Forecasting the worst: is implied volatility forward-looking enough?

Carlo Confalonieri & Paola De Vincentiis

Journal of Banking Regulation2026https://doi.org/10.1057/s41261-025-00306-warticle
AJG 2ABDC C
Weight
0.50

What the paper says

This paper backtests the accuracy of 1-day Value-at-Risk (VaR), computed using the variance–covariance approach, employing various alternative methodologies for estimating the risk factor’s volatility. The aim is to determine whether using option-implied volatility (IV) produces superior results compared to other time-series-based measures. Specifically, we want to test if a forward-looking estimate, which reflects the sentiment of market investors, is more capable of capturing the tail behaviour of returns distribution during crisis periods characterised by volatility spikes. The empirical analysis is performed on the S&P500 Stock Index, using both the VIX Index and the newly introduced VIX1D Index by the Chicago Board of Trade. Our results indicate that VIX-based VaR tends to be underestimated during market turbulences, both in terms of the frequency of realised losses exceeding the threshold and the average magnitude of the excess losses. However, a different picture emerges when we use the new VIX1D Index instead of the traditional VIX. Preliminary evidence indicates that the 1-day implied volatility derived from zero-day-to-expiration (0DTE) options may be more effective for estimating VaR and superior to alternative methodologies.

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https://doi.org/https://doi.org/10.1057/s41261-025-00306-w

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@article{carlo2026,
  title        = {{Forecasting the worst: is implied volatility forward-looking enough?}},
  author       = {Carlo Confalonieri & Paola De Vincentiis},
  journal      = {Journal of Banking Regulation},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1057/s41261-025-00306-w},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.