Modeling the Residuals of Financial Time Series with Missing Values for Risk Measures Using R
American Journal of Theoretical and Applied Statistics
Volume 7, Issue 6, November 2018, Pages: 247-255
Received: Nov. 30, 2018;
Accepted: Dec. 14, 2018;
Published: Jan. 10, 2019
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Udokang Anietie Edem, Department of Mathematics and Statistics, School of Applied Science and Technology, Federal Polytechnic, Offa, Nigeria
Ugwuowo Fidelis Ifeanyi, Department of Statistics, Faculty of Physical Sciences, University of Nigeria, Nsukka, Nigeria
This paper is to fit an appropriate model on the returns of daily stock price and determine the appropriate model for the residuals in order to compute some risk measures. The daily stock price of First Bank Nigeria, Plc was collected from Nigerian Stock Exchange Market. The methods of weekly average, regression imputation and repetition were used in computing the missing values. Also, adopted was deleting days with missing values. The method of transformation was determined in each of the series and log transformation was adopted for the four series. In the model selection, the ARMA+GARCH model of the repetition had the minimum AIC as compared to other methods of dealing with missing values. The distribution of the residuals was found to be suitable to the Generalized Parato Distribution (GPD). The parameters of this distribution were used in computation of risk measures. The computed Value at Risk (VaR) has a value of 49438.79 and that of the Expected Shortfall (ES) as 49291.24 with position of 1,000,000. This is an indication that the risk of investing in the stock of the First Bank Nigeria, Plc is eminent.
Udokang Anietie Edem,
Ugwuowo Fidelis Ifeanyi,
Modeling the Residuals of Financial Time Series with Missing Values for Risk Measures Using R, American Journal of Theoretical and Applied Statistics.
Vol. 7, No. 6,
2018, pp. 247-255.
Copyright © 2018 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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