This paper develops an inferential theory for state-varying factor models of large dimensions. Unlike constant factor models, loadings are general functions of some recurrent state process. We develop an estimator for the latent factors and state-varying loadings under a large cross-section and...

This paper develops a novel method to estimate a latent factor model for a large target panel with missing observations by optimally using the information from auxiliary panel data sets. We refer to our estimator as target-PCA. Transfer learning from auxiliary panel data allows us to deal with a...

The Internet Appendix collects the proofs and additional results that support the main text. The additional theoretical results include a detailed description of special cases and related models and an extension to noisy and misspecified state processes. We also provide an estimator for the...

Statistical arbitrage identifies and exploits temporal price differences between similar assets. We propose a unifying conceptual framework for statistical arbitrage and develop a novel deep learning solution, which finds commonality and time-series patterns from large panels in a data-driven...

In this paper we analyze performance-based remuneration for risk-averse managers in a Black Scholes-type model. We assume that the firm's performance is influenced by an industry and a firm-specific risk. A relative performance compensation which rewards a manager relative to the exogenous...

This paper analyzes which stock option scheme best aligns the interests of a firm's manager and shareholders when both are risk-averse. We consider granting to the manager a basic fixed salary and one of the following four options: European, Parisian, Asian and American options. Choosing the...