Herding in Commodities and Cryptocurrencies

When Herding Moves Beyond Equities

Author

Nam Nguyen
February 08, 2026 • Estimated Reading Time: 10 minutes

Herding behavior has been extensively studied and is well understood in equity markets, but far less so in other asset classes such as commodities and cryptocurrencies. In this issue, we explore key aspects of herding behavior in crypto and commodity markets.

In this issue:

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  • Risk, Leverage, and Optimal Betting in Financial Markets (11 min)

  • The Effectiveness of Collar Structures in Equity and Commodity Markets (12 min)

Investor Behavior in Crypto During Geopolitical Shocks

Herd behavior refers to the tendency of investors to follow the actions of a larger group, often ignoring their own analysis or information. This collective movement can lead to asset bubbles during bull markets and sharp sell-offs during downturns. Understanding herd behavior is essential for identifying potential mispricings and avoiding emotionally driven decisions.

Herding behavior has been well studied in the equity markets, but less so in the cryptocurrency market. One might expect stronger herding in crypto due to the prevalence of young, inexperienced traders and the fact that crypto markets are under-regulated, less transparent, and highly volatile. However, existing studies have produced inconclusive results.

Reference [1] extends the research on herding in the crypto space by examining behavior during major geopolitical events, such as the COVID-19 pandemic and the Russia–Ukraine war.

Findings

  • The study finds strong evidence of market-wide herding behavior in cryptocurrency markets by analyzing the relationship between return dispersion and market returns.

  • Geopolitical risk (GPR) significantly amplifies herding, with severe herding detected across nearly all model specifications.

  • The GPR Threat index has a stronger impact on herding than the GPR Act index, indicating that perceived geopolitical threats matter more than realized events.

  • Herding behavior is asymmetric, occurring more intensely during bearish market conditions than bullish ones.

  • Imitative trading is particularly pronounced during periods of market stress, confirming the presence of asymmetric herding.

  • The strongest herding effects are observed during extreme geopolitical and global events, notably the COVID-19 pandemic and the Russia–Ukraine war.

  • The findings suggest that herding in cryptocurrency markets is largely intentional, reflecting low information symmetry, weak disclosure, and limited information quality.

  • Actual geopolitical events (GPR Act) tend to lose explanatory power because market participants rapidly process and price in the information once it is released.

  • When realized geopolitical shocks exceed investor expectations, uncertainty rises sharply and herding intensifies.

In short, the authors found that herding intensifies during such events and is clearly present throughout these periods.

Reference

[1] Phasin Wanidwaranan, Jutamas Wongkantarakorn, Chaiyuth Padungsaksawasdi, Geopolitical risk, herd behavior, and cryptocurrency market, The North American Journal of Economics and Finance Volume 80, September 2025, 102487

Does Herding Behavior Exist in the Commodity Markets?

Herding behavior has been shown to exist in equity markets. Reference [2] examines the herding behavior in the commodity markets.

Findings

  • The study investigates herding behavior in commodity ETFs using high-frequency microstructure data and a GARCH model that incorporates cross-sectional and market volatility at 15-, 30-, 45-, and 60-minute intervals.

  • During periods of market instability and the COVID-19 pandemic, agricultural and metal-based ETFs generally exhibit weaker herding behavior, while energy-based ETFs tend to herd more.

  • Under normal market conditions, herding typically emerges at frequencies longer than 30 minutes.

  • Broad basket commodity ETFs and energy-based ETFs display herding behavior across multiple frequencies rather than at a single time scale.

  • A notable exception is agricultural ETFs during the COVID-19 pandemic, where herding is observed across all frequencies, representing a key and unusual finding.

  • Correlation analysis shows that commodity ETFs become less correlated with each other as time progresses.

  • Lower observation frequencies are associated with weaker correlations across ETFs, except in the energy sector.

  • The results suggest that herding behavior varies significantly by commodity type, market regime, and observation frequency.

The findings provide insights for investors, economists, and policymakers, particularly for designing diversification, hedging strategies and mitigating risks such as asset price bubbles and financial instability.

Reference

 [2] Ah Mand, Abdollah and Sifat, Imtiaz and Ang, Wei Kee and Choo, Jian Jing, Herding Behavior in Commodity Markets. SSRN 4502804

Closing Thoughts

Taken together, these two studies show that herding behavior extends well beyond equity markets and plays a meaningful role in both cryptocurrencies and commodity ETFs, particularly under stress. In crypto markets, herding is strongly amplified by geopolitical risk, bearish conditions, and extreme events. In commodity ETFs, herding is more nuanced and highly dependent on asset class, market regime, and trading frequency, with energy and broad commodity baskets exhibiting persistent herding, while agricultural and metal ETFs remain relatively resilient except during extreme volatility.

Overall, the evidence suggests that herding is regime-dependent, frequency-specific, and asset-class-specific, with important implications for risk management, diversification, and the design of trading and hedging strategies during periods of market stress.

Educational Video

University Lecture:  Herding and Bubbles (Financial Markets Microstructure)

The lecture examines financial bubbles through the lens of herding and informational cascades, framing bubbles as situations where prices deviate persistently from fundamentals because traders rationally ignore their private information. In sequential decision-making settings, agents observe past actions but not the underlying signals that generated them, leading public information to dominate private beliefs. Once public belief becomes sufficiently strong, individual traders optimally follow the crowd, triggering herding and informational cascades that can sustain bubbles even when initial signals are wrong.

The lecture further shows that herding is not inherently irrational but arises from rational inference under information constraints, especially when private signals are bounded in precision. Extensions incorporating price-setting and noise traders reveal that herding can persist even with flexible prices, generating temporary mispricing and speculative bubbles. The key takeaway is that bubbles emerge from failures in information aggregation, where actions convey too little information about underlying beliefs, causing markets to overweight public signals and underutilize dispersed private information.

Volatility Weekly Recap

The figure below shows the term structures for the VIX futures (in colour) and the spot VIX (in grey).

The market began the week with a strong rally, with the S&P 500 briefly reaching a new all-time high. However, weakness emerged on Tuesday, followed by a sharp sell-off on Thursday driven by declines in technology stocks and weaker-than-expected labor market data. A tech-led rebound on Friday provided some relief, but for the week the S&P 500 fell 0.63% and the Nasdaq declined 1.62%.

Gold and silver traded sideways with elevated volatility; silver ended the week down 9%, while gold finished up 5%. Bitcoin dropped below $73,000 on Tuesday, staged a modest rebound, sold off again, and then recovered somewhat on Friday.

On the volatility front, despite a spike in spot volatility, VIX futures remained in contango; however, the roll yield turned negative.

Around the Quantosphere

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  • Before gold and silver crashed, hedge funds were already making a quiet move (businessinsider)

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  • The Race to Bring Quantitative Strategies To Corporate Bond Markets (bloomberg)

  • Top hedge fund strategies allocators love for 2026 (businessinsider)

  • A hedge fund legend expects a rally before a downturn (finance yahoo)

  • I’m a 30-year-old Goldman Sachs trader, I would never leave for a market-making firm (efinancialcareers-canada)

  • How hedge funds are tapping prediction markets and their data for an edge (businessinsider)

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