejected and there is evidence of structural breaks in the exchange rates series. Therefore, the decision of using regime switching model in this study is justified. Using model selection criteria (AIC, SBC, HQC), we compare the in-sample fitting between two types of regime switching model. The two regime switching models we considered were the Self-Exciting Threshold Autoregressive (SETAR) model and the Markov switching Autoregressive (MS-AR) model where these models can explain the abrupt changes in a time series but differ as how they model the movement between regimes. From the AIC, SBC and HQC values, it is found that the MS -AR model is the best fitted model for all the return series. In addition, the regime switching model also found to perform better than simple autoregressive model in in-sample fitting. This result justified that nonlinear model give better in-sample fitting than linear model. Universiti Kebangsaan Malaysia 2006-12 Article PeerReviewed Mohd Tahir Ismail, and Zaidi Isa, (2006) Modelling exchange rates using regime switching models. Sains Malaysiana, 35 (2). pp. 55-62. ISSN 0126-6039 http://www.ukm.my/jsm/english_journals/vol35num2_2006/vol35num2_06page55-62.html
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Kebangasaan Malaysia
building	UKM Institutional Repository
collection	Online Access
description	The behaviour of many financial time series cannot be modeled solely by linear time series model. Phenomena such as mean reversion, volatility of stock markets and structural breaks cannot be modelled implicitly using simple linear time series model. Thus, to overcome this problem, nonlinear time series models are typically designed to accommodate these nonlinear features in the data. In this paper, we use portmanteau test and structural change test to detect nonlinear feature in three ASEAN countries exchange rates (Malaysia, Singapore and Thailand). It is found that the null hypothesis of linearity is rejected and there is evidence of structural breaks in the exchange rates series. Therefore, the decision of using regime switching model in this study is justified. Using model selection criteria (AIC, SBC, HQC), we compare the in-sample fitting between two types of regime switching model. The two regime switching models we considered were the Self-Exciting Threshold Autoregressive (SETAR) model and the Markov switching Autoregressive (MS-AR) model where these models can explain the abrupt changes in a time series but differ as how they model the movement between regimes. From the AIC, SBC and HQC values, it is found that the MS -AR model is the best fitted model for all the return series. In addition, the regime switching model also found to perform better than simple autoregressive model in in-sample fitting. This result justified that nonlinear model give better in-sample fitting than linear model.
format	Article
author	Mohd Tahir Ismail, Zaidi Isa,
spellingShingle	Mohd Tahir Ismail, Zaidi Isa, Modelling exchange rates using regime switching models
author_facet	Mohd Tahir Ismail, Zaidi Isa,
author_sort	Mohd Tahir Ismail,
title	Modelling exchange rates using regime switching models
title_short	Modelling exchange rates using regime switching models
title_full	Modelling exchange rates using regime switching models
title_fullStr	Modelling exchange rates using regime switching models
title_full_unstemmed	Modelling exchange rates using regime switching models
title_sort	modelling exchange rates using regime switching models
publisher	Universiti Kebangsaan Malaysia
publishDate	2006
url	http://journalarticle.ukm.my/3990/ http://journalarticle.ukm.my/3990/
first_indexed	2023-09-18T19:40:20Z
last_indexed	2023-09-18T19:40:20Z
_version_	1777405547173117952

Modelling exchange rates using regime switching models

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