Modelling and Forecasting of Stock Price Volatility – an Analysis

K. Kannan, Dr. S. Balamurugan
Price Volatility; Modelling and Forecasting; NIFTY 50; GARCH Family Models
The current study uses the daily adjusted closing price for the period from 1 April 2011 to 31 March 2021 to model and forecast the price volatility of the NIFTY 50 companies listed on the Indian stock market using the GARCH family of models. Analytical research design is the method employed for the study. Purposive sampling was employed for the study’s sample design, and the researcher chose one significant sector from the NIFTY 50 index that was listed on March 31, 2021. According to sector weightage, key sectors like financial services were chosen. In this study, several combinations of GARCH and ARCH lags were utilised, and high-order models were generally examined to determine which model was most appropriate.The study took into account the forecasting models Generalized Autoregressive Conditional Heteroscedasticity-symmetric GARCH (2,1), Exponential GARCH-EGARCH (2,1), and Threshold GARCH-TGARCH (2,1). Heteroscedasticity tests, such as the Lagrange Multiplier (LM) test for ARCH, were employed to determine the presence of heteroscedasticity in the return series’ residual in order to observe the ARCH effect. We can employ ARCH/GARCH models if the ARCH effect is present. To determine whether the return series are stationary, tests for stationarity such the Augmented Dicky-Fuller test were done. To determine if the return series were normally distributed, tests for normality such as the Jarque-Bera test were applied.Using the Mean Absolute Error and Root Mean Square Error error statistics, the effectiveness of these GARCH models was assessed. TGARCH scored well based on these evaluations and it aids in capturing the leverage impact, volatility clustering, forecasting accuracy, and differentiating the asymmetric influence between good and negative news.
[1] Abdalla, S., Z., & Winker, P. (2012). Modelling Stock Market Volatility Using Univariate GARCH Models: Evidence from Sudan and Egypt. International Journal of Economics and Finance, 4(6), 161 – 176.
[2] Babatunde, O. A. (2013). Stock Market Volatility and Economic Growth in Nigeria. International Review of Management and Business Research, 2(1), 201 – 209.
[3] FauziaMubarik,&Attiya Yasmin Javid. (2018), the Impact of Macroeconomic Volatility on Stock Return Volatility: Evidence from Pakistan Stock Market. Pakistan Business Review, 826 – 842.
[4] Godfrey Joseph,& Ismail. (2020). Modelling Volatility in the Stock Market for Accuracy in Forecasting. International Journal of Recent Technology and Engineering, 8(5).
[5] Dana AL- Najjar. (2016). Modelling and Estimation of Volatility using ARCH/ GARCH Models in Jordan’s Stock Market. Asian Journal of Finance and Accounting, 8(1), 152-167.
[6] Dohyunchun, Hooncho, &DoojinRyu. (2019, January). Forecasting the Korea Composite Stock Price Index 200 Spot Volatility Measures. Statistical Mechanics and its Applications Journal, 514, 156-166.
[7] Hussainey, K.,&Khanh Ngoc, L. (2009). The impact of macroeconomic indicators on Vietnamese stock prices. Journal of Risk Finance, 10(4), 321-332.
[8] Huthaifa, Alaa, & Ahmad. (2020). Modelling and Forecasting the Volatility of Cryptocurrencies: A Comparison of Non- Linear GARCH type Models. International Journal of Financial Research, 11(4), 346-356.
[9] Jelilov, Paul, & Usman. (2020). Testing the nexus between stock market returns and inflation in Nigeria: Does the effect of COVID‐19 pandemic matter?. Journal of Public Affairs an international journal.
[10] Liu, H.C., & Hung J.C. (2010). Forecasting S&P-100 stock index volatility: The role of volatility asymmetry and distributional assumption in GARCH models. Expert Systems with Applications, 37, 4928-4934.
[11] Mazur, Dang, & Miguel. (2020). COVID-19 and the march 2020 stock market crash. Evidence from S&P1500. Finance Research Letters, 10(4).
[12] Narayan, & Reddy. (2020). The Dynamics of Macroeconomic Variables in Indian Stock Market: A Bai- Perron Approach. Journal of Macroeconomics and Finance in Emerging Market Economics, 13(1).
[13] PhichHangOu,&Hengshan Wang. (2011, July). Modeling and Forecasting Stock Market Volatility by Gaussian Processes based on GARCH, EGARCH and GJR Models. Paper Presented at International Conference of Applied and Engineering Mathematics, Proceedings of World Congress of Engineering, London, U.K. 6-8.

Received :29August2022
Accepted :13December2022
Published :30December2022
DOI: 10.30726/esij/v9.i4.2022.94002

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