We propose uniformly valid inference on volatility with noisy high-frequency data. We assume the observed transaction price follows a continuous-time Itô-semimartingale, contaminated by a discrete-time moving-average noise process associated with the arrival of trades. We estimate the quadratic...

We present a new type of unsupervised learning problem in which we find a small set of representative regions that approximates a larger dataset. These regions may be presented to a practitioner along with additional information in order to help the practitioner explore the data set. An...

We apply machine-learning techniques to predict drug approvals using drug-development and clinical-trial data from 2003 to 2015 involving several thousand drug-indication pairs with over 140 features across 15 disease groups. To deal with missing data, we use imputation methods that allow us to...

Internet recommender systems are popular in contexts that include heterogeneous consumers and numerous products. In such contexts, product features that adequately describe all the products are often not readily available. Content-based systems therefore rely on user-generated content such as...

This article surveys the state of the art in the econometrics of regression models with many instruments or many regressors based on alternative – namely, dimension – asymptotics. We list critical results of dimension asymptotics that lead to better approximations of properties of familiar...

We present a way to compute additive marginal contributions for the six capital measures forming the capital computation under the new Basel III market risk regime, commonly named “Fundamental Review of the Trading Book regime” (FRTB). Marginal contributions are most useful for the...

We consider a nonparametric time series regression model. Our framework allows precise estimation of betas without the usual assumption of betas being piecewise constant. This property makes our framework particularly suitable to study individual stocks. We provide an inference framework for all...

Recently several large volatility matrix estimation procedures have been developed for factor-based Ito processes whose integrated volatility matrix consists of low-rank and sparse matrices. Their performance depends on the accuracy of input volatility matrix estimators. When estimating...

Several novel large volatility matrix estimation methods have been developed based on the high-frequency financial data. They often employ the approximate factor model that leads to a low-rank plus sparse structure for the integrated volatility matrix and facilitates estimation of large...

The relationships between contingent convertible (CoCo) bonds and their underlying equities, credit default swap spreads (CDS), interest rates, implied volatilities and foreign exchange rates are studied. Starting with the dynamic correlation of the DCC-GARCH method, it is found that CoCo bonds...