A Hybrid Model of Machine Learning Model and Econometrics’ Model to Predict Volatility of KSE-100 Index
DOI:
https://doi.org/10.53909/rms.04.01.0125Keywords:
Volatility, KSE-100 index, GARCH model, Neural Network Autoregressive Model, Hybrid modelAbstract
Purpose:
The purpose of this paper is to predict the volatility of the KSE-100 index using econometric and machine learning models. It also designs hybrid models for volatility forecasting by combining these two models in three different ways.
Methodology:
Estimations and forecasting are based on an econometric model GARCH (Generalized Auto Regressive Conditional Heteroscedasticity) and a machine learning model NNAR (Neural Network Auto-Regressive model). The hybrid models designed with GARCH and NNAR include GARCH-based NNAR, NNAR-based GARCH, and the linear combination of GARCH and NNAR.
Findings:
In a comparison of the forecasting results of the KSE-100 index over different periods, the least RMSE is found in a linear combination of NNAR and GARCH, followed by NNAR, GARCH, NNAR based GARCH, and GARCH based NNAR models.
Conclusion:
The study concludes that the hybrid model designed with a linear combination of GARCH and NNAR performs better among all the models in forecasting the volatility of the KSE-100 index.
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