Nonlinear dependence and its application in finance

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Mathematics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2023

Author dc:creator

• Xie, Yong

Contributors dc:contributor

• Linders, Daniel
• DeVille, Lee
• Sowers, Richard
• Verdickt, Gertjan

• Nonlinear Dependence
• Asset Pricing
• Implied Information
• Risk Forecasting
• Implied Correlation
• Ncertainty Risk

Statement dc:rights

• Copyright 2023 Yong Xie

Language dc:language

en, eng

Handle dc:identifier

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

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F121995