Modeling and Forecasting Bank Stock Prices: GARCH and ARIMA Approaches

Author:

Shogole Leeto Malose Patrick,Nwanamidwa Sharon,Chanza Martin,Munapo Elias,Mpeta Kolentino N.

Publisher

Springer Nature Switzerland

Reference22 articles.

1. Alzyadat, J.A., Abuhommous, A.A., Alqaralleh, H.: Testing the conditional volatility of Saudi Arabia stock market: Symmetric and asymmetric autoregressive conditional heteroskedasticity (garch) approach. Acad Acc Finan Stud J 25(2), 1–9 (2021)

2. Arnerić, J., Poklepović, T., Aljinović, Z.: Garch based artificial neural networks in forecasting conditional variance of stock returns. Croatian Oper Res Rev 329–343 (2014)

3. Bijelic, A., Ouijjane, T.: Predicting exchange rate value-at-risk and expected shortfall: A neural network approach (2019)

4. Bollerslev, T.: Generalized autoregressive conditional heteroskedasticity. J. Econ. 31(3), 307–327 (1986)

5. Bouri, E., Jalkh, N., Roubaud, D.: Commodity volatility shocks and bric sovereign risk: A garch-quantile approach. Resour. Policy 61, 385–392 (2019)

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