Education
M.S. Finance
Work Experience
YieldChain Co.Senior Quantitative Researcher
September 2021 - PRESENT
- Engineered an advanced C++ library, benchmarking the efficacy of multiple pricing engines, encompassing the optimization of Monte Carlo simulation techniques via CPU and GPU integrations, finite difference approaches, and quadratic pricing methodologies. Demonstrated proficiency in cross-platform development across Linux, MacOS, and Windows environments.
- Conducted intricate intraday trading analyses in the Chinese ETF market, leveraging strategies derived from proprietary pricing engines using C++. Possess specialized expertise in pricing and hedging Asian-style options within the energy sector.
- Employed advanced quantitative techniques utilizing Python and SQL to engineer web-service volatility surfaces, integrating stochastic volatility models such as SSVI, SABR, and Heston, along with the local volatility surface via finite difference methods and machine learning strategies. Independently conceptualized and developed the Optshare Python package, now available on PyPI.
- Conducted in-depth analysis of interest rate derivative pricing, emphasizing short-term rate and LMM-class models. Spearheaded the design and development of a sophisticated OTC interest rate option pricing tool, pivotal for clients in enhancing trading, pricing, and hedging activities within the Chinese OTC market. Demonstrated proficiency in FICC products, encompassing areas such as interest rate derivatives, quanto options, and foreign currency instruments.
- Contributed to the pricing mechanisms of new structured products within the OTC system, employing C++, JavaScript, and the UX design tool, Axure. Possess robust expertise in web development frameworks and libraries, including jQuery, React, Django, and the C++ Mongoose framework.
- Undertook comprehensive research into contemporary advancements in derivative risk and pricing modeling in academic circles, with a specific focus on the integration of deep learning techniques for option pricing.
Research Experience
Optimization of the Monte Carlo Simulation Methods in the Over-the-Counter Derivative Pricing
July 2022 - July 2023
- Developed an advanced Monte Carlo engine utilizing modern algorithms and enhanced stochastic process discretization. Incorporated the FASTNORM Algorithm, tripling computational efficiency.
- Leveraged vectorization and OpenMP-based multi-threading programming techniques in contemporary CPU architectures, boosting computing speeds by 40x.
- Implemented a GPU-driven Monte Carlo algorithm with CUDA programming, realizing a speed increase of over 100x compared to traditional methods.
Advanced Pricing Models for Interest Rate Derivatives: An In-depth Analysis of the Over-the-Counter Derivative Market in China.
July 2023 - PRESENT
- Delved into the complexities of options intricately linked to key benchmarks such as the Loan Prime Rate (LPR) and Shanghai Interbank Offered Rate (SHIBOR).
- Conducted a comprehensive examination of interest rate derivative pricing, emphasizing short-term rate and LMM-class models.
- Orchestrated the design and evolution of an advanced OTC interest rate option pricing tool, essential for clientele in the Chinese OTC environment, optimizing trading, pricing, and hedging operations.
- Undertook an empirical investigation, striving for clarity on methodologies and implications of each model in the context of contemporary interest rate derivatives.
Empirical Analysis of Volatility Modelling Using Chinese Intraday Option Data
January 2023 - PRESENT
- Utilized Python and SQL for efficient data processing and developed the research-oriented Optshare package, available on PyPI.
- Developed volatility surfaces encompassing local volatility, stochastic volatility (including SSVI-class, SABR-class, and Heston models), and stochastic local volatility (SLV) frameworks.
- Leveraged advanced numerical methods, including finite difference, fast Fourier transform, and quadratic integration, combined with machine learning techniques to align with intraday option data, optimizing computational efficiency.
Prospect theory and asset prices: an empirical test in the context of the Covid-19 pandemic period
March 2021 - September 2021
- Conducted an empirical dissertation on "Prospect Theory and Asset Prices" during the Covid-19 pandemic under Dr. Anita Suurlaht at Smurfit
- Devised a 7-Factor CAPM model, and performed cross-sectional analysis of prospect theory against U.S. and Asian stock market data using R and Stata
Skills
Note: I think these sections are silly, but everyone seems to have one. Here is a *mostly* honest overview of my skills.