Debiased Kernel Estimation of Spot Volatility in the Presence of Infinite Variation Jumps

B. Cooper Boniece, Jos\'e E. Figueroa-L\'opez and Tianwei Zhou

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Abstract: Volatility estimation is a central problem in financial econometrics, but becomes particularly challenging when jump activity is high, a phenomenon observed empirically in highly traded financial securities. In this paper, we revisit the problem of spot volatility estimation for an It\^o semimartingale with jumps of unbounded variation. We construct truncated kernel-based estimators and debiased variants that extend the efficiency frontier for spot volatility estimation in terms of the jump activity index $Y$, raising the previous bound $Y

Date: 2025-10
New Economics Papers: this item is included in nep-ecm
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