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Mathematical Finance, Fixed Income models, Python, SQL, C++, MATLAB, R

Jersey City, NJ
March 25, 2021

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646-***-**** jx****@JINGHAN http://www.XUE


Columbia University GPA: 3.97/4.33 New York, NY

M.S. in Operations Research Sept. 2018 – Feb. 2020 Coursework: Optimization, Stochastic Process, Simulation, Tools for Analytic, Financial Engineering, Asset Allocation, Algorithmic Trading, Quantitative Risk Management, Credit Risk Model and Derivatives. Beihang University(Former: BUAA) GPA: 3.81/4.00 Beijing, CN B.S. in Financial Engineering B.S. in Applied Mathematics Sept. 2014 – Jun. 2018 Coursework: Mathematics in Finance, Econometrics, Probability, Statistics, Linear Algebra, Calculus, Fixed Income & Securities. TECHNICAL SKILLS

Languages: Python, SQL, C/C++, R, Matlab Developer Tools: Git, VS Code, PyCharm, MS SQL Server WORKING EXPERIENCE

WisdomTree Investments, Inc. New York, NY

Modern Alpha Intern Jun. 2019 – Aug. 2019

• Developed a dynamic Black-Litterman Optimizer on ETF model portfolios for internal asset re-balancing applications.

• Generated a fully automated workflow of loading, extracting &cleaning over 30 ETF model portfolios’ fundamental and market data using an integration of SQL & Python.

• Derived portfolios’ prior equilibrium distributions over 12 asset classes under reverse optimization method, estimated the posterior distributions in terms of investors’ views and uncertainty.

• Designed a user-friendly interface for portfolio selection, market view creation, constraints adjustment, exposures visualization and efficient frontiers generation.[Python & Dash]

• Implemented several scenario tests over 30 views’ uncertainty and tilt constraints adjustment.[Python]

• Updated WT’s Asset Allocation system with a clean and clear re-balancing interface. [PyCharm] 91 JinRong (Finance) Information Service, Inc Beijing, CN Quantitative Research Intern Jun. 2017 – Aug. 2017

• Delved into the study on statistical arbitrage and simulated pair-trading over China commodity futures market in R and Excel.

• Identified trading pairs on 24 commodity futures utilizing correlation, distance approach and co-integration analysis.[R]

• Generated trading signals through fixed threshold and moving average methods, back tested trading strategy under 4 methods for the pair selection.[R]

• Implemented performance evaluation by adjusting number of alternative trading pairs, rolling periods and error bounds, visualized complete comparison result in Excel.[R, Excel] PROJECTS

A Multi-layer Network Learning Algorithm in Trading Volume Prediction C++ Oct 2019 – Dec 2019

• Conducted a multi-layer feed-forward network for Index daily trading volume prediction.

• Computed loss function and generalized gradients with respect to weights and activation functions in the network.

• Developed a back propagation framework under Frank-Wolfe optimization method in C++ to train the network.

• Applied Multi-threading and learning rate adjustment to boost the efficiency of training. Application of Hawkes Process in Algorithmic Trading Python, MATLAB Mar 2019 – May 2019

• Explored the effectiveness of Hawkes Process in modeling U.S. High-Frequency dynamic market price.

• Using MLE to estimate parameters given by stochastic process and stochastic differential equations.[Python]

• Created jump reinforcement signals to predict the movement of HF price based on symmetrical Hawkes Process.[Python]

• Evaluated confidence level of jump signals through Wavelet Decomposition Analysis.[Matlab]

• Designed a modifying Volume Weighted Average Price (VWAP) execution strategy based on jump reinforcement signals.

• Simulated in Python and testified cost decreasing in buy-side trading compared with VWAP. Analysis of Chinese market risk, uncertainty and monetary policy R Dec 2017 – May 2018

• Generated risk aversion indicator by decomposing iVIX and forecasting market conditional variance.

• Constructed four-variable VAR model to examine interaction between risk aversion, uncertainty, monetary policy stance and business cycle, examined the robustness with replacing measurement of monetary policy stance.

• Manipulated impulse response functions to identify impact terms of different shock directions.

• Concluded asymmetric affections among lax/tight monetary policy, risk aversion and uncertainty in short/long run.

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