Gold Price Modeling Using System Dynamics

Abstract

The global gold market has recently attracted a lot of attention and the price of gold is relatively higher than its historical trend. This paper constitutes the first exercise of system dynamics applied to predict gold price in monthly frequency from January 2010 to June 2011. Rather than static forecasting characteristics found in another quantitative method, time-series, system dynamics allows possibility for prediction based on capturing causal interactions and consequently the feedback loops usually found in a complex system behaviour such as the gold price system. Therefore, it was expected that moving toward forecasting method utilizing system dynamics model could result in better prediction of the gold price. Our paper supports such hypothesis. Having ability to take into account of qualitative factors particularly political chaos and economic crisis events, the model developed in this paper reduces the prediction error, mean absolute percent error (MAPE), to merely 2% compared to approximately 9% error found with Holt-Winter Exponential Smoothing and 11% error using Box-Jenkins Method.

Sociocultural Behavior Sensemaking: State of the Art in Understanding the Operational Environment

“…The literature describes many examples of using system dynamics for forecasting, especially for various forms of demand (Niu & Gillard, 1994;Tharmmaphornphilas, Loharsiriwat, & Vannasetta, 2012;Najjar, 2013). Tharmmaphornphilas, Loharsiriwat, and Vannasetta (2012) compared their results to that of a Box Jenkins (1970) forecast and attributed their improved prediction results to the inclusion of qualitative influences ignored by other methods and of interactions and feedback in the system that are not apparent from an analysis of the data alone.…”

“…Tharmmaphornphilas, Loharsiriwat, and Vannasetta (2012) compared their results to that of a Box Jenkins (1970) forecast and attributed their improved prediction results to the inclusion of qualitative influences ignored by other methods and of interactions and feedback in the system that are not apparent from an analysis of the data alone.…”

The performance of hybrid ARIMA-GARCH modeling in forecasting gold price

“…There are many forecast models used in forecasting gold price. The models which has been used recently are Box-Jenkins models (Miswan et al, 2013;Khashei et al, 2008), back propagation neural network (Yuan, 2012;Zhou et al, 2012;Parisi et al, 2008), system dynamics model (Tharmmaphornphilas et. al, 2012), varying-coefficient regression model (Zhang et al, 2011), data mining methods (Mustaffa &Yusof, 2011), jump-and-dip diffusion (Shafiee and Topal, 2010), artificial intelligence models, multiple linear regression models (Ismail et al, 2009) or can be the hybrid of the above mentioned models (Khashei et al, (2009(Khashei et al, ( , 2008; Hadavandi et al, 2010;Asadi et al, 2012).…”