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.
Course Overview
Portfolio allocation is a continuous challenge: Investors must find the right balance between seeking higher returns and managing increased risk. How can you use quantitative modeling to help optimize your portfolio?
In this course, you will delve into data-driven and model-based approaches to portfolio allocation using the Julia programming language. You will discover various methods to estimate the required components of the allocation problem from data or by using simple models. You will then determine how to evaluate portfolio performance and examine the role of diversification in portfolio performance. Finally, you will explore utility maximization, risk aversion, and behavioral finance to help you better understand portfolio allocation choices. By the end of the course, you will be able to develop optimized portfolios that consist of combinations of both more risky assets and risk-free ones to balance risk and reward.
You are required to have completed the following courses or have equivalent experience before taking this course:
- Quantitative Modeling of Fixed Income Debt Securities
- Equity Asset Pricing Using Stochastic Models
- Analysis of Equity Derivatives at Expiration
- Analysis of Equity Derivatives Before Expiration
Key Course Takeaways
- Construct low- and high-correlation portfolios of risky assets
- Compute the optimum risk-reward trade-off surface for a collection of risky and risk-free assets
- Evaluate the performance of single index models
- Use single index models and minimum variance portfolio allocation
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
100% Online
cornell's Top Minds
career