Spot Silver (XAGUSD) [Interim] 20-day Forecast

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The previous forecast was almost on the dot: See https://ngtiankhean.blogspot.com/2025/12/spot-silver-updated-forecast-till-31.html 

The current forecast is for each day from 05 Jan to 30 Jan 2026. I consider this forecast "Interim" because there are many exogenous factors during this period that may come into play which we cannot model and will make this forecast irrelevant. Among them: impact of China's export control, Bloomberg will also rejig the  weighting for Silver in its Commodity Index due to Silver's outperformance, COMEX declares force majeure and pays in cash if it cannot make physical delivery, the bullion banks may exit all Silver positions even if they incur losses.

The Model
For those who want to know: I use GARCH, ARIMA, 1000-trials Monte Carlo Simulation and Quantile Projections. The data is fitted to a Student-t Distribution to better accomodate tail risks. The Distribution is diagnosed for Fit with Aike Information Criteria (AIC). Also, ARIMA Residuals are injected randomly into the Monte Carlo while it is running to simulate shocks to the model. 

The above Heatmap summarizes our model's forecast probabilities. "Hit any day"probability means the probability of hitting x dollars on any of the 20-days forecasted . The Close above on Day 20 probability means the probability of the price being x dollars on the last day of the forecast. 

This chart above presents the Heatmap data from a different perspective, represented as bars and showing the difference in probabilities between Hit Any Day and Close Above on Day20.

Tail Risks 
Due to the exogenous factors  mentioned above, there will be increased volatility during the forecast period. The increased volatility may take Silver down below $70.00. But because the Demand-Supply fundamentals  remain unchanged, the rebound will be faster than the downturn. To model this asymmetric behaviour I may switch to EGARCH from GARCH. EGARCH says bad news leads to larger price decrease than good news leads to price increase-due to the frantic covering of leveraged short positions. The model's equation includes a term that explicitly accounts for the sign and magnitude of past standardized shocks  The gamma asymmetry parameter is the key to capturing the different effects of downturns and upturns. 

Here we take a look at the asymmetric tail risks. In box and whisker chart below, you can see that Up volatility at 1.25 % is less than Down volatility at 1.70%. The Return Distribution shows that the extremities are more pronounced on on the left tail. So, for the moment our forecast remains valid. 


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