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日内波动率曲线形状变化的在线检测AI 解读

We devise an on-line detector for temporal instability in the shape of average intraday volatility curves under a general semimartingale setup for the price-volatility dynamics. We adopt a block-based strategy to estimate volatility nonparametrically from the intraday observations over local time windows with asymptotically shrinking size. Our detector then tracks sequential changes in running means of the intraday volatility curve estimates. Asymptotic size and power…

含高维协变量的惩罚稀疏协方差回归AI 解读

Covariance regression offers an effective way to model the large covariance matrix with the auxiliary similarity matrices. In this work, we propose a sparse covariance regression (SCR) approach to handle the potentially high-dimensional predictors (i.e., similarity matrices). Specifically, we use the penalization method to identify the informative predictors and estimate their associated coefficients simultaneously. We first investigate the Lasso estimator and subsequently…

日内波动率曲线均值平稳性的检验AI 解读

We develop a test for mean stationarity of latent volatility curves using high‐frequency data. To derive the asymptotic test size and power, we establish a functional invariance principle for semimartingales under a strong mixing condition. The power properties are analyzed under alternatives featuring deterministic trends in the volatility curve dynamics. Application to S&P 500 futures data provides strong evidence of nonstationary variation in the volatility pattern, with…

日内周期性波动率曲线AI 解读

The volatility of financial asset returns displays pronounced variation over the trading day. Our goal is nonparametric inference for the average intraday volatility pattern, viewed as a function of time-of-day. The functional inference is based on a long span of high-frequency return data. Our setup allows for general forms of volatility dynamics, including time-variation in the intraday pattern. The estimation is based on forming local volatility estimates from the…

含噪声与跳跃高频数据下波动率的波动估计及检验AI 解读

We establish a feasible central limit theorem with convergence rate n1/8 for the estimation of the integrated volatility of volatility (VoV) based on noisy high-frequency data with jumps. This is the first inference theory ever built for VoV estimation under such a general setup. The central limit theorem is applied to provide interval estimates of the VoV and conduct hypothesis tests. Furthermore, when one is interested in the null hypothesis that the VoV is zero, we show…

舍入误差与随机市场微观结构噪声并存时的统一波动率估计AI 解读

Widely used volatility estimation methods mainly consider one of the following two simple microstructure noise models: random additive noise on log prices, or pure rounding errors. Apparently in real data these two types of noise co-exist. In this paper, we discover a common feature of these two types of noise and propose a unified volatility estimation approach in the presence of both rounding and random noise. Our data-driven method enjoys superior properties in terms of…

基于 OU-Gamma 时间变换的随机波动率模型：理论与估计AI 解读

We consider stochastic volatility models that are defined by an Ornstein–Uhlenbeck (OU)-Gamma time change. These models are most suitable for modeling financial time series and follow the general framework of the popular non-Gaussian OU models of Barndorff-Nielsen and Shephard. One current problem of these otherwise attractive nontrivial models is, in general, the unavailability of a tractable likelihood-based statistical analysis for the returns of financial assets, which…