Hedge Fund Risk Management

JULIA XU

******@***.*** 347-***-**** www.linkedin.com/in/julia-xu2425

EDUCATION

NEW YORK UNIVERSITY, TANDON SCHOOL OF ENGINEERING, Brooklyn, NY Expected 05/25 Master of Science in Financial Engineering, GPA: 3.7/4.0, Entrance Scholarship $3k UNIVERSITY OF LIVERPOOL, Liverpool, UK 06/23

Bachelor of Science in Mathematics with Finance, GPA: 3.9/4.0, First Class, Academic Excellence Awards (Top 5%) TECHNICAL SKILLS & COURSEWORK

• Skills: Python R MATLAB C++ Java SQL LaTeX Excel VBA Bloomberg FINRA SIE Certified

• Courses: Probability and Statistics, Stochastic Calculus,Quantitative Methods in Finance, Time Series Econometrics, Interest Rates Derivative and Risk Management, Machine Learning, Data Structure, Financial Computing, Hedge Fund Strategies INTERESTS

I am passionate about quant research and risk management, with experience in portfolio analysis, risk reporting, VaR back-testing, Monte Carlo simulations, Greeks.

WORK EXPERIENCE

NEW YORK UNIVERSITY 05/24 – 08/24

Hedge Fund Research Assistant, Capstone Project Supervised by Dr. James Conklin NY

• Developed a quantitative framework to evaluate hedge fund managers' timing and sizing skills. Leveraged gain/loss-based performance measures and maximum entropy methods, which improved the reliability of small-sample performance evaluations

• Explored and implemented a Python-based analysis platform (pandas, statsmodels & scipy). Integrating hedge fund return data from Pivotal Path to construct beta-neutral returns, resulting in a more accurate measure of true manager skill

• Applied statistical methods such as hit rate and edge ratio to quantify timing and sizing skills of hedge fund managers

• Analyzed skill persistence across economic environments by segmenting time-series data into five-year window to assess stability TRADEUPTOWALLSTREET 11/23 - 03/24

Quantitative Analyst Intern NY

• Constructed a missing data imputation framework by selecting interpolation methods based on data characteristics, which improved data accuracy and analysis reliability

• Automated portfolio analysis workflows. Computing risk metrics (volatility, drawdown, Sharpe ratio) with rolling window estimation and visualizing results using Python matplotlib, leading to more efficient and accurate portfolio assessments SHENWAN HONGYUAN SECURITIES 06/23 - 08/23

Risk Analyst Intern China

• Enhanced risk reporting through data automation. Pulled position data via API, processed it using Python (pandas), and calculated key risk metrics (PnL, VaR). Aggregated exposure and Greeks (Delta, Gamma, Vega) while monitoring portfolio concentration

• Performed attribution analysis, breaking down PnL and VaR by book and asset class to identify top contributors and detractors. Explained excess return from benchmarks. Built user-friendly visualizations and delivered polished slides

• Supported stress testing and scenario analysis. Employed MSCI RiskMetrics to estimate portfolio PnL impact under hypothetical shocks (e.g.: SPX 10%, VIX 10pts) and historical scenarios (e.g.: Covid-19, 2023 Bank Crisis) RESEARCH / ACADEMIC PROJECTS

NEW YORK UNIVERSITY, TANDON SCHOOL OF ENGINEERING

Yield Curve Modeling & Bond Risk Analysis Using PCA & VaR 11/24 - 12/24

• Built a bond pricing framework. Handled missing yield data. Discounted future cash flows to compute bond prices.

• Applied Principal Component Analysis (PCA) to decompose yield curve movements into level, slope, and curvature factors. Simulated future yield curves based on historical factor dynamics.

• Estimated Value-at-Risk (VaR) using a scenario-driven approach. Simulated future bond prices under PCA-based yield shifts, constructed the PnL distribution, and extracted the 95% percentile loss as the risk measure. Asian Option Pricing Using Monte Carlo Simulation and Variance Reduction Method 04/24 - 05/24

• Applied Monte Carlo simulations with variance reduction. Used Geometric Asian options as control variates to estimate risk- neutral prices of Arithmetic Asian options, which reduced estimation error and computational noise

• Designed a Python class for calculation and visualization, achieving high computational efficiency (correlation coefficients >0.95) Testing the Predictive Power of Single Alpha Directional Trading Signals 02/24 - 03/24

• Demonstrated that trading signals with good P&L results can exhibit weak predictive power in formal econometric terms

• Conducted OLS regressions on ETF daily returns to see the statistical significance of Bollinger Bands and MAXO trading signals

• Addressed heteroskedasticity by incorporating the VIX index. Evaluated regression quality (e.g. coefficient values, t-statistics) EXTRACURRICULAR ACTIVITIES

• Kaggle Silver medal: Home Credit - Credit Risk Model Stability; Forum: Quinnipiac GAME Forum, ARPM Quant Bootcamp

• Interests: Badminton, K-Pop, Ballet, Cooking Shanghai Cuisine (Favorite dish: Sweet and Sour Pork)

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