Paper: arXiv 2603.02898

Authors: Bo Pieter Johannes Andrée

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.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper novelly applies classic financial range-based volatility estimators (Parkinson, Garman-Klass, Yang-Zhang) to synthetic OHLC food price series derived from a fiGARCH-GED model. It provides strong empirical backing across multiple international conflict and crisis case studies, showing that range volatility captures market stress far better than traditional momentum indicators like RSI.

Research Flowchart

  flowchart TD
    A[Research Goal: Adapt OHLC Range-Based Volatility Estimators to Monitor Local Food Market Stress] --> B[Data Inputs: World Bank Real-Time Monthly Food Prices & Documented Disruption Timelines]
    
    B --> C[Compute Volatility Estimators: Parkinson, Garman-Klass, Rogers-Satchell & Yang-Zhang]
    
    C --> D[Evaluate & Benchmark Performance Against Traditional Indicators e.g., RSI]
    
    D --> E[Key Finding 1: Volatility Spikes Align with Insecurity, Climate Shocks, Policy Shifts & Trade Disruptions]
    
    D --> F[Key Finding 2: Detects Symmetric/Reversing Shocks That Momentum Indicators Miss]
    
    E & F --> G[Outcome: OHLC Indicators Provide Robust Early Warning Signals for Humanitarian & Trade Risk]

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0 of 89 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.