Jeffrey Varner holds a Bachelor of Science degree (Chemistry), a Masters and a Ph.D. degree in Chemical Engineering, from Purdue University. Prof. Varner’s graduate thesis work at Purdue was done under the direction of Prof. D. Ramkrishna in the area of modeling and analysis of metabolic networks. Following Purdue, Prof. Varner was a postdoctoral researcher in the Department of Biology at the ETH-Zurich where he studied signal transduction mechanisms involved in cell-death under Prof. Jay Bailey. After the ETH, Prof. Varner was a Scientist in the Oncology business unit of Genencor International Inc, Palo Alto, CA. While at Genencor, Prof. Varner was involved in the discovery of novel targets in human cancers, and was a project team member for preclinical, phase-I and II studies of protein therapeutics for the treatment of colorectal cancer and Chronic Lymphocytic Leukemia (CLL). Prof. Varner left Genencor at the end of 2005 to join the faculty of the Chemical and Biomolecular Engineering department at Cornell University. At Cornell, the Varner lab is developing physiochemical modeling tools to rationally reprogram human signal transduction architectures.
Equity Asset Pricing Using Stochastic ModelsCornell Course
Course Overview
When people think about investing, they typically consider stocks and the stock market. Unlike Treasury securities, investing in equities like stocks involves purchasing shares in a publicly traded company on an exchange, which comes with significant risks due to share price fluctuations. Predicting exact future share prices is likely an unsolvable problem, but using the power of modeling, you can predict a range of possible future equity share price values.
In this course, you will discover how to use tools in the Julia programming language to simulate and analyze equity share price distributions. You will explore different approaches, from approximating future prices using discrete lattice models to using continuous stochastic modeling to simulate the prices over time for individual stocks and groups of stocks. By the end of the course, you will be able to predict future share price distributions, understand the statistical properties of these distributions, and evaluate the various methods for modeling share prices.
You are required to have completed the following course or have equivalent experience before taking this course:
- Quantitative Modeling of Fixed Income Debt Securities
Key Course Takeaways
- Develop a binomial net present value equity trade rule
- Examine geometric Brownian motion models and stylized facts
- Explore advanced topics in single-asset geometric Brownian motion
- Compute allocations for a portfolio of equities
How It Works
Course Author
Who Should Enroll
- Quantitative analysts
- Finance professionals looking to upskill in data modeling
- Engineers looking to transition into finance
- Research scientists
- Computer scientists
- Personal investors
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