Improving forecasts of the EGARCH model using artificial neural network and fuzzy inference system

DOI10.1155/2020/6871396zbMath1497.62242OpenAlexW3037335798MaRDI QIDQ2194682

Jane A. Aduda, Ananda O. Kube, Geleta T. Mohammed

Publication date: 7 September 2020

Published in: Journal of Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2020/6871396

zbMATH Keywords

stock marketfuzzy modelEGARCH-ANN model

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Statistical methods; risk measures (91G70) Neural nets and related approaches to inference from stochastic processes (62M45) Inference from stochastic processes and fuzziness (62M86) Financial markets (91G15)

Related Items (1)

Hybrid model for stock market volatility

Uses Software

• FinTS
• JADE

Cites Work

• Unnamed Item
• Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces
• Generalized autoregressive conditional heteroscedasticity
• GARCH based artificial neural networks in forecasting conditional variance of stock returns
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Artificial neural networks: an econometric perspective^∗
• Fuzzy sets
• Analysis of Financial Time Series
• Some Asymptotic Results for Learning in Single Hidden-Layer Feedforward Network Models

This page was built for publication: Improving forecasts of the EGARCH model using artificial neural network and fuzzy inference system