Market Intelligence Cell (MIC)
Cherry Market Stability Technical Report (Draft)
Market Intelligence Cell (MIC) • Draft analytical pack for internal review & web publication.
This technical note presents a multidimensional stability assessment of cherry prices across major market–grade segments.
The MIC Stability Index (0–100) integrates six risk dimensions—price variability (CV), short-term volatility (4-week), medium-term volatility (8-week), downside risk (maximum drawdown), shock frequency, and directional consistency—to distinguish stable benchmark segments from structurally volatile ones.
Empirically, Ganderbal (Medium, Small, Large) emerges as the most stable cluster with stability scores around ~80 and full 52-week coverage. Stability in Ganderbal is driven by low structural variability and contained drawdown exposure despite moderate directional inconsistency. In contrast, Narwal Jammu (F&V) | Small exhibits the highest structural risk, combining elevated CV (~0.50) with the largest drawdown (~1.6), indicating substantial downside exposure. Agl ar grades show instability primarily driven by drawdown risk, while Azadpur segments demonstrate moderate structural risk but elevated shock frequency.
Key takeaway: Instability is dimension-specific—some markets are drawdown-driven, others shock-driven—requiring grade-level and driver-level advisories rather than blanket market classifications.
The Stability Summary Table confirms Ganderbal (all three grades) as the highest stability cluster (~80). These segments maintain low variability and limited downside risk while sustaining competitive mean prices (e.g., Large ~₹143/kg).
Narwal Jammu (F&V) | Large and Parimpore | Large also fall in the Stable class (~67), but their dominant instability drivers differ—Narwal Large is primarily downside-risk driven, whereas Parimpore Large is affected by directional inconsistency.
Azadpur | Large (~61) remains stable but its instability is shock-driven rather than structurally volatile. Moderately Stable segments (e.g., Narwal Jammu (F&V) | Medium, Azadpur | Medium, Parimpore | Small) show elevated drawdown or shock exposure.
Insight: Stability varies materially across grades within the same market—grade-level monitoring is essential.
The heatmap reveals the internal structure of stability. Ganderbal grades show strong performance across CV, short-term and medium-term volatility, and shock frequency dimensions, explaining their high overall scores.
However, directional consistency appears comparatively weaker in otherwise stable segments—indicating alternating weekly price movement despite controlled volatility.
Narwal Jammu (F&V) | Small displays weakness across variability and drawdown dimensions but maintains high directional consistency, suggesting persistent trend-driven movement rather than oscillatory noise. Agl ar grades exhibit broader multi-dimensional weakness, especially in drawdown and shock exposure.
Interpretation: Most volatile segments fail due to one or two dominant weak dimensions rather than uniform instability across all metrics.
The quadrant map separates segments by mean price (₹/kg) and short-term volatility (4-week).
Premium-Stable Quadrant (High Price + Low Volatility): Ganderbal | Large and Parimpore | Large combine strong price realization with contained volatility.
High-Reward High-Risk Quadrant (High Price + High Volatility): Narwal Jammu (F&V) | Medium shows attractive price levels but elevated short-term volatility.
Low-Price High-Volatility Quadrant: Agl ar | Small and Narwal Jammu (F&V) | Small combine weaker price levels with higher volatility, increasing uncertainty.
Insight: High price does not automatically imply high risk—some premium segments are structurally stable.
The structural map shows deeper instability by combining coefficient of variation (CV) with maximum drawdown.
Narwal Jammu (F&V) | Small stands out as the most structurally exposed segment (highest CV and highest drawdown), indicating significant downside vulnerability.
Ganderbal | Small exhibits the lowest structural exposure (low CV and low drawdown), reinforcing its role as the most stable reference segment.
Agl ar grades display elevated drawdown combined with moderate variability, suggesting risk driven by sustained declines rather than isolated shocks. Azadpur segments exhibit moderate structural risk but relatively higher shock frequency.
Interpretation: Structural instability is concentrated in specific segments rather than evenly distributed across markets.
The stacked decomposition highlights the dominant contributors to instability.
Narwal Jammu (F&V) | Small shows the largest combined contribution of variability risk and drawdown risk, explaining its lower composite stability.
Agl ar | Small and Agl ar | Medium are primarily drawdown-driven, indicating sustained downside exposure. In contrast, Ganderbal grades show minimal combined contributions across both components.
Operational Insight: Variability-driven instability supports timing-based advisories, while drawdown-driven instability supports risk-hedging and cautious procurement strategies.
The radar comparison illustrates multidimensional contrast between the most stable and most volatile segments.
Top-performing segments (notably Ganderbal grades) display broad and balanced polygons, indicating strong performance across variability, volatility, and shock dimensions.
Bottom-performing segments show sharp contractions along CV and drawdown axes, confirming concentrated structural weaknesses. Directional consistency varies asymmetrically—some unstable segments maintain strong trend persistence despite poor volatility control.
Conclusion: Stability superiority is multidimensional; it arises from consistent strength across several risk metrics rather than dominance in a single dimension.
Scores are computed from the available weekly market records and will update as new seasonal data is incorporated.
Stability should be interpreted alongside arrivals, quality variation, trading windows, and supply-side disruptions.
Transparency: The Stability Index is a diagnostic intelligence tool designed for market advisory purposes—not a price prediction model.