Forecasting the World Gold Price Using Optimized Neuro-Fuzzy with Genetic Algorithm (Ga-Anfis) and Smooth Transition Regression with Long Memory (Fi-Star) Modelling

Posted: 24 Feb 2012

See all articles by Ali Habibnia

Ali Habibnia

London School of Economics & Political Science (LSE); City University London - The Business School; University of Tehran - Faculty of Economics

Date Written: November 1, 2010

Abstract

Indubitably investing in the gold market has been one of the most captivating investments in the world over the centuries. The gold standard has been played a key role in the international monetary system here before. It is utilized to mint coins and gold bullion as the money reserves. Because of the volatile and uncertain nature of the paper currency market, investors alternatively place gold as a safe asset and as the hedge against inflation in their portfolios to boost the performance and reduce the risk of their investments.

So an accurate gold price forecast is vital for central banks, Hedgers and speculators.

The main purpose of this paper is to present a novel and precise hybrid model to forecast the world gold price. In the first stage, predictability and nonlinearity assumption and chaoticity of time series data, have been examined by Largest Lyapunov Exponent (LLE) and BDS test. The results of the tests showed the gold price is chaotic and nonlinear and using nonlinear models recognized appropriate for this kind of data. Considering markets are highly correlated, the lags of world oil price, US dollar index and stock market index has been recognized significant for forecasting gold price.

The price has been modeled in two methods to compare, the first one is based on the combination of artificial intelligence techniques which structured optimized Adaptive Neuro-Fuzzy Inference System by Genetics Algorithm (GA-ANFIS) that contains multilayer feed forward neural network with back propagation, and takagi sugeno fuzzy inference system. Since there are a numerous combination of inputs can be exercise and there is exist a complex and nonlinear relationship between independent variables, in this paper, the idea of a combination of genetic algorithm and a neuro-fuzzy system is arose to find the best of the best significant inputs and optimal lags.

The second model, which is recognized appropriate for the gold data, is Logistic Smooth Transition Autoregressive (STAR) concerning long memory effect and the original model changed to FI-LSTAR in order to estimate accurate predictions, and US dollar index has been accepted as a transition factor. In this method STEPLS technique has been used to find the optimal lags. The results show that the hybrid artificial intelligence model produced more accurate forecasts and generally input selection and choosing appropriate model based on the nature of the data can play vital roles in forecast models.

Suggested Citation

Habibnia, Ali, Forecasting the World Gold Price Using Optimized Neuro-Fuzzy with Genetic Algorithm (Ga-Anfis) and Smooth Transition Regression with Long Memory (Fi-Star) Modelling (November 1, 2010). Available at SSRN: https://ssrn.com/abstract=2010545

Ali Habibnia (Contact Author)

London School of Economics & Political Science (LSE) ( email )

Houghton Street
London, WC2A 2AE
United Kingdom

City University London - The Business School ( email )

106 Bunhill Row
London, EC1Y 8TZ
United Kingdom

University of Tehran - Faculty of Economics ( email )

Tehran
Iran

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