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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...
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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...
Persistent link: https://www.econbiz.de/10014256300
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...
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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...
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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...
Persistent link: https://www.econbiz.de/10013086767
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...
Persistent link: https://www.econbiz.de/10013091782