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

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  • B. Cooper Boniece
  • Jos'e E. Figueroa-L'opez
  • Tianwei Zhou

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

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  • B. Cooper Boniece & Jos'e E. Figueroa-L'opez & Tianwei Zhou, 2025. "Debiased Kernel Estimation of Spot Volatility in the Presence of Infinite Variation Jumps," Papers 2510.14285, arXiv.org.
    • Handle: RePEc:arx:papers:2510.14285
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    File URL: http://arxiv.org/pdf/2510.14285
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