Statistical modelling for forecasting volatility in potato prices using ARFIMA-FIGARCH model

DIPANKAR MITRA; RANJIT KUMAR PAUL; A K PAUL

doi:10.56093/ijas.v88i2.79205

Authors

DIPANKAR MITRA Ph D Scholar, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012
RANJIT KUMAR PAUL Scientist, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012
A K PAUL Principal Scientist, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012

https://doi.org/10.56093/ijas.v88i2.79205

Keywords:

ARFIMA, FIGARCH, GPH, Long memory, Potato, Volatility

Abstract

This paper investigates the presence of long memory both in mean and volatility in the potato prices in Agra and Amritsar markets of India, using the Autoregressive fractionally integrated moving average (ARFIMA) and Fractionally integrated generalized autoregressive conditional heteroscedastic (FIGARCH) models. Long memory tests are carried out both for the returns and squared return series. The results of GPH estimator indicate the existence of long memory in the price data.The ARFIMA model with error following FIGARCH process is fitted to return prices of potato for each of the two markets. At the end, the forecasting performance of fitted ARFIMA-FIGARCH models are carried out in terms of RMAPE and RMSE and the residuals are also examined to check adequacy of the fitted models.

Downloads

Download data is not yet available.

References

Baillie R T. 1996. Long memory processes and fractional integration in econometrics. Journal of Econometrics 73: 5–9. DOI: https://doi.org/10.1016/0304-4076(95)01732-1

Bolerslev T. 1986. Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics 31: 307–27. DOI: https://doi.org/10.1016/0304-4076(86)90063-1

Engle R F. 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50: 987–1007. DOI: https://doi.org/10.2307/1912773

Engle R F and Bollerslev T. 1986. Modelling the persistence of conditional variances. Econometric Reviews 5: 1–50. DOI: https://doi.org/10.1080/07474938608800095

Granger C W J and Joyeux R. 1980. An introduction to long-memory time series models and fractional differencing. Journal of Time Series Analysis 4: 221–38.

Paul R K, Gurung B and Paul A K. 2014. Modelling and forecasting of retail price of arhar dal in Karnal, Haryana. Indian Journal of Agricultural Sciences 85(1): 69–72.

Paul R K, Gurung B and Samanta S. 2015a. Analyzing the effect of dual long memory process in forecasting agricultural prices in different markets of India. International Journal of Empirical Finance 4(4): 235–49.

Paul R K, Gurung B, Samanta S and Paul A K. 2015b. Modeling long memory in volatility for spot price of lentil with multi-step ahead out-of-sample forecast using AR-FIGARCH model. Economics Affairs 60(3): 457–66. DOI: https://doi.org/10.5958/0976-4666.2015.00065.0

Paul R K, Gurung B, Paul A K and Samanta, S. 2016a. Long memory in conditional variance. Journal of Indian Society of Agricultural Statistics 70(3): 243–54.

Paul R K, Rana S and Saxena R. 2016b. Effectiveness of price forecasting techniques for capturing asymmetric volatility for onion in selected markets of Delhi. Indian Journal of Agricultural Sciences 86(3): 303–9.

Tayafi M and Ramanathan T V. 2012. An overview of FIGARCH and related time-series models. Australian Journal of Statistics 141: 175–96.