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利用短期期权估计日内波动率模式AI 解读

We propose a nonparametric estimator for the deterministic periodic component of volatility from short-dated options within an in-fill asymptotic setting. The estimator uses options with zero and one day to expiration sampled at high-frequency during a trading day. At each point in time, we aggregate the options to form nonparametric estimates of conditional risk-neutral expectations of future integrated return variation for the two available option tenors. A suitable ratio…

基于期权的即时波动率估计之偏差缩减AI 解读

We consider the problem of nonparametric spot volatility estimation from options that is robust to time-variation in volatility and presence of jumps in the underlying asset price. Using a higher-order expansion of the characteristic function of the underlying price increment over shrinking time intervals and option-based estimates of the latter over two distinct horizons, we achieve asymptotic bias-reduction in spot volatility estimation, relative to existing methods, that…

利用短期期权测量与预测波动率的信息增益AI 解读

Summary We study the gains from using short‐dated options for volatility measurement and forecasting. Using option portfolios, we estimate nonparametrically spot volatility under weak assumptions for the underlying asset. This volatility estimator complements existing ones constructed from high‐frequency returns. We show empirically, using the market index and Dow 30 stocks, that combining optimally return and option data can lead to nontrivial gains for volatility…