Study on Financial Market Risk Measurement Based on GJR-GARCH and FHS

Hong Zhang; Jian Guo; Li Zhou

doi:doi:10.11648/j.sjams.20150303.12

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Study on Financial Market Risk Measurement Based on GJR-GARCH and FHS

Hong Zhang, Jian Guo, Li Zhou

Published in Science Journal of Applied Mathematics and Statistics (Volume 3, Issue 3)

Received: 30 March 2015 Accepted: 16 April 2015 Published: 27 April 2015

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Abstract

In this paper, we establish GJR-GARCH models to extract the residuals of logarithmic returns of one kind of Chinese stock index--- Shanghai Composite Index and the series of independent and identically distribution standardized residuals is formed from the filtered model residuals and conditional volatilities from the return series with an GJR-GARCH model. The results show that from the contrast of actual value and lower limit of predicted VaR value, actual index value for 8 days breaks below the prediction lower limit. The fitting result of VaR method to the market risk of the Shanghai composite index is better than that of the Traditional Historical Simulation.

Published in	Science Journal of Applied Mathematics and Statistics (Volume 3, Issue 3)
DOI	10.11648/j.sjams.20150303.12
Page(s)	70-74
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

VaR, FHS, GJR-GARCH Model, Financial Market Risk

References

[1]	Matthew Pritsker, The Hidden Dangers of Historieal Simulation, Journal of Banking& Finance, 2006(30):572-579.
[2]	Kostas Giannopoulos, Radu Tunaru, Coherent risk measures under filtered historical simulation, Journal of Banking & Finanee, 2005(29):981-994.
[3]	Giovanni Barone-Adesi, Kostas Giannopoulos, Les VosPer, Baektesting Derivative Portfolios with Filtered Historieal Simulation (FHS), European finaneial management, 2002 (8):33-55.
[4]	Giovanni Barone-Adesi, Kostas Giannopoulos, Non-parametrie VaR Teehniques. Myths and Realities, Economic Notes, 2001(30): 169-171
[5]	Giovanni Barone-Adesi, Kostas Giannopoulos, Les Vosper, VaR without Correlations for Non-Linear Portfolios, Journal of Futures Markets, 1999(19):588-601
[6]	Andrey I. Kibzun，Evgeniy A. Kuznetsov. Analysis of criteria VaR and CVaR，Journal of Banking & Finance,2006(30): 779-796.
[7]	Malay Bhattaeharyya，Abhishek Chaudhary，Gaurav Yadav. Conditional VaR estimation using Pearson’s type IV distribution European Journal of Operational Research,，2007，in press.
[8]	Allan Gregory, Jonathan Reeves. Interpreting value at risk (VaR) forecasts, 2007(3):1-20.
[9]	Yau Man Zeto Samue L Value at risk and conditional extreme value theory via mark: ov regime switching models, The Journal of Futures Markets, 2008(28):155-181.
[10]	Alexandra Costello, Ebenezer Asem, Eldon Gardner. Comparison of Historically Simulated VaR: Evidence from Oil Prices, Energy Economics, 2008(10):1600-1623.
[11]	Malay Bhattacharyya, Gopal Ritolia. Conditional VaR using EVT Towards a planned margin scheme, International Review of Financial Analysis, 2008(17):382-395.
[12]	Michael Mcaleer, Bernardo Do Veiga. Forecasting Value-at-Risk with a Parsimonious Portfolio Spillover GARCH (PS-GARCH) Model, Journal of Forecasting, 2008(27):1-19.
[13]	Jenkinson A F. The frequency distribution of the annual maximum (or mimimum) values of meteorological elements, Quarterly Journal of the Royal meteorological society, 1955(81):145-158.
[14]	Christian Genest, Jock MacKay. The Joy of Copulas: Bivariate Distributions with Uniform Marginals, The American Statistician, 1986(40):280-283.

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APA Style

Hong Zhang, Jian Guo, Li Zhou. (2015). Study on Financial Market Risk Measurement Based on GJR-GARCH and FHS. Science Journal of Applied Mathematics and Statistics, 3(3), 70-74. https://doi.org/10.11648/j.sjams.20150303.12

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ACS Style

Hong Zhang; Jian Guo; Li Zhou. Study on Financial Market Risk Measurement Based on GJR-GARCH and FHS. Sci. J. Appl. Math. Stat. 2015, 3(3), 70-74. doi: 10.11648/j.sjams.20150303.12

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AMA Style

Hong Zhang, Jian Guo, Li Zhou. Study on Financial Market Risk Measurement Based on GJR-GARCH and FHS. Sci J Appl Math Stat. 2015;3(3):70-74. doi: 10.11648/j.sjams.20150303.12

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@article{10.11648/j.sjams.20150303.12,
  author = {Hong Zhang and Jian Guo and Li Zhou},
  title = {Study on Financial Market Risk Measurement Based on GJR-GARCH and FHS},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {3},
  number = {3},
  pages = {70-74},
  doi = {10.11648/j.sjams.20150303.12},
  url = {https://doi.org/10.11648/j.sjams.20150303.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20150303.12},
  abstract = {In this paper, we establish GJR-GARCH models to extract the residuals of logarithmic returns of one kind of Chinese stock index--- Shanghai Composite Index and the series of independent and identically distribution standardized residuals is formed from the filtered model residuals and conditional volatilities from the return series with an GJR-GARCH model. The results show that from the contrast of actual value and lower limit of predicted VaR value, actual index value for 8 days breaks below the prediction lower limit. The fitting result of VaR method to the market risk of the Shanghai composite index is better than that of the Traditional Historical Simulation.},
 year = {2015}
}

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TY  - JOUR
T1  - Study on Financial Market Risk Measurement Based on GJR-GARCH and FHS
AU  - Hong Zhang
AU  - Jian Guo
AU  - Li Zhou
Y1  - 2015/04/27
PY  - 2015
N1  - https://doi.org/10.11648/j.sjams.20150303.12
DO  - 10.11648/j.sjams.20150303.12
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 70
EP  - 74
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20150303.12
AB  - In this paper, we establish GJR-GARCH models to extract the residuals of logarithmic returns of one kind of Chinese stock index--- Shanghai Composite Index and the series of independent and identically distribution standardized residuals is formed from the filtered model residuals and conditional volatilities from the return series with an GJR-GARCH model. The results show that from the contrast of actual value and lower limit of predicted VaR value, actual index value for 8 days breaks below the prediction lower limit. The fitting result of VaR method to the market risk of the Shanghai composite index is better than that of the Traditional Historical Simulation.
VL  - 3
IS  - 3
ER  -

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Author Information

Hong Zhang

School of Information, Beijing Wuzi University, Beijing, China
Jian Guo

School of Information, Beijing Wuzi University, Beijing, China
Li Zhou

School of Information, Beijing Wuzi University, Beijing, China

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Plain Text BibTeX RIS

APA Style

Hong Zhang, Jian Guo, Li Zhou. (2015). Study on Financial Market Risk Measurement Based on GJR-GARCH and FHS. Science Journal of Applied Mathematics and Statistics, 3(3), 70-74. https://doi.org/10.11648/j.sjams.20150303.12

Copy | Download

ACS Style

Hong Zhang; Jian Guo; Li Zhou. Study on Financial Market Risk Measurement Based on GJR-GARCH and FHS. Sci. J. Appl. Math. Stat. 2015, 3(3), 70-74. doi: 10.11648/j.sjams.20150303.12

Copy | Download

AMA Style

Hong Zhang, Jian Guo, Li Zhou. Study on Financial Market Risk Measurement Based on GJR-GARCH and FHS. Sci J Appl Math Stat. 2015;3(3):70-74. doi: 10.11648/j.sjams.20150303.12

Copy | Download

@article{10.11648/j.sjams.20150303.12,
  author = {Hong Zhang and Jian Guo and Li Zhou},
  title = {Study on Financial Market Risk Measurement Based on GJR-GARCH and FHS},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {3},
  number = {3},
  pages = {70-74},
  doi = {10.11648/j.sjams.20150303.12},
  url = {https://doi.org/10.11648/j.sjams.20150303.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20150303.12},
  abstract = {In this paper, we establish GJR-GARCH models to extract the residuals of logarithmic returns of one kind of Chinese stock index--- Shanghai Composite Index and the series of independent and identically distribution standardized residuals is formed from the filtered model residuals and conditional volatilities from the return series with an GJR-GARCH model. The results show that from the contrast of actual value and lower limit of predicted VaR value, actual index value for 8 days breaks below the prediction lower limit. The fitting result of VaR method to the market risk of the Shanghai composite index is better than that of the Traditional Historical Simulation.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Study on Financial Market Risk Measurement Based on GJR-GARCH and FHS
AU  - Hong Zhang
AU  - Jian Guo
AU  - Li Zhou
Y1  - 2015/04/27
PY  - 2015
N1  - https://doi.org/10.11648/j.sjams.20150303.12
DO  - 10.11648/j.sjams.20150303.12
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 70
EP  - 74
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20150303.12
AB  - In this paper, we establish GJR-GARCH models to extract the residuals of logarithmic returns of one kind of Chinese stock index--- Shanghai Composite Index and the series of independent and identically distribution standardized residuals is formed from the filtered model residuals and conditional volatilities from the return series with an GJR-GARCH model. The results show that from the contrast of actual value and lower limit of predicted VaR value, actual index value for 8 days breaks below the prediction lower limit. The fitting result of VaR method to the market risk of the Shanghai composite index is better than that of the Traditional Historical Simulation.
VL  - 3
IS  - 3
ER  -

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