Preprint Article Version 1 This version is not peer-reviewed

The Effect of High Positive Autocorrelation on the Performance of Garch Family Models

Version 1 : Received: 13 November 2018 / Approved: 16 November 2018 / Online: 16 November 2018 (07:11:22 CET)

How to cite: Emenogu, N.G.; Adenomon, M.O. The Effect of High Positive Autocorrelation on the Performance of Garch Family Models. Preprints 2018, 2018110381 (doi: 10.20944/preprints201811.0381.v1). Emenogu, N.G.; Adenomon, M.O. The Effect of High Positive Autocorrelation on the Performance of Garch Family Models. Preprints 2018, 2018110381 (doi: 10.20944/preprints201811.0381.v1).

Abstract

This study compared the performance of five Family Generalized Auto-Regressive Conditional Heteroscedastic (fGARCH) models (sGARCH, gjrGARCH, iGARCH, TGARCH and NGARCH) in the presence of high positive autocorrelation. To achieve this, financial time series was simulated with autocorrelated coefficients as ρ = (0.8, 0.85, 0.9, 0.95, 0.99), at different time series lengths (as 250, 500, 750, 1000, 1250, 1500) and each trial was repeated 1000 times carried out in R environment using rugarch package. And the performance of the preferred model was judged using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Results from the simulation revealed that these GARCH models performances varies with the different autocorrelation values and at different time series lengths. But in the overall, NGARCH model dominates with 62.5% and 59.3% using RMSE and MAE respectively. We therefore recommended that investors, financial analysts and researchers interested in stock prices and asset return should adapt NGARCH model when there is high positive autocorrelation in the financial time series data.

Subject Areas

financial time series; autocorrelation; models; GARCH; RMSE; MAE

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