Application of Conditional Autoregressive Value at Risk Model to Kenyan Stocks: A Comparative Study
American Journal of Theoretical and Applied Statistics
Volume 6, Issue 3, May 2017, Pages: 150-155
Received: Oct. 28, 2015; Accepted: Nov. 6, 2015; Published: May 22, 2017
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Authors
Winnie Mbusiro Chacha, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
P. Mwita, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
B. Muema, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
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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.
Keywords
VaR, Asymmetric CAViaR, Symmetric CAViaR, Indirect GARCH (1, 1) CAViaR
To cite this article
Winnie Mbusiro Chacha, P. Mwita, B. Muema, Application of Conditional Autoregressive Value at Risk Model to Kenyan Stocks: A Comparative Study, American Journal of Theoretical and Applied Statistics. Vol. 6, No. 3, 2017, pp. 150-155. doi: 10.11648/j.ajtas.20170603.13
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Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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