Estimation of Monthly Gold Prices using Non-Gaussian Innovations

Authors

  • Ajanthy Mahalingam
  • T. S. G. Peiris Department of Mathematics, University of Moratuwa, Moratuwa 10400

Keywords:

GARCH Modeling, Gold Prices, Time Series Analysis, Volatility Clustering

Abstract

The global gold market has recently attracted a lot of attention and the price of gold has been fluctuating over the years with high volatility. The frequent fluctuation of gold prices has affected many related sectors and stock market indices and the development of a model to estimate gold prices is of much economic importance to many stake holders. Thus this study was carried out to develop a GARCH model using monthly gold price series (in US$) for the period from December 1978 to December 2012. GARCH models under normal, non-normal innovations: t-distribution and generalized error distribution were selected for the data. After several levels of statistical screening the ARPARCH with t distribution was found to be the best model for the gold price data.  The model was trained using December 1978 to December 2010 and validated using January 2011 to December 2012. There was a very strong correlation (r = 0.98, p = 0.000) between observed data and the fitted data for both training and validation data sets.

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Published

2015-06-15

How to Cite

Mahalingam, A., & Peiris, T. S. G. (2015). Estimation of Monthly Gold Prices using Non-Gaussian Innovations. Asian Journal of Business and Management, 3(3). Retrieved from https://ajouronline.com/index.php/AJBM/article/view/2688

Issue

Vol. 3 No. 3 (2015): June 2015

Section

Articles

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