Repository logo
 

Asymmetric Systematic Risk and Risk Premiums Under a Regime-Switching Model

dc.contributor.authorZhang, Ziwei
dc.contributor.copyright-releaseNot Applicableen_US
dc.contributor.degreeMaster of Scienceen_US
dc.contributor.departmentRowe School of Businessen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.external-examinerN/Aen_US
dc.contributor.graduate-coordinatorHamed Aghakhanien_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.thesis-readerIraj Fooladien_US
dc.contributor.thesis-readerLeonard MacLeanen_US
dc.contributor.thesis-supervisorYonggan Zhaoen_US
dc.date.accessioned2022-04-14T14:43:41Z
dc.date.available2022-04-14T14:43:41Z
dc.date.defence2022-04-11
dc.date.issued2022-04-14T14:43:41Z
dc.description.abstractThe purpose of this thesis is to identify asymmetry in stocks’ systematic risk and market risk premiums under different financial market regimes. It is assumed that there are two unobserved regimes, bull and bear markets, in the U.S. stock market, which follow a hidden Markov process. A sample of 597 firms that are traded on multiple U.S. stock exchanges from January 1986 to October 2021 is used to test the hypotheses that systematic risk and market risk premiums are asymmetric in different market regimes. It is found that there is a strong asymmetry in the stocks’ systematic risk under both the extended CAPM and the Fama and French three-factor model setting. To test asymmetric market risk premiums, the cross-sectional regression is used and finds evidence that supports asymmetric market risk premium in both the extended CAPM and the Fama and French three-factor model.en_US
dc.identifier.urihttp://hdl.handle.net/10222/81566
dc.subjectAsymmetric Systematic Risken_US
dc.subjectAsymmetric Risk Premiumsen_US
dc.subjectRegime-Switching Modelen_US
dc.subjectFinancial risk management.
dc.titleAsymmetric Systematic Risk and Risk Premiums Under a Regime-Switching Modelen_US
dc.typeThesisen_US
dc.typeTexten_US

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
ZiweiZhang2022.pdf
Size:
3.12 MB
Format:
Adobe Portable Document Format
Description:

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: