News consumers in many authoritarian countries read government-controlled sources even when independent sources are available. We test whether these choices reflect a preference for pro-government coverage versus persistent tastes for specific websites. We exploit textual data from news...

We introduce a robust, flexible and easy-to-implement method for estimating the yield curve from Treasury securities. This method is non-parametric and optimally learns basis functions in reproducing Hilbert spaces with an economically motivated smoothness reward. We provide a closed-form...

High-frequency data can provide us with a quantity of information for forecasting, help to calculate and prevent the future risk based on extremes. This tail behaviour is very often driven by exogenous components and may be modelled conditional on other variables. However, many of these...

High-frequency financial data allow us to estimate large volatility matrices with relatively short time horizon. Many novel statistical methods have been introduced to address large volatility matrix estimation problems from a high-dimensional Ito process with microstructural noise...

This paper constructs an estimator for the number of common factors in a setting where both the sampling frequency and the number of variables increase. Empirically, we document that the covariance matrix of a large portfolio of US equities is well represented by a low rank common structure with...

We develop a novel machine learning method to estimate large dimensional time-varying GMM models via our newly designed ridge fusion regularization scheme. Our method is a one-step procedure and allows for abrupt, smooth and dual type time variation with a fast rate of convergence. It...

Several novel statistical methods have been developed to estimate large integrated volatility matrices based on high-frequency financial data. To investigate their asymptotic behaviors, they require a sub-Gaussian or finite high-order moment assumption for observed log-returns, which cannot...

This paper introduces a unified parametric modeling approach for time-varying market betas that can accommodate continuous-time diffusion and discrete-time series models based on a continuous-time series regression model to better capture the dynamic evolution of market betas.We call this the...

Various parametric models have been developed to predict large volatility matrices, based on the approximate factor model structure. They mainly focus on the dynamics of the factor volatility with some finite high-order moment assumptions. However, the empirical studies have shown that the...

High-frequency data can provide us with a quantity of informa- tion for forecasting, help to calculate and prevent the future risk based on extremes. This tail behaviour is very often driven by ex- ogenous components and may be modelled conditional on other vari- ables. However, many of these...