Model Calibration and Validation for the Fuzzy-EGARCH-ANN Model
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...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2021/6637091 |
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| _version_ | 1832551966909136896 |
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| author | Geleta T. Mohammed Jane A. Aduda Ananda O. Kube |
| author_facet | Geleta T. Mohammed Jane A. Aduda Ananda O. Kube |
| author_sort | Geleta T. Mohammed |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9f13dcbfa2a14c6dbddd236bb43002ac |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-9f13dcbfa2a14c6dbddd236bb43002ac2025-02-03T05:59:59ZengWileyApplied Computational Intelligence and Soft Computing1687-97322021-01-01202110.1155/2021/6637091Model Calibration and Validation for the Fuzzy-EGARCH-ANN ModelGeleta T. Mohammed0Jane A. Aduda1Ananda O. Kube2Mathematics DepartmentMathematics DepartmentMathematics DepartmentThis 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.http://dx.doi.org/10.1155/2021/6637091 |
| spellingShingle | Geleta T. Mohammed Jane A. Aduda Ananda O. Kube Model Calibration and Validation for the Fuzzy-EGARCH-ANN Model Applied Computational Intelligence and Soft Computing |
| title | Model Calibration and Validation for the Fuzzy-EGARCH-ANN Model |
| title_full | Model Calibration and Validation for the Fuzzy-EGARCH-ANN Model |
| title_fullStr | Model Calibration and Validation for the Fuzzy-EGARCH-ANN Model |
| title_full_unstemmed | Model Calibration and Validation for the Fuzzy-EGARCH-ANN Model |
| title_short | Model Calibration and Validation for the Fuzzy-EGARCH-ANN Model |
| title_sort | model calibration and validation for the fuzzy egarch ann model |
| url | http://dx.doi.org/10.1155/2021/6637091 |
| work_keys_str_mv | AT geletatmohammed modelcalibrationandvalidationforthefuzzyegarchannmodel AT janeaaduda modelcalibrationandvalidationforthefuzzyegarchannmodel AT anandaokube modelcalibrationandvalidationforthefuzzyegarchannmodel |