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Kumar, R. and Dhankar, R.S. (2010) Empirical Analysis of Conditional Heteroskedasticity in Time Series of Stock Returns and Asymmetric Effect on Volatility. Global Business Review, 11, 21-33.
https://doi.org/10.1177/097215090901100102

has been cited by the following article:

• TITLE: A Mixed Model Approach to Forecasting CO2 Emissions from Sudan’s Energy Sector

  AUTHORS: Maria H. Mohamed, Altaiyb Omer Ahmed Mohmmed, Mohammedelameen Eissa Qurashi, Mubarak H. Elhafian

  KEYWORDS: ARCH, ARIMA, Dioxide Emissions, Energy Sector

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.14 No.2, May 30, 2026

  ABSTRACT: The study models annual (1970-2023) percentages of energy-related CO2 emissions in Sudan using ARIMA for the mean process and ARCH for conditional variance. After differencing to attain stationarity, several ARIMA ranks are compared; residual heteroskedasticity motivates an ARCH extension. The authors finally adopt an ARCH (2) specification and generate 10-year forecasts. They conclude that the ARCH (2) model better captures volatility and yields more reliable predictions than the tested ARIMA alternatives.