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We study a class of nonparametric volatility estimators based on the Laplace transform, which are robust to the presence of the endogeneity of observation times. Asymptotic properties and feasible central limit theorems are established. In the presence of time endogeneity, our bias-corrected Laplace estimator takes advantage of the informational content of time endogeneity, which leads to narrower confidence bounds. The finite sample properties of the estimator are studied through Monte Carlo simulations. Through the simulation study, we also find that due to the presence of the kernel, Laplace estimator could be adopted in a model with microstructure noise. The performance of the Laplace estimator is compared with other commonly used estimators through forecasting exercises by employing high frequency data. We conclude that the bias-corrected Laplace estimator performs better than most estimators in terms of forecasting equity return volatility in the presence of both time endogeneity and market microstructure noise. [ABSTRACT FROM AUTHOR]

Employing tick-by-tick maximum likelihood estimation on several leading models from the financial economics literature, we find that the market microstructure noise is mostly explained by a linear model where the trade direction, that is, whether the trade is buyer or seller initiated, is multiplied by the dynamic quoted bid-ask spread. Although reasonably stable intraday, this model manifests variability across days and stocks. Among different observable high frequency financial characteristics of the underlying stocks, this variability is best explained by the tick-to-spread ratio, implying that discreteness is the first residual source of noise. We determine the bid-ask bounce effect as the next source of noise. [ABSTRACT FROM AUTHOR]

It is well known that in finance variances and covariances of asset returns move together over time. Recently, much interest has been aroused by an approach involving the use of the realized covariance (RCOV) matrix constructed from high-frequency returns as the ex-post realization of the covariance matrix of low-frequency returns. For the analysis of dynamics of RCOV matrices, we propose the generalized conditional autoregressive Wishart (GCAW) model. Both the noncentrality matrix and scale matrix of the Wishart distribution are driven by the lagged values of RCOV matrices, and represent two different sources of dynamics, respectively. The GCAW is a generalization of the existing models, and accounts for symmetry and positive definiteness of RCOV matrices without imposing any parametric restriction. Some important properties such as conditional moments, unconditional moments, and stationarity are discussed. Empirical examples including sequences of daily RCOV matrices from the New York Stock Exchange illustrate that our model outperforms the existing models in terms of model fitting and forecasting. [ABSTRACT FROM AUTHOR]

In this article, we propose a nonparametric procedure to estimate the integrated volatility of an Itô semimartingale in the presence of jumps and microstructure noise. The estimator is based on a combination of the preaveraging method and threshold technique, which serves to remove microstructure noise and jumps, respectively. The estimator is shown to work for both finite and infinite activity jumps. Furthermore, asymptotic properties of the proposed estimator, such as consistency and a central limit theorem, are established. Simulations results are given to evaluate the performance of the proposed method in comparison with other alternative methods. [ABSTRACT FROM AUTHOR]

This article contributes to the theory for preaveraging estimators of the daily quadratic variation of asset prices and provides novel empirical evidence. We develop asymptotic theory for preaveraging estimators in the case of autocorrelated microstructure noise and propose an explicit test for serial dependence. Moreover, we extend the theory on preaveraging estimators for processes involving jumps. We discuss several jump-robust measures and derive feasible central limit theorems for the general quadratic variation. Using transaction data of different stocks traded at the New York Stock Exchange, we analyze the estimators’ sensitivity to the choice of the preaveraging bandwidth. Moreover, we investigate the dependence of preaveraging-based inference on the sampling scheme, the sampling frequency, microstructure noise properties, and the occurrence of jumps. As a result of a thorough empirical study, we provide guidance for optimal implementation of preaveraging estimators and discuss potential pitfalls in practice. [ABSTRACT FROM PUBLISHER]