Agricultural commodity prices exhibit the same patterns that quantitative finance models were designed to handle: trends, volatility clustering, seasonal effects, and sudden shocks. This article explains how I borrowed techniques from quantitative finance — inspired by the awesome-quant project — and applied them to agricultural price forecasting in KilimoPRO.
EWMA Volatility (RiskMetrics)
Exponentially Weighted Moving Average (EWMA) volatility estimation assigns more weight to recent observations. Using the RiskMetrics standard lambda of 0.94, the variance is updated as: σ²ₜ = λ × σ²ₜ₋₁ + (1-λ) × r²ₜ. This gives more weight to recent price movements, which is critical for agricultural markets where volatility can spike suddenly due to droughts, pest outbreaks, or policy changes.
GARCH(1,1)
Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models volatility clustering — the tendency for periods of high volatility to be followed by more high volatility. The GARCH(1,1) model is: σ² = ω + α × r² + β × σ². For agricultural prices, I estimated parameters using method of moments with typical commodity values: α=0.1, β=0.85, ω derived from the long-run variance.
Value at Risk (VaR)
Value at Risk answers the question: "What's the most I could lose with 95% confidence?" For a farmer considering whether to store their produce or sell now, VaR provides a quantified risk assessment. VaR₉₅ = price × σ × 1.645. If the 95% VaR is KES 500 per bag, it means there's a 5% chance prices will drop by more than KES 500 in the forecast period.
RSI (Relative Strength Index)
RSI measures the speed and change of price movements on a scale of 0-100. RSI > 70 indicates "overbought" (prices may fall — sell signal). RSI < 30 indicates "oversold" (prices may rise — hold signal). This is directly applicable to agricultural markets: when maize prices spike above historical norms (RSI > 70), it's a good time to sell.
SMA Crossover
Simple Moving Average crossover compares short-term (5-period) and long-term (20-period) moving averages. When the short-term crosses above the long-term, it's a bullish signal (sell to capture the trend). When it crosses below, it's bearish (store and wait).
Putting It All Together
The KilimoPRO quant forecasting module combines all these metrics into a single recommendation. For example: "Prices are trending upward. RSI indicates prices are high (72) — this may be a good time to sell. Short-term moving average is above long-term — bullish signal. Sell now to maximize returns. ⚠️ High price volatility (18.5%). Consider selling in batches. Risk assessment: 95% chance prices won't drop more than 12% in the next 2 weeks."