Value at Risk (VaR) became the industry accepted measure for risk by financial institutions and their regulators after the Basel I Accords agreement of 1996. As a result, many methodologies of estimating VaR models used to carry out risk management in finance have been developed. Engle and Manganelli (2004) developed the Conditional Autoregressive Value at Risk (CAViaR) which is a quantile that focuses on estimating and measuring the lower tail risk. The CAViaR quantile measures the quantile directly in an autoregressive framework and applies the quantile regression method to estimate the CAViaR parameters. This research applied the asymmetric CAViaR, symmetric CAViaR and Indirect GARCH (1, 1) specifications to KQ, EABL and KCB stock returns and performed a set of in sample and out of sample tests to determine the relative efficacy of the three different CAViaR specifications. It was found that the asymmetric CAViaR slope specification works well for the Kenyan stock market and is best suited to estimating VaR. Further, more research needs to be carried out to develop e a satisfactory VaR estimation model.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 6, Issue 3) |
| DOI | 10.11648/j.ajtas.20170603.13 |
| Page(s) | 150-155 |
| 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), 2017. Published by Science Publishing Group |
VaR, Asymmetric CAViaR, Symmetric CAViaR, Indirect GARCH (1, 1) CAViaR
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APA Style
Winnie Mbusiro Chacha, P. Mwita, B. Muema. (2017). Application of Conditional Autoregressive Value at Risk Model to Kenyan Stocks: A Comparative Study. American Journal of Theoretical and Applied Statistics, 6(3), 150-155. https://doi.org/10.11648/j.ajtas.20170603.13
ACS Style
Winnie Mbusiro Chacha; P. Mwita; B. Muema. Application of Conditional Autoregressive Value at Risk Model to Kenyan Stocks: A Comparative Study. Am. J. Theor. Appl. Stat. 2017, 6(3), 150-155. doi: 10.11648/j.ajtas.20170603.13
AMA Style
Winnie Mbusiro Chacha, P. Mwita, B. Muema. Application of Conditional Autoregressive Value at Risk Model to Kenyan Stocks: A Comparative Study. Am J Theor Appl Stat. 2017;6(3):150-155. doi: 10.11648/j.ajtas.20170603.13
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Download@article{10.11648/j.ajtas.20170603.13,
author = {Winnie Mbusiro Chacha and P. Mwita and B. Muema},
title = {Application of Conditional Autoregressive Value at Risk Model to Kenyan Stocks: A Comparative Study},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {6},
number = {3},
pages = {150-155},
doi = {10.11648/j.ajtas.20170603.13},
url = {https://doi.org/10.11648/j.ajtas.20170603.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20170603.13},
abstract = {Value at Risk (VaR) became the industry accepted measure for risk by financial institutions and their regulators after the Basel I Accords agreement of 1996. As a result, many methodologies of estimating VaR models used to carry out risk management in finance have been developed. Engle and Manganelli (2004) developed the Conditional Autoregressive Value at Risk (CAViaR) which is a quantile that focuses on estimating and measuring the lower tail risk. The CAViaR quantile measures the quantile directly in an autoregressive framework and applies the quantile regression method to estimate the CAViaR parameters. This research applied the asymmetric CAViaR, symmetric CAViaR and Indirect GARCH (1, 1) specifications to KQ, EABL and KCB stock returns and performed a set of in sample and out of sample tests to determine the relative efficacy of the three different CAViaR specifications. It was found that the asymmetric CAViaR slope specification works well for the Kenyan stock market and is best suited to estimating VaR. Further, more research needs to be carried out to develop e a satisfactory VaR estimation model.},
year = {2017}
}
TY - JOUR T1 - Application of Conditional Autoregressive Value at Risk Model to Kenyan Stocks: A Comparative Study AU - Winnie Mbusiro Chacha AU - P. Mwita AU - B. Muema Y1 - 2017/05/22 PY - 2017 N1 - https://doi.org/10.11648/j.ajtas.20170603.13 DO - 10.11648/j.ajtas.20170603.13 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 150 EP - 155 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20170603.13 AB - Value at Risk (VaR) became the industry accepted measure for risk by financial institutions and their regulators after the Basel I Accords agreement of 1996. As a result, many methodologies of estimating VaR models used to carry out risk management in finance have been developed. Engle and Manganelli (2004) developed the Conditional Autoregressive Value at Risk (CAViaR) which is a quantile that focuses on estimating and measuring the lower tail risk. The CAViaR quantile measures the quantile directly in an autoregressive framework and applies the quantile regression method to estimate the CAViaR parameters. This research applied the asymmetric CAViaR, symmetric CAViaR and Indirect GARCH (1, 1) specifications to KQ, EABL and KCB stock returns and performed a set of in sample and out of sample tests to determine the relative efficacy of the three different CAViaR specifications. It was found that the asymmetric CAViaR slope specification works well for the Kenyan stock market and is best suited to estimating VaR. Further, more research needs to be carried out to develop e a satisfactory VaR estimation model. VL - 6 IS - 3 ER -
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