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Data Assistant

New York, New York, United States
November 14, 2018

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Master of Science in Financial Engineering Expected Dec 2019

Anticipated Coursework: Continuous-Time Asset Pricing, Discrete-Time Asset Pricing, Monte Carlo Simulation, Stochastic Calculus, Optimization, Machine Learning, Time Series Analysis NANJING UNIVERSITY, Nanjing, China

Bachelor of Economics in Financial Engineering, GPA: 3.8/4.0 Sep 2014 – Jun 2018

Coursework: Black-Scholes & Greeks, CAPM, PCA, Linear Regression, Interest rate models, Machine Learning, Data Mining Using Python, NLP, Probability, Statistics, Numerical Analysis, ODE, Time Series Analysis UNIVERSITY OF CALIFORNIA, BERKELEY, Berkeley, CA

Exchange Program: Major in Mathematics and Statistics, GPA:4.0/4.0 Aug 2016 – Dec 2016 EXPERIENCE

China Times Asset Co., Ltd. Shanghai, China Feb 2018 – May 2018 Quantitative Trading Assistant

Fit ARMA and GARCH models on SSE Composite Index for prediction; increased the estimation accuracy 28% by implementing non-linear GARCH models; explored economic mechanisms for volatility clustering

Estimated portfolio risk with Value at Risk using historical bootstrap and Monte Carlo method in Python; drafted portfolio investment strategies based on different expected return and risk-aversion level and presented to managers SWS Research Co., Ltd. Shanghai, China Sep 2017 – Nov 2017 Quantitative Research Intern

Analyzed the influence of shorting cost on SSE ETF 50 using Excel VBA; adjusted Black-Scholes model to equalize implied volatility of calls and puts by adding a shorting cost parameter

Forecasted long-term and short-term volatility of S&P 500, SSE ETF 50 and HSI using implied volatility and GARCH models on realized volatility; investigated pros and cons of two models on different time spans

Developed a rating model to classify stocks into value and growth styles; used Machine Learning techniques to construct SWS style index; connected index database with Wind API for automatic real-time update PROJECTS


Static Hedging for American Options Using European Options Jan 2018 – May 2018

Built a static hedging model to replicate and price American options with standard European options

Solved the boundary-free problem of pricing American options by adding value-matching and smooth-pasting conditions; obtained numerical solutions by employing Newton-Raphson method using MATLAB

Compared the performance of static hedging with the benchmark computed from Monte Carlo simulation; Static hedging reduced 47% of hedging error compared to daily delta hedging on S&P 500 index options Statistical Arbitrage for Stock Index Future and Bond Future Jan 2017 – Apr 2017

Computed correlation and analyzed co-movement mechanism between stock index futures and bond futures

Conducted cointegration test of two futures in python and interpreted the results

Employed Bollinger bands and developed pairs trading strategy to build portfolio; portfolio achieved Sharpe ratio of 1.59 in 16-month back-testing with excess return of 26% UNIVERSITY OF CALIFORNIA, BERKELEY, Berkeley, CA

Data Mining & Machine Learning: 2016 Presidential Election Debrief Nov 2016 – Dec 2016

Extracted, cleaned and integrated 4 years’ election data, GML and census data by web scraping, exploratory data analysis and data manipulation in R; used GitHub for version control and team collaboration

Predicted 2016 presidential election results using K-NN, Decision Tree and Naïve Bayes SKILLS

Programming & Other Software: Python, C++, R, SQL, Excel VBA, XPath, MATLAB, LaTeX, Bloomberg

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