Autoregressive Integrated Moving Average Model for Gold Price Forecasting : Evidence from the Indian Market

Authors

  •   Khujan Singh Assistant Professor, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar - 125 001, Haryana
  •   Anil Kumar Research Scholar, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar - 125 001, Haryana

DOI:

https://doi.org/10.17010/ijrcm/2017/v4/i3/118920

Keywords:

Gold Prices

, Time Series, Autoregressive Integrated Moving Average (ARIMA), Investment, Stationary, Forecasting

F470

, G170, G130, G150

Paper Submission Date

, March 9, 2017, Paper sent back for Revision, July 12, Paper Acceptance Date, September 15, 2017.

Abstract

The present study was conducted to forecast the gold prices in India by employing ARIMA (1, 1, 2) model on time series data for short term. The stationarity of time series data was tested by using the ADF unit root test. To overcome the problem of autocorrelation, Breusch - Godfrey serial correlation was conducted. The study forecasted gold prices within sample and post sample forecast. Actual values of gold prices and the forecasted values of gold prices moved in the same direction very closely. The post sample forecasted values of gold prices revealed an increasing trend. The predicted six months values of gold prices probably indicated reasonable returns for investors who held gold in their financial portfolios. Hence, the ARIMA (1, 1, 2) model was found to be the best fit to forecast short term gold prices on time series data.

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Published

2017-09-01

How to Cite

Singh, K., & Kumar, A. (2017). Autoregressive Integrated Moving Average Model for Gold Price Forecasting : Evidence from the Indian Market. Indian Journal of Research in Capital Markets, 4(3), 33–43. https://doi.org/10.17010/ijrcm/2017/v4/i3/118920

Issue

Volume 4, Issue 3, July-September 2017

Section

Articles
Crossref
Scopus
Google Scholar
Europe PMC

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