arXiv 3 Mar 2026 · Finance — Statistical Finance
arXiv:2603.02898 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Andrée, Bo Pieter Johannes (2021) Estimating Food Price Inflation from Partial Surveys | 0.928 | 4 | 3 | 100% |
| 2 | Baillie, Richard T. and Bollerslev, Tim and Mikkelsen, Hans Ole (1996) Fractionally integrated generalized autoregressive conditional heteroskedasticity | 0.928 | 4 | 3 | 100% |
| 3 | Yang, Dennis and Zhang, Qiang (2000) Drift-independent volatility estimation based on high, low, open, and close prices | 0.874 | 6 | 2 | 100% |
| 4 | Andrée, Bo Pieter Johannes and Pape, Utz Johann (2023) Machine Learning Imputation of High Frequency Price Surveys in Papua New Guinea | 0.843 | 3 | 3 | 100% |
| 5 | Appel, Gerald (1979) The Moving Average Convergence-divergence Trading Method | 0.843 | 3 | 3 | 100% |
| 6 | Wilder, J. Welles (1978) New Concepts in Technical Trading Systems | 0.843 | 3 | 3 | 100% |
| 7 | Garman, Mark B. and Klass, Michael J (1980) On the estimation of security price volatilities from historical data | 0.737 | 3 | 2 | 100% |
| 8 | Adrian, Tobias and Fleming, Michael J. and Shachar, Or and Vogt, Erik (2017) Market liquidity after the financial crisis | 0.644 | 2 | 2 | 100% |
| 9 | Corwin, Shane A. and Schultz, Paul (2012) A simple way to estimate bid-ask spreads from daily high and low prices | 0.644 | 2 | 2 | 100% |
| 10 | Food and Agriculture Organization of the United Nations (2009) The Food Price Crisis of 2007/2008: Evidence and Implications for Food Security | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 86 scored citations.