Yu-Sin Chang, Ph.D., FRM

Assistant Professor

  • Milwaukee WI UNITED STATES
  • Mathematics

Dr. Yu-Sin Chang's research interests include probability, stochastics, and financial mathematics.

Contact

Education, Licensure and Certification

Ph.D.

Applied Mathematics

Illinois Institute of Technology

M.S.

Financial Engineering

University of Michigan-Ann Arbor

M.S.

Money and Banking

National Chengchi University

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Biography

Dr. Yu-Sin Chang joined the Mathematics Department as an Assistant Professor in Fall 2018. She earned her Ph.D. in Applied Mathematics from Illinois Institute of Technology. Her research focuses on the areas of Probability, Stochastics, and Financial Mathematics. Dr. Chang worked in financial industry as a Quantitative Researcher for two and half years and has the Financial Risk Manager (FRM) designation. Dr. Chang received the Society for Industrial and Applied Mathematics Early Career Award in 2019. She contributes to the Actuarial Science program and general mathematical courses.

Areas of Expertise

Financial Mathematics
Stochastic Processes and Stochastic Analysis
Applied Mathematics

Accomplishments

Society for Industrial and Applied Mathematics

2018-12-12

Early Career Travel Award

Affiliations

  • Association for Women in Mathematics (AWM) : Member
  • Society for Industrial and Applied Mathematics (SIAM) : Member

Social

Event and Speaking Appearances

Markovian Consistency of Multivariate Markov Processes

Joint Mathematics Meetings  Denver, CO

2020-01-16

Systemic Risk and the Dependence Structures

SIAM Conference on Financial Mathematics and Engineering  Toronto, Ontario Canada

2019-06-16

Third Eastern Conference on Mathematical Finance

Illinois Institute of Technology, Chicago, IL, October, 2018  

Research Grants

Summer Faculty Development Grant

Milwaukee School of Engineering $3000

2020-05-01

One long-term objective of this project is to study the stochastic dependence between the coordinates of a multivariate Markov process. The interconnection or dependence structure between objects within a stochastic dynamical system plays a critical role in modeling various phenomena from a wide range of applied fields. Various applications are related to, among others, the linked financial institutions within a financial system, sociology, criminology and terrorism, social networks, ruin problems in insurance, and credit risk. For finite dimensional multivariate random variables the dependence structure between their components is fully characterized in terms of the so-called copulae and the Sklar's Theorem. On the other hand, the analysis or the characterization of the dependence structure between the components of a multivariate stochastic process is a relatively new area. In part, this project contributes to this area.

Selected Publications

Systemic Risk and the Dependence Structures

arXiv Preprint

Chang, Y.S.

2018

We propose a dynamic model of dependence structure between financial institutions within a financial system and we construct measures for dependence and financial instability. Employing Markov structures of joint credit migrations, our model allows for contagious simultaneous jumps in credit ratings and provides flexibility in modeling dependence structures. Another
key aspect is that the proposed measures consider the interdependence and reflect the changing economic landscape as financial institutions evolve over time. In the final part, we give several examples, where we study various dependence structures and investigate their systemic instability measures. In particular, we show that subject to the same pool of Markov chains, the simulated Markov structures with distinct dependence structures generate different sequences of systemic instability

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