[
AbdElaal, M.A. (2011). Modeling and forecasting time varying stock return volatility in the Egyptian stock market. International Research Journal of Finance and Economics, 78.
]Search in Google Scholar
[
Aggarwal, R. (1988). Stock index futures and cash market volatility. Review of Futures Markets, 7(2), 290–299.
]Search in Google Scholar
[
Akgül, I., Sayyan, H. (2005). İMKB-30 Hisse Senedi Getirilerinde Volatilitenin Asimetrik Koşullu Değişen Varyans Modelleri ile Öngörüsü. TC Marmara Üniversitesi Bankacılık ve Sigortacılık Yüksekokulu 2005 Geleneksel Finans Sempozyumu Tebliğleri.
]Search in Google Scholar
[
Al Freedi, A., Shamiri, A., Isa, Z. (2012). A study on the behavior of volatility in Saudi Arabia stock market using symmetric and asymmetric GARCH models. Journal of Mathematics & Statistics, 8(1).
]Search in Google Scholar
[
Ali, I. (2016). Stock Market Volatility and Returns: A Study of NSE & BSE in India. International Journal of Humanities & Social Science Studies, 202–211.
]Search in Google Scholar
[
Angabini, A., Wasiuzzaman, S. (2011). GARCH models and the financial crisis-A study of the Malaysian. The International Journal of Applied Economics and Finance, 5(3), 226–236. DOI: 10.3923/ijaef.2011.226.236.
]Search in Google Scholar
[
Asteriou, D., Hall, S.G. (2011). ARIMA models and the Box–Jenkins methodology. Applied Econometrics, 2(2), 265–286.
]Search in Google Scholar
[
Awartani, B.M., Corradi, V. (2005). Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries. International Journal of forecasting, 21(1), 167–183.
]Search in Google Scholar
[
Banumathy, K., Azhagaiah, R. (2015). Modelling Stock Market Volatility: Evidence from India. Managing Global Transitions: International Research Journal, 13(2).
]Search in Google Scholar
[
Bekaert, G., Harvey, C.R. (1997). Emerging Equity Market Volatility. Journal of Financial Economics, 43, 29–78.
]Search in Google Scholar
[
Black, F. (1976). Studies of stock market volatility changes. Proceedings of the American Statistical Association, Business & Economic Statistics Section, 1976.
]Search in Google Scholar
[
Bollerslev, T. (1986). Generalised Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31, 307–327.
]Search in Google Scholar
[
Brooks, C., Burke, S.P., Persand, G. (2003). Multivariate GARCH models: software choice and estimation issues. Journal of Applied Econometrics, 18(6), 725–734.
]Search in Google Scholar
[
Dickey, D.A., Fuller, W.A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427–431.
]Search in Google Scholar
[
Ding, Z., Granger, C.W.J., Angle, R.F. (1993). A long memory property of stocks market returns and a new Model. Journal of Empirical Finance, 1(1), 83–106. DOI: 10.1016/0927-5398 (93)90006-D.
]Search in Google Scholar
[
Emenike, K.O. (2010). Modelling stock returns volatility in Nigeria using GARCH models.
]Search in Google Scholar
[
Engle, R.F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI: 10.2307/1912773.
]Search in Google Scholar
[
Engle, R., Granger, C. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251–276. DOI: 10.2307/1913236.
]Search in Google Scholar
[
Ezzat, H. (2012). The application of GARCH and EGARCH in modeling the volatility of daily stock returns during massive shocks: The empirical case of Egypt.
]Search in Google Scholar
[
Fama, E. F. (1965). The behavior of stock market prices. Journal of Business, 38, 34–105. DOI: 10.1086/29474.
]Search in Google Scholar
[
Floros, C. (2008). Modelling Volatility Using GARCH Models: Evidence from Egypt and Israel. Middle Eastern Finance and Economics, 2, 31–41.
]Search in Google Scholar
[
Frimpong, J.M., Oteng-Abayie, E.F. (2006). Modelling and forecasting volatility of returns on the Ghana stock exchange using GARCH models.
]Search in Google Scholar
[
Glosten, L.R., Jagannathan, R., Runkle, D.E. (1993). On the relation between expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779–1801. DOI: 10.1111/j.1540-6261.1993.tb05128.x.
]Search in Google Scholar
[
Gokan, S. (2000). Forecasting volatility of emerging stock markets: Linear versus non-linear GARCH models. Journal of Forecasting, 19, 499–504. DOI: 10.1002/1099-131x (200011)19:6<499:aid-for745>3.0.co;2-p.
]Search in Google Scholar
[
Gökbulut, R., Pekkaya, M. (2014). Estimating and Forecasting Volatility of Financial Markets Using Asymmetric GARCH Models: An Application on Turkish Financial Markets. International Journal of Economics and Finance, 6(4), 23–33. DOI: 10.5539/ijef.v6n4p23.
]Search in Google Scholar
[
Harris, R., Sollis, R. (2003). Applied time series modelling and forecasting. Wiley.
]Search in Google Scholar
[
Hsieh, D.A. (1989). Modeling Heteroscedasticity in daily foreign-exchange rates. Journal of Business & Economic Statistics, 7, 307–317. DOI: 10.2307/1391528.
]Search in Google Scholar
[
Joshi, P. (2014). Forecasting volatility of Bombay stock exchange. International journal of current research and academic review, 2(7), 222–230.
]Search in Google Scholar
[
Kumar, A., Mishra, U.S. (2019a). Testing the Volatility and Model Designing for International Tourist Footfalls in India: Applying GARCH Family Models. Journal of Advanced Research in Dynamical and Control Systems, 11(SP-10), 212–220. DOI: 10.5373/JARDCS/V11SP10/20192793.
]Search in Google Scholar
[
Kumar, A., Mishra, U.S. (2019b). Volatility clustering and price dependency of turmeric amongst the state of India. Journal of Advanced Research in Dynamical and Control Systems, 11(SP-10), 228–235. DOI: 10.5373/JARDCS/V11SP10/20192795.
]Search in Google Scholar
[
Kumar, A., Biswal, S.K. (2019). Impulsive Clustering and Leverage Effect of Emerging Stock Market with Special Reference to Brazil, India, Indonesia, and Pakistan. Journal of Advanced Research in Dynamical and Control Systems, 11(11), 33–37. DOI: 10.5373/jardcs/v11sp11/20192925.
]Search in Google Scholar
[
Kumar, A. (2018a). Prospects of GST on Indian economy-An econometric approach. International Journal of Management, IT and Engineering, 8(7), 147–164.
]Search in Google Scholar
[
Kumar, A. (2018b). A Study on Risk Hedging Strategy: Efficacy of Option Greeks. Abhinav National Monthly Refereed Journal of Research in Commerce & Management, 7(4), 77–85.
]Search in Google Scholar
[
Kumar, A. (2019). Relationship and Co-Integration of Nifty 50 Future Index with Advanced Country Future Index. Journal of Advanced Research in Dynamical and Control Systems, 11(SP-10), 197–203. DOI: 10.5373/JARDCS/V11SP10/20192791.
]Search in Google Scholar
[
Kumar, A., Biswal, S.K., Swain, P.K. (2019a). A dynamic association between stock markets, Forex, gold and oil prices in Indian context. Revista ESPACIOS, 40(06).
]Search in Google Scholar
[
Kumar, A., Biswal, S.K., Swain, P.K. (2019b). Volatility Clustering and Model Development for NSE Index Future and Spot Index through ARCH/GARCH Models. Journal of Advanced Research in Dynamical and Control Systems, 11(10), 221–227. DOI: 10.5373/jardcs/v11sp10/20192794.
]Search in Google Scholar
[
Mahadevan, M.S. (2014). A Study on Volatility Modelling of BSE Sensex. International Journal of Banking, Risk and Insurance, 2(1), 32.
]Search in Google Scholar
[
Mandelbrot, B. (1963). The variation of certain speculative prices. Journal of Business, 36, 394–414. DOI: 10.1086/294632.
]Search in Google Scholar
[
Miron, D., Tudor, C. (2010). Asymmetric conditional volatility models: Empirical estimation and comparison of forecasting accuracy. Romanian Journal of Economic Forecasting, 13(3), 74–92.
]Search in Google Scholar
[
Nelson, D.B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347–370.
]Search in Google Scholar
[
Pagan, A. (1996). The econometrics of financial markets. Journal of Empirical Finance, 3(1), 15–102.
]Search in Google Scholar
[
Peter, C.B., Phillips, Perron, P. (1988). Testing for a Unit Root in Time Series Regression. Biometrika, 75(2), 335–346. DOI: 10.2307/2336182.
]Search in Google Scholar
[
Rashid, A., Ahmad, S. (2008). Predicting stock returns volatility: An evaluation of linear vs. nonlinear methods. International Research Journal of Finance and Economics, 20(31), 141–150.
]Search in Google Scholar
[
Sahoo, J., Herlina, Khurshid, M., Mishra, D., Kumar, A., Sahoo, J. (2024). Fuelling the future: A comparative exploration of energy prices in India and the US. Journal of Statistics and Management System, 27(1), 187–200. DOI: 10.47974/JSMS-1212.
]Search in Google Scholar
[
Su, C. (2010). Application of EGARCH model to estimate the financial volatility of daily returns: The empirical case of China. The University of Gothenburg, Master Degree Project No. 2010:142.
]Search in Google Scholar
[
Taylor, S. (1994). Modelling Stochastic Volatility: A Review and Comparative Study. Mathematical Finance, 4, 183–204. DOI: 10.1111/j.1467-9965.1994.tb00057.x.
]Search in Google Scholar
[
Tripathy, T., Gil-Alana, L.A. (2017). Macroeconomic Regimes and Foreign Exchange Rate Volatility in India. IUP Journal of Applied Economics, 16(3), 25–46.
]Search in Google Scholar
[
Zokoian, J.M. (1994). Threshold Heteroscedasticity Models. Journal of Economic Dynamics and Control, 18(5), 931–955. DOI: 10.1016/0165-1889 (94) pp. 90039-6.
]Search in Google Scholar
, 