Incorporating Global Industrial Classification Standard Into Portfolio Allocation: A Simple Factor-Based Large Covariance Matrix Estimator With High-Frequency Data-Reference-Cited by-同舟云学术

Incorporating Global Industrial Classification Standard Into Portfolio Allocation: A Simple Factor-Based Large Covariance Matrix Estimator With High-Frequency Data

Published:2016-09-15 Issue:4 Volume:34 Page:489-503
ISSN:0735-0015
Container-title:Journal of Business & Economic Statistics
language:en
Short-container-title:Journal of Business & Economic Statistics

Author:

Fan Jianqing¹,Furger Alex²,Xiu Dacheng³

Affiliation:

1. Department of Operations Research & Financial Engineering and BendheimCenter for FinancePrinceton University, Princeton, NJ 08544, and School of Data Science, Fudan University, Shanghai, China

2. Department of Operations Research & Financial Engineering Princeton University, 26 Prospect Avenue, Princeton, NJ 08544

3. Booth School of Business University of Chicago, 5807 S. Woodlawn Avenue, Chicago, IL 60637

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Economics and Econometrics,Social Sciences (miscellaneous),Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/07350015.2015.1052458

Reference45 articles.

1. High-Frequency Covariance Estimates With Noisy and Asynchronous Financial Data

2. Aït-Sahalia, Y., Kalnina, I., and Xiu, D. (2014), “The Idiosyncratic Volatility Puzzle: A Reassessment at High Frequency,” Discussion Paper, The University of Chicago.

3. The Distribution of Realized Exchange Rate Volatility

4. Inferential Theory for Factor Models of Large Dimensions

5. Determining the Number of Factors in Approximate Factor Models

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