Efficient asymptotic variance reduction when estimating volatility in high frequency data

Efficient Estimation of Volatility using High Frequency Data

The limitations of volatilities computed with daily data as well as simple statistical considerations strongly suggest to use intraday data in order to obtain accurate volatility estimates. Under a continuous time arbitrage-free setup, the quadratic variations of the prices would allow us, in principle, to construct an approximately error free estimate of volatility by using data at the highest...

Asymptotic algorithm for computing the sample variance of interval data

The problem of the sample variance computation for epistemic inter-val-valued data is, in general, NP-hard. Therefore, known efficient algorithms for computing variance require strong restrictions on admissible intervals like the no-subset property or heavy limitations on the number of possible intersections between intervals. A new asymptotic algorithm for computing the upper bound of the samp...

Forecasting Volatility using High Frequency Data

Assessment of Uncertainty in High Frequency Data: The Observed Asymptotic Variance∗

High frequency inference has generated a wave of research interest among econometricians and practitioners, as indicated from the increasing number of estimators based on intra-day data. However, we also witness a scarcity of methodology to assess the uncertainty – standard error– of the estimator. The root of the problem is that whether with or without the presence of microstructure, standard ...

Spot volatility estimation for high-frequency data

The availability of high-frequency intraday data allows us to accurately estimate stock volatility. This paper employs a bivariate diffusion to model the price and volatility of an asset and investigates kernel type estimators of spot volatility based on highfrequency return data. We establish both pointwise and global asymptotic distributions for the estimators. Jianqing Fan is Frederick Moore...