University of Illinois Urbana-Champaign

Nonlinear dependence and its application in finance

Xie, Yong

Content Files
XIE-DISSERTATION-2023.pdf

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https://hdl.handle.net/2142/121995

Description

Title
Nonlinear dependence and its application in finance
Author(s)
Xie, Yong
Issue Date
2023-11-20
Director of Research (if dissertation) or Advisor (if thesis)
Linders, Daniel
Doctoral Committee Chair(s)
DeVille, Lee
Committee Member(s)
  • Sowers, Richard
  • Verdickt, Gertjan
Department of Study
Mathematics
Discipline
Mathematics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
  • nonlinear dependence
  • asset pricing
  • implied information
  • risk forecasting
  • implied correlation
  • ncertainty risk
Abstract
We explore the implication of nonlinear dependence in the asset pricing theories and risk analytics. Using the Model-Free Implied Dependence (MFID), a measure that exhibits information on linear and nonlinear dependence within the market, we show that stocks with high exposure to MFID generate significantly higher risk-adjusted returns in bad times, which is consistent with time-varying preferences, implying increased demand for assets that offer a hedge in bad times. This finding is robust against other risk common risk factors including implied correlation. Besides, we propose Implied Correlation Gap (ICG) on the top of model-free implied dependence and implied correlation. We demonstrate that ICG is capable of revealing the inadequacy of implied correlation and proxies the degree of nonlinear dependence in the market. More importantly, we document that ICG exhibits incremental predictive power for market uncertainty risks. Our results imply that such predictability is bridged by the investor disagreement.
Graduation Semester
2023-12
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/121995
Copyright and License Information
Copyright 2023 Yong Xie

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