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 $Y<4/3$ to $Y<20/11$, thereby covering nearly the entire admissible range $Y<2$. Compared with earlier work, our approach attains smaller asymptotic variances through the use of unbounded kernels, is simpler to implement, and has broader applicability under more flexible model assumptions. A comprehensive simulation study confirms that our procedures substantially outperform competing methods in finite samples.

翻译：波动率估计是金融计量经济学中的一个核心问题，但当跳跃活动性较高时，该问题变得尤为棘手，这在交易活跃的金融证券中已被经验观察所证实。本文重新审视了具有无界变差跳跃的伊藤半鞅的现货波动率估计问题。我们构建了截断核估计量及其去偏变体，这些估计量在跳跃活动指数$Y$方面扩展了现货波动率估计的效率前沿，将先前的界$Y<4/3$提升至$Y<20/11$，从而几乎覆盖了整个允许范围$Y<2$。与早期工作相比，我们的方法通过使用无界核获得了更小的渐近方差，实现更简单，并且在更灵活的模型假设下具有更广泛的适用性。一项全面的模拟研究证实，我们的方法在有限样本中显著优于竞争方法。