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Use Python and SQL to analyze performance of investment strategies and recommend the optimal strategy for minimizing extreme loss from market downturn.
Under the tangled global impacts from all directions, the turbulence of the current financial market makes it hard to predict and invest. At this moment, studying tail risk (known as the extreme loss from an asset or portfolio and represented at the left tail of the return distribution) and designing tail hedge strategies has become more critical to protect against severe market distress. In this project, you'll wear the hat of Financial Engineer and study tail hedge strategies as potential investment models for controlling significant drawdown risks. Under the supervision of an experienced Build Fellow, you'll construct, implement and measure the performance of tail hedge strategies empirically using more than 15 years of index and option data. You'll also formulate your own opinion on the strategies’ behavior for the future potential market move and provide cost-effective recommendations for clients. You will use Pandas, Numpy and Matplotlib Python libraries to analyze strategies and build your own functions on selecting, exiting and entering option positions.
Get to know the Project Leader and other students, ask questions about the project requirements, prepare your workspace.
Definition of indexes, ETFs and options. Study S&P 500 time series. Perform value at risk analysis on S&P 500.
Breakdown strategy component and introduce concept of backtest and rolling.
Pseudo code for rolling functions.
Pseudo code for unit sizing functions.
Study performance metrics such as returns, volatility, maximum drawdown etc.
Measure the tail hedge effectiveness on the cost of the protection and consistency of outperforms during crisis.
Polish your project deliverables and present them to the Project Leader and other students in the final group session.
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Nan is a Finance Fellow and currently working for Volos as a senior index engineer. Nan previously worked for Intercontinental Exchange, specializing in margin modeling, financial product proxying, and financial data analytics. Nan holds an M.S. in Quantitative and Computational Finance from the Georgia Institute of Technology.