Affiliations: [a] The Graduate School, ICAR-Indian Agricultural Research Institute, New Delhi, India | [b] ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP, India | [c] ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
Correspondence:
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Corresponding author: Ranjit Kumar Paul, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India. E-mail: ranjit.paul@icar.gov.in.
Abstract: Volatility is a matter of concern for time series modeling. It provides valuable insights into the fluctuation and stability of concerning variables over time. Volatility patterns in historical data can provide valuable information for predicting future behaviour. Nonlinear time series models such as the autoregressive conditional heteroscedastic (ARCH) and the generalized version of the ARCH model, i.e. generalized ARCH (GARCH) models are popularly used for capturing the volatility of a time series. The realization of any time series may have significant statistical dependencies on its distant counterpart. This phenomenon is known as the long memory process. Long memory structure can also be present in volatility. Fractionally integrated volatility models such as the fractionally integrated GARCH (FIGARCH) model can be used to capture the long memory in volatility. In this paper, we derived the out-of-sample forecast formulae along with the forecast error variances for the AR (1) -FIGARCH (1, d, 1) model by recursive use of conditional expectations and conditional variances. For empirical illustration, the modal spot prices of onion for Delhi, Lasalgaon and Bengaluru markets, India and S&P 500 index (close) data are used.
Keywords: Long memory, nonlinear time series models, GARCH, volatility
