Range-Based Volatility Estimators for Monitoring Market Stress: Evidence from Local Food Price Data
Bo Pieter Johannes Andr\'ee

TL;DR
This paper demonstrates how range-based volatility estimators applied to local food prices can effectively monitor market stress and disruptions across diverse development and conflict-affected settings.
Contribution
It adapts open-high-low-close volatility estimators for localized market monitoring, providing a robust tool for detecting market stress in various challenging contexts.
Findings
Elevated volatility correlates with insecurity, weather shocks, and trade disruptions.
OHLC volatility measures detect stress missed by traditional momentum indicators.
Volatility signals align with documented market disruption timelines.
Abstract
Range-based volatility estimators are widely used in financial econometrics to quantify risk and market stress, yet their application to local commodity markets remains limited. This paper shows how open-high--low-close (OHLC) volatility estimators can be adapted to monitor localized market distress across diverse development contexts, including conflict-affected settings, climate-exposed regions, remote and thinly traded markets, and import- and logistics-constrained urban hubs. Using monthly food price data from the World Bank's Real-Time Prices dataset, several volatility measures -- including the Parkinson, Garman-Klass, Rogers-Satchell, and Yang-Zhang estimators -- are constructed and evaluated against independently documented disruption timelines. Across settings, elevated volatility aligns with episodes linked to insecurity and market fragmentation, extreme weather and disaster…
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Taxonomy
TopicsMarket Dynamics and Volatility · Agricultural risk and resilience · Complex Systems and Time Series Analysis
