I.A.S.R.I, Library Avenue, New Delhi-110012 Chairperson: Dr. Prajneshu Abstract: Well-known Box-Jenkins Autoregressive Integrated Moving Average (ARIMA)

methodology has virtually dominated analysis of time-series data, particularly during the period 1930-‘80. However, one limitation of this methodology is that it is not capable of modelling those data sets that depict volatility. Fortunately, to this end, Autoregressive Conditional Heteroscedastic (ARCH) families of parametric nonlinear time-series models have been proposed during last two decades or so. Various aspects of these models are thoroughly discussed. Estimation procedure for fitting ARCH models is also described. Modelling and forecasting of volatile black pepper price time-series data is carried out. An extension of ARCH family to incorporate the effect of regressor in mean and variance equations is also considered. Details for application of AR(p)-ARCH-in-Mean model to describe volatility in onion price time-series data are provided. In the end, some extensions, like Generalized ARCH (GARCH), and Threshold ARCH (TARCH), are briefly mentioned