Nonlinear dependence and its application in finance

Xie, Yong

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

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

Copyright and License Information

Copyright 2023 Yong Xie

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