In empirical work on multivariate financial time series, it is common to postulate a Multivariate GARCH model. We show that the popular Gaussian quasi-maximum likelihood estimator of MGARCH models is very sensitive to outliers in the data. We propose to use robust M-estimators and provide...

An estimator of the ex-post covariation of log-prices under asynchronicity and microstructure noise is proposed. It uses the Cholesky factorization of the covariance matrix in order to exploit the heterogeneity in trading intensities to estimate the different parameters sequentially with as many...

The increase in trading frequency of Exchanged Traded Funds (ETFs) presents a positive externality for financial risk management when the price of the ETF is available at a higher frequency than the price of the component stocks. The positive spillover consists in improving the accuracy of...

In this supplementary appendix to the paper Lu et al.(2021) ``Estimation of factors using higher-order multi-cumulants in weak factor models", we first present an overview of several alternative factor estimation and selection approaches. Second, we interpret the alternating least squares...

We propose a jump robust positive semidefinite rank-based estimator for the daily covariance matrix based on high-frequency intraday returns. It disentangles covariance estimation into variance and correlation components. This allows to estimate correlations over lower sampling frequencies, to...

The Gaussian rank correlation equals the usual correlation coefficient computed from the normal scores of the data. Although its influence function is unbounded, it still has attractive robustness properties. In particular, its breakdown point is above 12%. Moreover, the estimator is consistent...

Full paper is available at: "https://ssrn.com/abstract=3087336" https://ssrn.com/abstract=3087336.In this supplementary appendix to the paper Boudt, Cornilly and Verdonck (2019), we first provide a brief R tutorial for the proposed NC estimator. Then, we go into more detail about the shape of...

We estimate the latent factors in high-dimensional panel non-Gaussian data using Higher-order multi-cumulant Factor Analysis (HFA). HFA consists of an eigenvalue ratio test to select the number of non-Gaussian factors and uses alternating regressions to estimate both Gaussian and non-Gaussian...

In this supplementary appendix, we first provide a brief R and Python tutorial for the proposed BAC estimator. Then, we describe the implementation of the BAC estimator in case of microstructure noise and jumps. We further present more detailed empirical results for the BAC estimation applied to...