Range-Based Volatility Estimators for Monitoring Market Stress: Evidence from Local Food Price Data

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 shocks, policy and fuel-cost adjustments, and global supply-chain and trade disruptions. Volatility also detects stress that standard momentum indicators such as the relative strength index (RSI) can miss, including symmetric or rapidly reversing shocks in which offsetting supply and demand disturbances dampen net directional price movements while amplifying intra-period dispersion. Overall, OHLC-based volatility indicators provide a robust and interpretable signal of market disruptions and complement price-level monitoring for applications spanning financial risk, humanitarian early warning, and trade.

Figures (10)

• Figure 1: Open, High, Low, Close food prices (index, log) in Al Fashir, Sudan, plotted on a candlestick chart together with six OHLC-based volatility metrics. In the candlestick chart (top panel), each bar represents one month: the vertical line spans the low to high, while the box spans open to close; filled (red) boxes indicate months where the close fell below the open, and hollow (green) boxes indicate months where the close exceeded the open. The dotted line shows the 12-month moving average, and the grey shading indicates Bollinger bands (two standard deviations around the moving average). Subsequent panels show close-to-close volatility and five range-based estimators (Parkinson, Garman--Klass, Rogers--Satchell, Garman--Klass--Yang--Zhang, and Yang--Zhang). All estimators identify the same broad stress periods but differ in smoothness: range-based measures respond more quickly to widening intra-period dispersion, while close-to-close volatility reacts only to month-end price jumps.
• Figure 2: Technical setup to detect volatility shocks. Top: log food prices in Al Fashir, Sudan. Middle: RSI with 30 and 70 reference levels. Bottom: Yang--Zhang annualized volatility, its 12-month moving average, and detected high-volatility episodes (red segments).
• Figure 3: Somalia (Baidoa): technical setup and detected stress episodes. Top panel: log food prices. Middle panel: RSI with 30 and 70 reference levels. Bottom panel: Yang--Zhang annualized volatility, its 12-month moving average, and detected high-volatility episodes (red segments).
• Figure 4: Cameroon (Far North): technical setup and detected stress episodes. Top panel: log food prices. Middle panel: RSI with 30 and 70 reference levels. Bottom panel: Yang--Zhang annualized volatility, its 12-month moving average, and detected high-volatility episodes (red segments).
• Figure 5: Haiti (Port-au-Prince): technical setup and detected stress episodes. Top panel: log food prices. Middle panel: RSI with 30 and 70 reference levels. Bottom panel: Yang--Zhang annualized volatility, its 12-month moving average, and detected high-volatility episodes (red segments).

Theorems & Definitions (6)

• Definition 1: Close-to-close volatility
• Definition 2: Parkinson high-low estimator
• Definition 3: Garman-Klass estimator
• Definition 4: Rogers-Satchell estimator
• Definition 5: Garman-Klass-Yang-Zhang extension
• Definition 6: Yang-Zhang estimator