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Gold Price Forecasting

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using two method. multple regression and ARIMA

Économie & financeBusiness
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HenryB-A5-0329-6 EdwardB-A6-0019-9 VeasnaB-A6-0047-1 Marco B-A6-0193-3
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Findings of Eric J Levin and Robert E Wright ,[object Object]
 
 
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Regression Model- Determinants ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Regression Model- Determinants (continued)
Gold price= 115.5702+2.5385CPI-0.0198DOWindex-2.2859exchange rate +4.849Oil price+14.90248real interest rate+95.276Dummy Regression Model- Result
Statistical result Dependent Variable: GOLD_PRICE Method: Least Squares Date: 12/12/08 Time: 16:44 Sample: 1 431 Included observations: 431 Variable Coefficient Std. Error t-Statistic Prob.   C 115.5702 27.72153 4.168968 0.0000 CPI 2.538530 0.129165 19.65334 0.0000 DOW_INDEX -0.019855 0.001615 -12.29644 0.0000 EXCHANGE_RATE -2.285975 0.231132 -9.890334 0.0000 OIL 4.844002 0.181572 26.67817 0.0000 REAL_INTEREST_RATE 14.90248 1.238273 12.03489 0.0000 DUMMY 95.27625 9.008833 10.57587 0.0000 R-squared 0.886812      Mean dependent var 362.1076 Adjusted R-squared 0.885211      S.D. dependent var 154.4284 S.E. of regression 52.32121      Akaike info criterion 10.76879 Sum squared resid 1160704.      Schwarz criterion 10.83483 Log likelihood -2313.674      F-statistic 553.6656 Durbin-Watson stat 0.266398      Prob(F-statistic) 0.000000
Regression Model- Special event and Dummy variable ,[object Object],[object Object],A dummy variable should be added when there is a market crash in order to increase the impact on gold price forecasting.
Special event and Dummy variable- continued ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Huge Fluctuation of gold price from 1979-1983 is due to war and bubble explosion happened simultaneously, which is very rare. And this kind event can not be predicted at all. Therefore , we can ignore it, that's why there is a big residual in our result.
Forecasting the gold price of 2007-2008 MAPE=0.10 Regression Model- Accuracy and implication Regression model can not account for significant fluctuation in SHORT PERIOD caused by irrational investing behavior, gold future speculation etc. Such as the Global Financial crisis 2008 .
 
ARIMA method- Pattern of gold price
ARIMA method- First difference
ARIMA method- Autocorrelation and Partial Autocorrelation
ARIMA method- Results of different ARIMA models ARIMA Coefficient Significant P-value of LBQ at lag12 ,24,36,48 AIC BIC (1,1,0) Lag12,24,36,48 6.131482 12.26 (2.1.0) Do not have 6.117008 12.2511 (0,1,1) Lag12,24,36,48 6.123105 12.2481 (0,1,2) One insignificant 0.065 6.1197 12.2538 (1,1,1) One insignificant Lag24,36,48 6.122717 12.2567 (2,1,2) Insignificant AR(1) and MA(1) - 6.115005 12.2672 (1,1,2) All insignificant - 6.123772 12.2669
ARIMA method- Graphical Result
Forecasting of gold price from 2007 January to June by using previous data: ARIMA model can only forecast next several period ARIMA method- Forecasting and Implication Period Forecasts Lower bound Upper bound Actual January 637.221 594.473 679.97 629.418 February 633.175 566.945 699.405 631.166 March 631.144 550.416 711.872 664.745 April 631.327 539.256 723.398 654.895 May 631.639 529.364 733.914 679.368 June 631.671 519.998 743.344 666.919
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Gold Price Forecasting

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  • 11. Gold price= 115.5702+2.5385CPI-0.0198DOWindex-2.2859exchange rate +4.849Oil price+14.90248real interest rate+95.276Dummy Regression Model- Result
  • 12. Statistical result Dependent Variable: GOLD_PRICE Method: Least Squares Date: 12/12/08 Time: 16:44 Sample: 1 431 Included observations: 431 Variable Coefficient Std. Error t-Statistic Prob.   C 115.5702 27.72153 4.168968 0.0000 CPI 2.538530 0.129165 19.65334 0.0000 DOW_INDEX -0.019855 0.001615 -12.29644 0.0000 EXCHANGE_RATE -2.285975 0.231132 -9.890334 0.0000 OIL 4.844002 0.181572 26.67817 0.0000 REAL_INTEREST_RATE 14.90248 1.238273 12.03489 0.0000 DUMMY 95.27625 9.008833 10.57587 0.0000 R-squared 0.886812      Mean dependent var 362.1076 Adjusted R-squared 0.885211      S.D. dependent var 154.4284 S.E. of regression 52.32121      Akaike info criterion 10.76879 Sum squared resid 1160704.      Schwarz criterion 10.83483 Log likelihood -2313.674      F-statistic 553.6656 Durbin-Watson stat 0.266398      Prob(F-statistic) 0.000000
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  • 15. Forecasting the gold price of 2007-2008 MAPE=0.10 Regression Model- Accuracy and implication Regression model can not account for significant fluctuation in SHORT PERIOD caused by irrational investing behavior, gold future speculation etc. Such as the Global Financial crisis 2008 .
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  • 17. ARIMA method- Pattern of gold price
  • 18. ARIMA method- First difference
  • 19. ARIMA method- Autocorrelation and Partial Autocorrelation
  • 20. ARIMA method- Results of different ARIMA models ARIMA Coefficient Significant P-value of LBQ at lag12 ,24,36,48 AIC BIC (1,1,0) Lag12,24,36,48 6.131482 12.26 (2.1.0) Do not have 6.117008 12.2511 (0,1,1) Lag12,24,36,48 6.123105 12.2481 (0,1,2) One insignificant 0.065 6.1197 12.2538 (1,1,1) One insignificant Lag24,36,48 6.122717 12.2567 (2,1,2) Insignificant AR(1) and MA(1) - 6.115005 12.2672 (1,1,2) All insignificant - 6.123772 12.2669
  • 21. ARIMA method- Graphical Result
  • 22. Forecasting of gold price from 2007 January to June by using previous data: ARIMA model can only forecast next several period ARIMA method- Forecasting and Implication Period Forecasts Lower bound Upper bound Actual January 637.221 594.473 679.97 629.418 February 633.175 566.945 699.405 631.166 March 631.144 550.416 711.872 664.745 April 631.327 539.256 723.398 654.895 May 631.639 529.364 733.914 679.368 June 631.671 519.998 743.344 666.919
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