Model Calibration and Validation for the Fuzzy-EGARCH-ANN Model. (24th December 2021)

• Record Type: Journal Article
• Authors: Mohammed, Geleta T.; Aduda, Jane A.; Kube, Ananda O.
• Other Names: Lin Cheng-Jian Academic Editor.
• Is Part Of: Applied computational intelligence and soft computing. Volume 2021(2021)
• Publication Date: 2021-12-24
• Subjects: Computational intelligence -- Periodicals; Soft computing -- Periodicals; 006.305
• DOI: 10.1155/2021/6637091
• Languages: English
• ISSNs: 1687-9724

Abstract

Abstract : This work shown as the fuzzy-EGARCH-ANN (fuzzy-exponential generalized autoregressive conditional heteroscedastic-artificial neural network) model does not require continuous model calibration if the corresponding DE algorithm is used appropriately, but other models such as GARCH, EGARCH, and EGARCH-ANN need continuous model calibration and validation so they fit the data and reality very well up to the desired accuracy. Also, a robust analysis of volatility forecasting of the daily S&P 500 data collected from Yahoo Finance for the daily spanning period 1/3/2006 to 20/2/2020. To our knowledge, this is the first study that focuses on the daily S&P 500 data using high-frequency data and the fuzzy-EGARCH-ANN econometric model. Finally, the research finds that the best performing model in terms of one-step-ahead forecasts based on realized volatility computed from the underlying daily data series is the fuzzy-EGARCH-ANN (1, 1, 2, 1) model with Student's t -distribution.