Selected publications
W-shaped implied volatility and investor learning about event risk
Sole author · Under review at Quantitative Finance, 2026
Implied-volatility curves can occasionally exhibit a W-shape — a local maximum near at-the-money rather than the standard U-shaped smile. This paper provides a structural explanation grounded in Bayesian learning about hidden regimes. The main theorem gives a necessary condition on beliefs for W-shapes to emerge, and intensity peaks at maximum uncertainty. Empirical tests on U.S. equity options for 247 S&P 500 firms (2017–2025) confirm the predictions: W-shapes appear roughly six times more often when options span an earnings announcement, scale with earnings-reaction strength, and notably do not appear around FOMC announcements.
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Cross-firm implied volatility spillovers at earnings announcements
Sole author · Under review at Journal of Financial Markets, 2025
When a firm announces earnings, the implied volatility of industry-peer firms moves with the announcer’s implied volatility. Using U.S. equity option data (2010–2024) and GICS peer groupings, spillovers are statistically significant even with date fixed effects. Controlling for industry-date averages, the spillover operates at the industry level rather than through firm-specific signals. Two tests suggest the spillover carries information about future uncertainty: it persists into the peer’s own subsequent earnings announcement — particularly when the peer reports shortly after — and it does not dissipate across the earnings season.
PDF · SSRN
Does divestment move risk? A null result from the world’s largest sovereign wealth fund
Sole author · Working paper, 2026
Theory predicts that institutional divestment raises firm-level volatility. This paper tests the prediction using 181 exclusions by Norway’s Government Pension Fund Global, the world’s largest sovereign wealth fund. GARCH(1,1) variance ratio tests show no effect at announcement or during the preceding investigation. Product-based exclusions (tobacco, weapons, coal) carry no underlying scandal, isolating the divestment channel; conduct-based exclusions bundle divestment with an ESG controversy, confounding the two effects. Panel difference-in-differences estimates are small and insignificant, and equivalence tests tightly bound the true effect around zero. Combined with the cost-of-capital null of Berk and van Binsbergen (2025), these findings indicate that neither theoretical channel — cost of capital or volatility — is empirically active.
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Crisis-origin dependence in sector volatility spillovers: Evidence from U.S. equity markets, 2015–2025
With Muhammad Yahya · Under review at International Journal of Finance & Economics, 2026
This paper examines whether the origin of a financial crisis determines how volatility shocks transmit through equity sectors. We apply a quantile vector autoregression (QVAR) framework to daily changes in option-implied volatility for nine U.S. sector ETFs from 2015 to 2025. The sample covers six crises with fundamentally different origins: the 2015–2016 oil/China shock, the 2018 Volmageddon tightening, the COVID-19 pandemic, the 2022–2023 high-inflation monetary tightening, the 2023 SVB banking crisis, and the 2025 tariff crisis. Panel regressions reject equal transmission patterns across all nine quantiles, and all six crises yield unique top-3 transmitter compositions. Monetary tightening elevates industrials and cyclical sectors; COVID-19 features the distinctive emergence of energy-sector transmission; the tariff crisis produces the unprecedented emergence of financials as the leading transmitter; and the oil/China shock features consumer discretionary and technology leadership—with no sector appearing consistently in the top-3 across all crises. We also document a consistent cyclical-defensive divide, where cyclical sectors transmit—and defensive sectors absorb—volatility. The divide holds across all regimes, with utilities qualifying as the only pure safe haven (a net receiver in every regime). Our findings demonstrate that crisis-origin shapes the volatility transmission network, with implications for scenario-based stress testing, portfolio construction, and crisis-specific risk management.
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