Wheat.
CBOT wheat futures — critical for global food security
$
CBOT Wheat (cents per bushel)
52-Week Range
481.25 — 731.25
598 trading days
Ann. Volatility
25.6%
MLE λ=0.962 (N=597)
VaR (95%)
2.36%
Cornish-Fisher
Regime
Mixed
P(turb.) = 32%
Price History.
Daily closing prices with Bollinger Band overlay
Regime Detection.
Hamilton (1989) two-state Markov switching model
Calm State
σ = 18.2% ann.
Avg. duration: 2 days
Turbulent State
σ = 28.3% ann.
Avg. duration: 2 days
Regime Timeline
Transition Probabilities
P(calm → calm) = 52.4%
P(calm → turb.) = 47.6%
P(turb. → turb.) = 50.0%
P(turb. → calm) = 50.0%
Model Diagnostics
Converged: Yes
Observations: 597
Log-likelihood: 1637.6
Vol ratio (turb/calm): 1.6x
Returns Distribution.
Log return histogram with normal overlay
Mean
0.0211%
Std Dev
1.5832%
Skewness
0.441
Excess Kurtosis
0.530
Jarque-Bera
26.36
JB p-value
0.0000
Normal?
No
Observations
597
Why this matters
Positive skewness indicates supply-shock driven right-tail events — prices spike up more often than they crash. The return distribution is approximately normal, suggesting standard risk metrics are reasonably accurate.
Volatility.
EWMA volatility (λ = 0.962) — annualized
Seasonality.
Monthly return patterns across available history
| Year | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2024 | — | — | — | +13.6% | +11.6% | -17.3% | -6.4% | +3.0% | +3.3% | — | -8.5% | +7.4% |
| 2025 | -0.8% | +11.3% | -2.8% | -4.9% | +0.7% | -1.2% | -0.1% | -1.2% | -4.2% | -9.5% | +0.6% | -2.5% |
| 2026 | +9.4% | +6.6% | +9.5% | +9.1% | -6.3% | -3.8% | +6.6% | — | — | — | — | — |
Average Monthly Return
Seasonal Context
Northern Hemisphere winter wheat planting (October–November) and harvest (June–July) drive the primary cycle. Spring wheat adds a secondary peak (August–September). Black Sea export corridors create a geopolitical overlay independent of agricultural seasonality.
Risk Metrics.
Value at Risk, Expected Shortfall, and drawdown analysis
Interpretation: On 95% of trading days, the loss is expected to be smaller than 2.36%. On the worst 5% of days, the average loss (CVaR) is estimated at 3.70%. The 99% VaR captures more extreme tail events at 3.23%.
Estimated from 597 daily returns. Tail risk estimates improve with longer history.
Maximum Drawdown: 34.19%
Related Markets.
Return correlations with economically linked assets
ZW vs ZS: 165 overlapping return observations
ZW vs KC: 163 overlapping return observations
Note: Return correlations are unstable over time and do not imply causation. These pairs are shown because they share economic drivers (e.g., agricultural supply chains, energy complex), not because correlation alone is meaningful. Short-sample correlations (N < 250) should be treated as rough estimates.
Trend Analysis.
Hurst exponent and Bollinger Band bandwidth
Hurst Exponent
Estimated via R/S analysis from 597 return observations
Bollinger Bandwidth
Bandwidth is within normal range. No strong squeeze or expansion signal detected.
About Wheat.
Fundamentals, catalysts, and Ethiopian trade relevance
Ethiopian Trade Relevance
Ethiopia is a net wheat importer, purchasing ~2 million tonnes annually. Wheat prices directly impact food security costs, government subsidy budgets, and inflation. The Black Sea premium (Ukraine/Russia supply risk) has made CBOT wheat a geopolitical barometer since 2022.
Supply & Demand Fundamentals
The most widely grown cereal globally. Russia, EU, and US are top exporters. Black Sea supply (Russia + Ukraine ~30% of global exports) creates geopolitical price sensitivity. Quality spreads (protein content, milling grade) drive basis differentials. Ethiopian imports source primarily from Black Sea and Australia.
Key Reports & Catalysts
Quote Convention
CBOT Wheat (cents per bushel)
Unit
¢/bu
Trading Days/Year
252