We propose a composite realized kernel to estimate the ex-post covariation of asset prices.
These measures can in turn be used to forecast the covariation of future asset returns …

The concept of volatility refers to any phenomenon presenting features of instability,
unpredictability, and a likeliness to change frequently, often without apparent or cogent …

We introduce a unifying class of nonparametric spot volatility estimators based on delta
sequences and conceived to include many of the existing estimators in the field as special …

Our aim is to give an overview of the topic of estimation of volatility, in a high-frequency
setting. We emphasize the various possible situations, relative to the underlying process …

The recent availability of high frequency data has permitted more efficient ways of computing
volatility. However, estimation of volatility from asset price observations is challenging …

We study the asymptotic normality of two feasible estimators of the integrated volatility of
volatility based on the Fourier methodology, which does not require the pre-estimation of the …

We will focus on estimating the integrated covariance of two diffusion processes observed in
a nonsynchronous manner. The observation data is contaminated by some noise, which …

This paper develops a new statistical inference theory for the precision matrix of high-
frequency data in a high-dimensional setting. The focus is not only on point estimation but …

We propose a composite realized kernel to estimate the ex-post covariation of asset prices.
Composite realized kernels are a data efficient method where the covariance estimate is …

We study the efficiency and robustness of the Fourier spot volatility estimator by Malliavin
and Mancino [2002] when high-frequency prices are contaminated by microstructure noise …