Gold Price Forecasting


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using two method. multple regression and ARIMA

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  • Gold Price Forecasting

    1. 1. HenryB-A5-0329-6 EdwardB-A6-0019-9 VeasnaB-A6-0047-1 Marco B-A6-0193-3
    2. 2. <ul><li>Why we choose this topic </li></ul><ul><li>Data Description </li></ul><ul><li>Data ana lys is </li></ul><ul><li>Conclusion </li></ul>
    3. 3. Findings of Eric J Levin and Robert E Wright <ul><ul><li>Gold price and Inflation go simultaneously: Gold can be a inflation hedge in the long-run </li></ul></ul>
    4. 6. <ul><ul><li>Monthly gold price </li></ul></ul><ul><ul><ul><li>From 1973.Jan to 2008.Nov </li></ul></ul></ul><ul><ul><ul><li>431 data points </li></ul></ul></ul><ul><ul><li>London pm fix ,quoted in us dollars . </li></ul></ul><ul><ul><ul><li>The market-clearing price of gold set twice a day in London is commonly referred to as the London fixing price (am or pm). </li></ul></ul></ul><ul><ul><ul><li>The price is also the international benchmark price. </li></ul></ul></ul>
    5. 7. <ul><ul><li>Multi-linear regression </li></ul></ul><ul><ul><li>Forecast gold price by some determinates, such as CPI, exchange rate, DJ index etc . (Source from U.S department of labor and Federal Reserve) </li></ul></ul><ul><ul><li>ARIMA </li></ul></ul><ul><ul><li>Forecast gold price only by previous prices, regardless of other factors. </li></ul></ul>
    6. 9. Regression Model- Determinants <ul><li>CPI of America </li></ul><ul><ul><li>- General inflation of all goods </li></ul></ul><ul><li>Exchange rate of USD against world currencies </li></ul><ul><ul><li>Gold price is in USD </li></ul></ul><ul><ul><li>Exchange rate and the economy condition </li></ul></ul><ul><li>Dow-Jones index </li></ul><ul><ul><li>- Less risk investment tool when stock market performance is bad </li></ul></ul>
    7. 10. <ul><li>Oil price </li></ul><ul><ul><li>Gold price and Oil price are closely related </li></ul></ul><ul><ul><li>Oil price indicates inflation </li></ul></ul><ul><li>Real interest rate </li></ul><ul><ul><li>Higher interest rate, lower gold price </li></ul></ul><ul><ul><li>Depositing in bank versus investing in gold </li></ul></ul>Regression Model- Determinants (continued)
    8. 11. Gold price= 115.5702+2.5385CPI-0.0198DOWindex-2.2859exchange rate +4.849Oil price+14.90248real interest rate+95.276Dummy Regression Model- Result
    9. 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
    10. 13. Regression Model- Special event and Dummy variable <ul><li>Stock collapse will happen once about every 10 years, and investors will invest more in gold , hence the gold price will increase in this period . </li></ul><ul><li>Although gold price changes in these stock collapses can be reflected by the fluctuation of some factors such as stock market, oil price and exchange rate etc, investors’ risk inverse affects gold price more significant. </li></ul>A dummy variable should be added when there is a market crash in order to increase the impact on gold price forecasting.
    11. 14. Special event and Dummy variable- continued <ul><li>Huge Fluctuation of gold price from 1979-1983 </li></ul><ul><li>G old price soared during 1979-1980 </li></ul><ul><ul><li>Irrational investment in gold caused by bad expectation to future </li></ul></ul><ul><ul><li>War in 1980 </li></ul></ul><ul><li>G old price fell down sharply in 1982 </li></ul><ul><ul><li>Bubble explosion </li></ul></ul><ul><li>G old price increased during 1982-1983 </li></ul><ul><ul><li>Economy recovery </li></ul></ul><ul><ul><li>Investors’ confidence back </li></ul></ul>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.
    12. 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 .
    13. 17. ARIMA method- Pattern of gold price
    14. 18. ARIMA method- First difference
    15. 19. ARIMA method- Autocorrelation and Partial Autocorrelation
    16. 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
    17. 21. ARIMA method- Graphical Result
    18. 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
    19. 23. <ul><li>Conclusion - Comparison of ARIMA and Regression for gold price forecasting </li></ul><ul><li>ARIMA </li></ul><ul><li>More accurate in short period forecasting </li></ul><ul><li>Only requires historical gold price data </li></ul><ul><li>Regression </li></ul><ul><li>Appropriate for predicting long-run trend </li></ul><ul><li>More difficult in choosing indicators </li></ul>
    20. 24. <ul><li>Q&A </li></ul>