Improving Prediction of Gold Prices through inclusion of Macroeconomic Variables.

This paper uses a method based on multivariate power-normal distribution for predicting future gold prices in Malaysia. First let r(r) be the vector consisting of the month-/ values of m selected macroeconomic variables, and gold price. The month-(/+l) gold price is then modelled to be dependent on...

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Publicado en:Pertanika Journal of Social Sciences & Humanities Vol. 24; pp. 101 - 108
Autores principales: Beh, W. L., Pooi, A. H.
Formato: Artículo
Publicado: Universiti Putra Malaysia Oct2016 Special Issue
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Acceso en línea:Ver este registro en EBSCOhost
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        atl: Improving Prediction of Gold Prices through inclusion of Macroeconomic Variables.
      aug:
        au:
          Beh, W. L.
          Pooi, A. H.
        affil:
          Department of Physical and Mathematical Science, Faculty of Science, Universiti Tunku Abdul Rahman, 31900 Kampar, Perak, Malaysia
          Sunway University Business School, Sunway University, Bandar Sunway, 47500 Selangor, Malaysia
      su:
        Gold sales & prices
        Macroeconomics
        Economic conditions in Malaysia
        Parsimonious models
        Prediction models
      sug:
        subj:
          Gold sales & prices
          Macroeconomics
          Economic conditions in Malaysia
          Parsimonious models
          Prediction models
      keyword:
        macroeconomic variables
        Multivariate power-normal distribution
        parsimonious model
        prediction interval
      ab: This paper uses a method based on multivariate power-normal distribution for predicting future gold prices in Malaysia. First let r(r) be the vector consisting of the month-/ values of m selected macroeconomic variables, and gold price. The month-(/+l) gold price is then modelled to be dependent on the present and M onpastvalues r (/), r ( / - l) , . . . , r( / - / + l) via a conditional distribution which is derived from a [{m + \)l + l]-dimensional powernormal distribution. The mean of the conditional distribution is an estimate of the month- (/+1) gold price. Meanwhile, the 100(a/2)% and 100(l-cx/2)% points of the conditional distribution can be used to form an out-of-sample prediction interval for the month-(HT) gold price. For a given value of /, we select various combinations of m variables from a pool of 17 selected macroeconomic variables in Malaysia, and obtain the combinations of which the corresponding mean absolute percentage errors (MAPE) are relatively smaller while the coverage probabilities and average lengths of the prediction interval are still satisfactory. It is found that the parsimonious model is one of which / = 2, m = 1 and involving the macroeconomic variable derived from the Gross Domestic Product, Kuala Lumpur Composite Index or Import Trade.
      pubtype: Academic Journal
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    language: English
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