Wenjun Jiang
About Wenjun Jiang
Wenjun Jiang is the Senior Vice President at Jefferies with extensive experience in quantitative finance and research, having previously worked at Morgan Stanley, Micron Technology, and the University of Washington.
Company
Wenjun Jiang is currently working at Jefferies as a Senior Vice President. He joined the company in 2017 and is based in New York, New York, United States. Jefferies is a global investment banking firm providing clients with capital markets and financial advisory services.
Title
Wenjun Jiang holds the position of Senior Vice President at Jefferies, where his role includes analyzing and optimizing financial portfolios and developing mathematical models for various financial instruments.
Previous Roles
Before joining Jefferies, Wenjun Jiang worked at Morgan Stanley as a Quantitative Associate from 2013 to 2016 in New York. He also interned at Micron Technology in Boise, Idaho, as a Research and Development Intern in 2010. His early career included research positions at the University of Washington as a Graduate Research Assistant and at the University of Science and Technology of China as an Undergraduate Research Assistant.
Education and Expertise
Wenjun Jiang studied at the University of Washington, earning a PhD in Physics with a focus on Nano Technology, a Master of Engineering in Electrical and Electronics Engineering, and a Graduate Certificate in Computational Finance. He also holds a Bachelor of Science in Condensed Matter and Materials Physics from the University of Science and Technology of China. His technical expertise includes portfolio-level pre-trade scenario analysis, post-trade return attribution, hedging effectiveness analysis, and developing dynamic indices using Python.
Research and Development
During his tenure at Morgan Stanley, Wenjun Jiang worked on exotic options such as basket knock-in/knock-out using local volatility models and European cliquets using stochastic local implied volatility models in C++ and R. He has also been involved in developing dynamic indices that optimize component weighting distribution using Python.