Constructing a financial stress index for Vietnam : an application of autoregressive conditional heteroskedastic models

Nguyen Chi DUC, Thuy Ai HO

Research output: Book Chapters | Papers in Conference ProceedingsBook ChapterResearchpeer-review

1 Scopus Citations

Abstract

This paper constructs an index to measure financial stress for Vietnam with monthly data from April 2007 to December 2016. Various measures of stress are selected based on literature and Vietnam’s practice. An important stress measure, the volatility of stock market, bond market, money market and banking sector, is estimated by variants of the general autoregressive conditional heteroskedasticity (GARCH) model. Individual stress variables are combined together to make an aggregate index using equal variance weighting scheme. The constructed index is a useful tool for policy makers to monitor the riskiness of domestic financial system as well as academics to conduct further research about financial crisis.
Original languageEnglish
Title of host publicationEconometrics for financial applications
PublisherSpringer-Verlag GmbH and Co. KG
Pages562-583
Number of pages22
ISBN (Print)9783319731490
DOIs
Publication statusPublished - 1 Jan 2018

Publication series

NameStudies in Computational Intelligence
Volume760
ISSN (Print)1860-949X

Fingerprint

Financial markets

Cite this

DUC, N. C., & HO, T. A. (2018). Constructing a financial stress index for Vietnam : an application of autoregressive conditional heteroskedastic models. In Econometrics for financial applications (pp. 562-583). (Studies in Computational Intelligence; Vol. 760). Springer-Verlag GmbH and Co. KG. https://doi.org/10.1007/978-3-319-73150-6_45
DUC, Nguyen Chi ; HO, Thuy Ai. / Constructing a financial stress index for Vietnam : an application of autoregressive conditional heteroskedastic models. Econometrics for financial applications. Springer-Verlag GmbH and Co. KG, 2018. pp. 562-583 (Studies in Computational Intelligence).
@inbook{1b5193463f364907853cad45cafb2c8c,
title = "Constructing a financial stress index for Vietnam : an application of autoregressive conditional heteroskedastic models",
abstract = "This paper constructs an index to measure financial stress for Vietnam with monthly data from April 2007 to December 2016. Various measures of stress are selected based on literature and Vietnam’s practice. An important stress measure, the volatility of stock market, bond market, money market and banking sector, is estimated by variants of the general autoregressive conditional heteroskedasticity (GARCH) model. Individual stress variables are combined together to make an aggregate index using equal variance weighting scheme. The constructed index is a useful tool for policy makers to monitor the riskiness of domestic financial system as well as academics to conduct further research about financial crisis.",
author = "DUC, {Nguyen Chi} and HO, {Thuy Ai}",
year = "2018",
month = "1",
day = "1",
doi = "10.1007/978-3-319-73150-6_45",
language = "English",
isbn = "9783319731490",
series = "Studies in Computational Intelligence",
publisher = "Springer-Verlag GmbH and Co. KG",
pages = "562--583",
booktitle = "Econometrics for financial applications",
address = "Germany",

}

DUC, NC & HO, TA 2018, Constructing a financial stress index for Vietnam : an application of autoregressive conditional heteroskedastic models. in Econometrics for financial applications. Studies in Computational Intelligence, vol. 760, Springer-Verlag GmbH and Co. KG, pp. 562-583. https://doi.org/10.1007/978-3-319-73150-6_45

Constructing a financial stress index for Vietnam : an application of autoregressive conditional heteroskedastic models. / DUC, Nguyen Chi; HO, Thuy Ai.

Econometrics for financial applications. Springer-Verlag GmbH and Co. KG, 2018. p. 562-583 (Studies in Computational Intelligence; Vol. 760).

Research output: Book Chapters | Papers in Conference ProceedingsBook ChapterResearchpeer-review

TY - CHAP

T1 - Constructing a financial stress index for Vietnam : an application of autoregressive conditional heteroskedastic models

AU - DUC, Nguyen Chi

AU - HO, Thuy Ai

PY - 2018/1/1

Y1 - 2018/1/1

N2 - This paper constructs an index to measure financial stress for Vietnam with monthly data from April 2007 to December 2016. Various measures of stress are selected based on literature and Vietnam’s practice. An important stress measure, the volatility of stock market, bond market, money market and banking sector, is estimated by variants of the general autoregressive conditional heteroskedasticity (GARCH) model. Individual stress variables are combined together to make an aggregate index using equal variance weighting scheme. The constructed index is a useful tool for policy makers to monitor the riskiness of domestic financial system as well as academics to conduct further research about financial crisis.

AB - This paper constructs an index to measure financial stress for Vietnam with monthly data from April 2007 to December 2016. Various measures of stress are selected based on literature and Vietnam’s practice. An important stress measure, the volatility of stock market, bond market, money market and banking sector, is estimated by variants of the general autoregressive conditional heteroskedasticity (GARCH) model. Individual stress variables are combined together to make an aggregate index using equal variance weighting scheme. The constructed index is a useful tool for policy makers to monitor the riskiness of domestic financial system as well as academics to conduct further research about financial crisis.

UR - http://commons.ln.edu.hk/sw_master/6671

UR - http://www.scopus.com/inward/record.url?scp=85038855145&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-73150-6_45

DO - 10.1007/978-3-319-73150-6_45

M3 - Book Chapter

SN - 9783319731490

T3 - Studies in Computational Intelligence

SP - 562

EP - 583

BT - Econometrics for financial applications

PB - Springer-Verlag GmbH and Co. KG

ER -

DUC NC, HO TA. Constructing a financial stress index for Vietnam : an application of autoregressive conditional heteroskedastic models. In Econometrics for financial applications. Springer-Verlag GmbH and Co. KG. 2018. p. 562-583. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-319-73150-6_45