Modeling and Forecasting Stock Market Volatility of CPEC Founding Countries: Using Nonlinear Time Series and Machine Learning Models


  • Tayyab Raza Fraz Dept. of Statistics, University of Karachi
  • Samreen Fatima Assistant Professor, Dept. of Statistics, University of Karachi
  • Mudassir Uddin Visiting Faculty, Dept. of Statistics, University of Karachi



forecast comparison, machine learning, LSTM, CGARCH


The highly sensitive, nonlinear, and unpredictable stock marketbehaviours are always challenging for researchers. Stock markets ofPakistan and China, i.e., KSE-100 and SSE-100, respectively, are thetwo most attractive stock markets after the official announcementof CPEC. Thus, the daily closing price of KSE-100 and SSE-100 Stockreturns are used to evaluate the volatility forecast performance ofthe machine learning technique, GARCH family and the nonlinearregime-switching models. The findings of this study revealed that thestandard GARCH model is the best-fitted model based on Akaike’sInformation Criteria (AIC) and Bayesian Information Criteria (BIC).Furthermore, the forecast performance of the machine learning LSTMmodel outperforms other models based on RMSE for SSE-100. Incontrast, the forecast performance of CGARCH for SSE-100 and theMarkov-regime-switchingmodelforKSE-100outperformsothermodelsbased on MAE, MAPE, and SMAPE evaluation criteria. It is also revealedthat the predictive power of the machine learning model is very closeto CGARCH and MRS model; therefore, the LSTM model can be used asan alternative to GARCH and regime-switching models for stock marketvolatility. These findings will help national and international investors,policy-makers, geographical economists, and industrialists to use thebestforecastmodeltomakebetterpoliciesandgaintremendousprofit.


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How to Cite

Fraz, T. R., Fatima, S., & Uddin, M. (2022). Modeling and Forecasting Stock Market Volatility of CPEC Founding Countries: Using Nonlinear Time Series and Machine Learning Models. JISR Management and Social Sciences & Economics, 20(1), 1–20.



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