Quantitative Investing with Tail Behavior—A Distributional Approach

Lingjie Ma
Additional contact information
Lingjie Ma: University of Illinois at Chicago

Chapter Chapter 8 in Quantitative Investing, 2020, pp 339-403 from Springer

Abstract: Abstract In previous chapters, we introduced classical mean–variance methodologies. Classical methodologies have been used widely in risk management, such as the use of standard deviation for risk; alpha models, such as the use of OLS for weighting schemes; and modern portfolio theory, such as the mean–variance optimization. However, these are all based on the assumption that the first two moments will capture most information about asset returns, which is usually not true of real-world finance data, where fat and long tails are often the case. In this chapter, we present a distributional approach to capture tail behaviors for quantitative investing. Quantile regression (QR), a frontier methodology that extends beyond the median into tail percentiles, provides a useful tool for incorporating tail information into portfolios. We explore how QR can be employed for risk management, alpha modeling, and portfolio construction. The last section introduces R codes and packages for QR.

Date: 2020
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-47202-3_8

Ordering information: This item can be ordered from
http://www.springer.com/9783030472023

DOI: 10.1007/978-3-030-47202-3_8

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().