Author
Listed:
- Michael Bloss
(International School of Finance (ISF), Nuertingen-Geislingen University, Sigmaringer Straße 25, 72622 Nuertingen, Germany)
- Dietmar Ernst
(International School of Finance (ISF), Nuertingen-Geislingen University, Sigmaringer Straße 25, 72622 Nuertingen, Germany)
- Leander Geisinger
(International School of Finance (ISF), Nuertingen-Geislingen University, Sigmaringer Straße 25, 72622 Nuertingen, Germany)
Abstract
We analyze extreme gold price movements between 1975 and 2025 using Extreme Value Theory (EVT). Using both the Block-Maxima and Peaks-over-Threshold approaches on a daily return basis, we estimate Value-at-Risk (VaR) and Expected Shortfall (ES) for the entire distribution focusing on a long-term view. Our results demonstrate that models based on the standard normal distribution systematically underestimate extreme risks, whereas EVT provides more reliable measures. In particular, EVT captures not only rare losses, but also sudden positive rallies, highlighting gold’s dual function as a risk and opportunity asset. Asymmetries emerge in the analysis: at the 0.99 quantile, losses appear larger in absolute value than gains. At the 0.995 quantile, in some episodes, upside extremes dominate. Furthermore, we find that geopolitical and economic shocks, including the oil crises, the 2008 financial crisis, and the COVD-19 pandemic, leave distinct signatures in the extremes. By covering five decades, our study provides the most extensive EVT-based assessment of gold risks to date. Our findings contribute to debates on financial stability and provide practical guidance for investors seeking to manage tail risks while recognizing gold’s potential as both a safe haven and a speculative asset.
Suggested Citation
Michael Bloss & Dietmar Ernst & Leander Geisinger, 2025.
"Extreme Value Theory and Gold Price Extremes, 1975–2025: Long-Term Evidence on Value-at-Risk and Expected Shortfall,"
Commodities, MDPI, vol. 4(4), pages 1-35, October.
Handle:
RePEc:gam:jcommo:v:4:y:2025:i:4:p:24-:d:1772353
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