Potash

The tool applies a statistical model to analyze weekly fertilizer price returns—measured as week-to-week percentage changes—using market prices closest to maturity. The first graph highlights periods of abnormal or excessive price volatility, defined as fluctuations that exceed a pre-determined threshold. These periods are marked by red vertical lines. The second graph provides a reference trend by displaying the price trajectory of Urea prices for comparison.

Periods of excessive price variability. This occurs when we observe a large number of extreme positive returns in the 270 weeks (5 years) preceding the week in question. An extreme positive return is defined as a return that exceeds a certain pre-established threshold. This threshold is normally taken to be a high order (95% or above) conditional quantile, (i.e. a value of return that is exceeded with low probability: 5% or less).

Weeks that are within periods of (high, moderate or low) price variability. This reflects the number of weeks in the current level of variability based on the number of extreme weekly price increases seen over the last three months. 

The model estimates the conditional mean and variance of returns as functions of the lagged return1. To identify excessive variability, the system calculates a threshold using the 95%-conditional Value-at-Risk (CVaR), estimated via a two-step approach based on a rolling window of prior observations. Periods of excessive price variability are identified when the actual price return statistically exceeds this estimated CVaR threshold.

• RED or excessive volatility: If the probability value is less than or equal to 2.5%, the null that violations (i.e. weeks of extreme price returns) are consistent with expected violations is highly questionable, meaning that we are in a period of an excessive number of weeks of extreme price returns relative to that expected by the model. Therefore, we characterize that date as belonging to a period of excessive volatility.
• ORANGE or moderate volatility: If the probability value is bigger than 2.5% or less than or equal to 5%, the null that violations are consistent with expectations is questionable at a low level, meaning that we are in a period of moderate number of weeks of extreme price returns relative to that expected. Therefore, we characterize that date as belonging to a period of moderate volatility.
• GREEN or low volatility: If the probability value is bigger than 5%, we accept the null that violations are consistent with expectations, meaning that the number of extreme price returns is consistent to what is expected from the model. Therefore, we characterize that date as belonging to a period of low volatility. 

The price volatility model used for commodities with daily data (like agricultural and energy prices) is often based on the Martins-Filho et al. two-step nonparametric method for estimating high conditional quantiles. This approach requires large, clean datasets and is described as computationally heavier. In contrast, the price volatility model used for weekly fertilizer prices adapts this methodology for smaller, limited datasets. This Yao (2025) Hill-based approach uses a simpler, faster Hill estimator for tail estimation, making it easier and quicker to implement. It was found to be more robust and effective for the weekly fertilizer data, demonstrating better out-of-sample performance.