The mathematical model of gold prices... And beyond!



The so complex and elusive mathematical modeling of gold prices historically escapes in the short and long term the bases of supply and demand they do not allow themselves to be modeled overtime only by factors such as real inflation, the interest rate, the strength (or weakness) of the dollar registered in the DXY index or by the uncertainty levels of the VIX index.

Mostly risk aversion or rejection measured as call and put options is one of the factors that monitor gold prices in the short term. In these times of IoT, big data and digitization, will it be possible to increase knowledge and more accurately model gold prices? Our team of professionals developed a stochastic model that correlates with the real data, adjusts with an R2 of 0.89, follows trends in Gold prices and is robust to the statistical criteria of independence, heteroscedasticity, and non-collinearity of the residuals. . The following graph shows the results of the model vs. the actual monthly data of Gold prices.


Graph1: Modeling of Gold prices generated from St. Louis Fed 2003-2019 macroeconomic data. It is statistically robust and all of its forecasts are within its 90% confidence levels.

The results of the model continue to be updated and monitored with macroeconomic data and levels of global uncertainty. News shocks impact short-term estimates, and it is interesting to see that the model quickly maintains the results within its confidence levels (90%).

What would be the applications of this econometric model?

Initially a good benchmarking for Gold mining companies- such as Newmont Goldcorp, Barrick Randgold, Anglogold, Agnico, Kinross, Buenaventura- in the estimated gold price for the formulation of their quarterly forecast and annual budget plans. For short term investments with call/put options and iron condor strategies, it would be a good proxy. For potential stop loss and take profit levels in investments with shares of gold mining companies on the stock exchange.

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