PL EN


Preferences help
enabled [disable] Abstract
Number of results
2019 | 119 | 68-84
Article title

Evaluation of Forecasts Performance of ARIMA-GARCH-type Models in the Light of Outliers

Content
Title variants
Languages of publication
EN
Abstracts
EN
The carry-over effect of biased estimates of ARIMA-GARCH-type models parameters on forecasting accuracy is investigated in the presence of outliers by exploring the daily returns of share price series of three major banks in Nigerian. The banks considered are Diamond, United bank for Africa and Union. The data were collected from the Nigerian Stock Exchange and spanned from January 3, 2006 to December 30, 2016, comprises 2713 observations and were divided into two portions. The first portion which ranges from January 3, 2006 to November 24, 2016, comprises 2690 observations was used for model formulation and the second portion which ranges from November 25, 2016 to December 30, 2016, consisting of 23 observations was used for out-of-sample forecasting performance evaluation. The parametric bootstrap technique was used in computing the forecasts while Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Error (ME) were the methods of forecast evaluation considered. The findings of this study showed that in the presence of outliers, the forecasts were found to be biased as indicated by ME and the accuracy reduced as shown by MSE, RMSE and MAE. However, adjusting for the outliers, only marginal improvement on the forecasts was observed, reason being that all the outliers were treated as innovations and they occurred before the forecasts origin.
Year
Volume
119
Pages
68-84
Physical description
Contributors
  • Department of Mathematical Science, Abubakar Tafawa Balewa University, Bauchi, Nigeria
author
  • Department of Mathematical Science, Abubakar Tafawa Balewa University, Bauchi, Nigeria
author
  • Federal College of Education (Tech.), Gombe, Gombe State, Nigeria
  • Department of Mathematics and Statistics, University of Maiduguri, Maiduguri, Nigeria
References
  • [1] Tsay, R. S., Analysis of Financial Time Series. 3rd ed., New York: John Wiley & Sons Inc., (2010) 97-140.
  • [2] Chiang, M., R. Y. Chou and L. Wang, Outlier Detection in the Lognormal Logarithmic Conditional Autoregressive Range Model. Oxford Bulletin of Economics and Statistics, 78 (1) (2014) 126-144
  • [3] Charles, A., Forecasting Volatility with Outliers in GARCH models. Journal of Forecasting, 27 (7) (2008) 551-565
  • [4] Park, B., An Outlier Robust GARCH Model and Forecasting Volatility of Exchange Rate Returns. Journal of Forecasting 21 (5) (2002) 381-393
  • [5] Chen, C. and L.M. Liu, Joint Estimation of Model Parameters and Outlier Effects in Time Series. Journal of the American Statistical Association 8 (1993) 284-297
  • [6] Lange, T., Tail Behaviour and OLS Estimation in AR-GARCH Models. Statistica Sinica 21 (2011) 1191-1200
  • [7] Franses, P. H. and H. Ghijsels, Additive Outliers, GARCH and Forecasting Volatility. International Journal of Forecasting, 15 (1) (1999) 1-9
  • [8] Akpan, E. A., K. E. Lasisi and A. Adamu, Modeling Heteroscedasticity in the Presence of Outliers in Discrete-Time Stochastic Series. Academic Journal of Applied Mathematical Sciences 4 (7) (2018) 61-76
  • [9] Moffat, I. U. and E.A. Akpan Time Series Forecasting: A Tool for Out -Sample Model Selection and Evaluation. American Journal of Scientific and Industrial Research 5 (6) (2014) 185-194
  • [10] Chatfield, C. Time Series Forecasting. (5th ed.). New York: Chapman and Hall CRC, (2000).
  • [11] Wei, W. W. S., Time Series Analysis Univariate and Multivariate Methods. 2nd Ed., New York: Adison Westley, (2006) 3-59.
  • [12] Akpan, E. A. and I. U. Moffat, Detection and Modeling of Asymmetric GARCH Effects in Discrete-Time Series. International Journal of Statistics and Probability 6 (6) (2017) 111-119
  • [13] Moffat, I.U. and E.A. Akpan, Identification and Modeling of Outliers in a Discrete-Time Stochastic Series. American Journal of Theoretical and Applied Statistics 6 (4) (2017) 191-197
  • [14] Box, G.E.P., G. M. Jenkins and G.C. Reinsel, Time Series Analysis: Forecasting and Control. (3rd Ed.). New Jersey: Wiley & Sons, (2008) 5-22.
  • [15] Chang, I., Tiao, G. C. and Chen, C. Estimation of Time Series Parameters in the Presence of Outliers. Technometrics 30 (1988) 193-204
  • [16] Carnero, M.A., Pena, D and Ruiz, E. Estimating GARCH Volatility in the Presence of Outliers. Economics Letters 114 (2012) 86-90
  • [17] David Ardia, Keven Bluteau, Kris Boudt, Leopoldo Catania. Forecasting risk with Markov-switching GARCH models:A large-scale performance study. International Journal of Forecasting Volume 34, Issue 4, October–December 2018, Pages 733-747
  • [18] Yu Runfang, Du Jiangze, Liu Xiaotao. Improved Forecast Ability of Oil Market Volatility Based on combined Markov Switching and GARCH-class Model. Procedia Computer Science Volume 122, 2017, Pages 415-422
Document Type
article
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.psjd-7420d652-c1cf-48c3-8bfe-20bb4b87e1a0
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.