Description
Full Time (1 Year, Present)
Supervised by: Profs. Victoria Ivashina (Lovett-Learned Professor of Business Administration; Unit Head, Finance) and Josh Lerner (Jacob H. Schiff Professor Of Investment Banking)
Portfolio Optimization Assumptions of Pension Funds Considering Cost of Illiquidity of Alternate Assets
- Developed simulations and models in Python to estimate the modeling assumptions of the average pension fund in the US about the expected return and covariance of alternative assets like Real Estate and Private Equity, as compared to Public Equity and Fixed Income.
- Studied the Mean Variance Optimization framework and formulated a simulaiton study to estimate the fixed cost of illiquidity for Real Estate and Private Equity on the expected returns.
- Surveyed volatility de-smoothing literature and created a simulation incorporating unsmoothed volatility to counter covariance suppression due to infrequent appraisal based pricing instead of market pricing.
- Created a simulation study for incorporating fixed costs of illiquidity on expected returns as investor views into the Black Litterman model.
- Documented progress in weekly presentations, discussions, as well as formal manuscripts
- Designed a Python Package for abstracting portfolio calculations and as a framework for portfolio optimization simulations.
Killer Acquisitions in the Finance Industry to Curb Innovative Competition: Evidence from Mergers, Acquisitions, Alliances, and Patent Throughput
- Constructed a pipeline in Excel and Python to build a big dataset of all financial firms in the US along with their financial and business characteristics
- Developed econometric regressions using synthetic control methods for causal inference in Python to support hypothesized arguments
Knowledge
- Research
- Portfolio Theory
- Econometrics
- Data Science
- Simulation Modeling
- Computational Finance
Skills
- Python
- Excel
- $\LaTeX$