Austin, Texas, United States
I am a currently a graduate student at the University of Chicago in Financial Mathematics program with expected graduation in Dec 2023. I have always had a strong desire to work in the quantitative finance industry. My college education and professional experience have taught me that rational analysis is more important in finance than gut feelings and intuition. With a double major in Finance and Mathematics from Rutgers University as well as self-study in Computer Science, I find it exhilarating to apply quantitative tools to assess market patterns and model market behavior. I am interested in pursuing a career in Quantitative Finance, particularly in Quantitative Trading, Quantitative Research, Portfolio and Risk Management, and am seeking summer internship opportunities in the same field.
• Performing comprehensive research and in-depth analysis to simulate the implied volatility curve for SPX across various options using a diverse range of models such as hyperbola, sigmoid, spline, and kernel smoothing techniques • Utilizing Principal Component Analysis (PCA) to effectively reduce the dimensionality of the SPX options data • Implementing advanced feature engineering techniques and leveraging machine learning algorithms to analyze the dynamics of the volatility curve, enabling the prediction of implied volatility and option prices
• Produced weekly fund reports individually based on weekly data of the Chinese fund market, especially the ETFs market, by conducting NPV volatility analysis, fund size net change analysis, and cash flow analysis, and estimated the performance and trend of the fund market • Applied the Lasso regression model to return on fund NPV and return on each industry’s NPV and estimated the weekly performance and changes of each industry to support the analysis of the weekly report • Programmed and categorized each fund into a general section based on fund heavy hold stocks’ belonging industries using Python and Pandas • Conducted regression and correlation test with an R-square of 0.18 to determine the relationship between the general market volatility and the long position redemptions of ETFs with different hysteresis periods