The expected value of sums of squared intraday returns (realized variance) gives rise to a least squares regression which adapts itself to the assumptions of the noise process and allows for joint inference on integrated variance (<inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="ubes_a_637876_o_ilm0001.gif"/>), noise moments, and price-noise relations. In the iid noise...

The expected value of sums of squared intraday returns (realized variance) gives rise to a least squares regression which adapts itself to the assumptions of the noise process and allows for joint inference on integrated variance (<inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="ubes_a_637876_o_ilm0001.gif"/>), noise moments, and price-noise relations. In the iid noise...

We propose a unified framework for estimating integrated variances and covariances based on simple OLS regressions, allowing for a general market microstructure noise specification. We show that our estimators can outperform, in terms of the root mean squared error criterion, the most recent and...

We develop a panel intensity framework for the analysis of complex trading activity datasets containing detailed information on individual trading actions in different securities for a set of investors. A feature of the model is the presence of a time varying latent factor, which captures the...

We develop a panel intensity framework for the analysis of complex trading activity datasets containing detailed information on individual trading actions in different securities for a set of investors. A feature of the model is the presence of a time-varying latent factor, which captures the...

We analyze the effects of nonsynchronicity and market microstructure noise on realized covariance type estimators. Hayashi and Yoshida (2005) propose a simple estimator that resolves the problem of nonsynchronicity and is unbiased and consistent for the integrated covariance in the absence of...

In this paper we introduce a new method of forecasting covariance matrices of large dimensions by exploiting the theoretical and empirical potential of using mixed-frequency sampled data. The idea is to use high-frequency (intraday) data to model and forecast daily realized volatilities combined...