Evaluation, Modeling and Forecasting Volatility of Daily and Weekly Returns on Nairobi Securities Exchange Using Garch Models
Journal Title: IOSR Journal of Economics and Finance (IOSR-JEF) - Year 2018, Vol 9, Issue 6
Abstract
This paper evaluates the forecasting ability of Nairobi Securities Exchange share prices at different time pointsusing Generalized Autoregressive Conditional Heteroskedasticity (GARCH).A five-year period wasused and appropriate models were determined for each time point for the companies chosen from amongst the lower order GARCH models that is GARCH (1, 1), GARCH (1, 2), GARCH (2, 1) and GARCH (2, 2). The best fitting GARCH models were chosen based on Akaike Information Criterion and Bayesian Information Criterion. Adequacy of the chosen models was done using Ljung Box and Lagrange Multiplier Autoregressive Conditional Heteroskedasticity (ARCH LM) tests. Parameter estimation was done by Bollerslev-Woodridge’s Quasi Maximum Likelihood Estimator (QMLE. The intervals with the least mean errors were considered to have the best predictive ability. The results revealed that GARCH (1, 1) models performed well in modeling most return series for companies investigated especially for daily when compared to weekly returns. GARCH (2, 1) seemed better for KQ weekly data while GARCH (2, 2) performed poorly for all the data sets. The forecasting performance of each time point based on the selected models, daily returns gave better prediction than weekly returns and the models generally performed well when modeled with higher frequency data.
Authors and Affiliations
Betty Korir
Keywords
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- EP ID EP414971
- DOI 10.9790/5933-0906011021.
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