Front Office Data

Luyong (Jeff) Wang

Phone: 609-***-****

Email:******.****@*****.***

Status: US citizen

OBJECTIVE:

To obtain a quantitative analyst position in investment bank, hedge fund or other financial firm

EXPERIENCE:

Barclays

VP, Front Office Credit Quant Quantitative Analytics Desk Strategy Group - Credit Products Dept (2017 – present)

• Model development, testing and documentation of valuation model of collateralized TRS (Total Return Swaps) on corporate risky (callable) bond or loan & credit risky bond forward pricing model built upon extended BK2 bond model in C++.

• Business-As-Usual support (BAU) and model development of Corporate Credit bond / Callable Bond /Bond Option Pricing using Dual Black–Karasinski (BK2) Model: A joint interest rates and credit model with discretized multi-dimensional lattice. Re-implementing BK2 model into next generation integrated cross asset Omega infrastructure in C++.

• Model development and documentation of NBT (Negative Basis synthetic structure with bespoke asset-linked swaps and CDS) pricing model and BAU quant support.

• Model development of bond portfolio margin model and EM bond haircut model for financing, repo/sec lending prime desk.

• Model re-implementation of Bond Liquidity Cost Score Model upon CATS/TRACE and its documentation.

• Knowledgeable with other credit derivatives, e.g. single name and index CDS, credit swaptions, iboxx TRS etc.

Bank of America Merrill Lynch

VP, Sr. Quantitative Finance Analyst CCR-Quant Department (2015 – 2017) Model development, testing, documentation for the cross-asset derivative pricing models in the pioneering unified BAML Front Office C++ CVA/CCR IMM pricing and simulation analytical library (Team lead of interest rates products, peer reviewer of other assets).

• Design and Implement Counterparty credit risk modeling of Securities Financing Transactions (SFT) in prime brokerage; modeling bond repo and Fixed Income Forward by spread-to-yield Vasicek simulation & Parameter Calibration by Full Parameterization

• Lead model development on pricing models for Interest Rates vanilla derivatives (e.g. Bond Future Option, Cap/Floor, Swaption, etc.) & exotics derivatives (e.g. Range Accruals) in SABR implied volatility skew config on rates scenario simulation engine.

• Major contact for quantitative support and model development for Rates Derivatives and SFT & coordinate CCR/CVA projects.

Citigroup (2008 – 2015) VP, Interest Rates Desk Quant FICC - Rates Quantitative Analysis Department

Front office quantitative analyst developing Fixed Income IR derivative pricing/risk analytical library (C++) for trading business and supporting bond market-making, rates flow and algo trading desks

• Design and implement Multifactor delivery option model for bond future based on Principle Components Analysis (PCA) and Monte Carlo simulation, providing delivery option model (DOM) for bond future trading (C++).

• Design and implement US treasury T-BILL floating rate notes pricing model in C++, new product of 2014, traded by financing desk.

• Design and implement Canadian Bond Future and Single OMX treasury future pricing analytics and risks (C++).

• Design and implement portfolio hedging strategy based on principle component analysis of the off-the-run govt bond curve and backtest strategy.

• Design and implement pricing and risk analytical libraries of bonds, inflation bonds, floating rate notes, STRIPS and bond futures for trading desk and IT system. Design and implement analytics, including curve building, bond math, curve spreads, risk hedging, profit attribution analysis, and market making (C++).

• Build Bond Relative Value Analytical Model to identify Rich/Cheap Spread Signal for Govt Bond and Corporate Bond Trading

• Develop the Affine 2+Term Structure Model to build live On-The-Run Bond Curve for bond market-marking and algo desks.

• Familiar with various interest rates derivatives (both vanilla and exotics) products.

• A substantial track record of delivering analytical or quantitative projects to trading desks and FO quant library management skills.

• Design and implement bond portfolio hedging strategy using quadratic optimization and backtest the strategy.

• Desk quant support trading, including analytical spreadsheet writing, partial DV01 hedging, Key Rate Duration, Relative Value analysis, portfolio valuations & hedging, curve fitting, P&L PAA analysis, data analysis, trading strategies & market making support.

AVP, Quantitative Analyst (Joined 4/2008 as AVP, promoted to VP on 2/2009) Risk Analytics

Counterparty Credit Risk Analytical Modeling on Multi-Asset OTC Derivatives & Backtesting. The engine deploys Monte Carlo simulation models to evolve the present state of universe of market factors to predict potential future distributions, and deploys derivative pricing models to translate the distributions of market factors into distributions of portfolio values. The innovative backtesting approach tackles testing validity of path dependency over the long horizon simulation of exposure and innovatively arrives at a coherent stat test to probe the model robustness cross IR, FX, Equity, Comm & Credit derivatives.

Performed thorough model validation on both simulation and multi-asset OTC derivatives pricing model library (C++). Enhanced and updated the analytic engine and derivative valuation models for counterparty credit risk and credit value adjustment.

Supported trading desks daily. Created contingent tools and models, Monte Carlo or closed-form, to improve the timeliness and accuracy of credit risk PSE, EPE and valuation (CVA/DVA).

Siemens Princeton, NJ (2004 –2008)

Research Scientist & Software Engineer Integrated Data Systems Department

Field: Predictive Modeling, Machine Learning & Data Mining on Large Scale Quantitative data

Computer Science Research

Boosted cascade learner to refine prediction of transcription binding sites. I propose a discriminative modeling algorithm based on boosted cascade to utilize massive dataset for prediction. It revolutionarily utilizes the negatives for the prediction and achieves optimal efficiency and reduces the false-positive rate when applied in large scale (First author journal paper).

A nonparametric kernel smoothing approach for segmentation and data analysis from high-throughput array CGH data to achieve edge-preserving smoothing of the quantitative data and detect the change point (First author journal paper).

Other Predictive modeling and Decision Support Projects (e.g. Patents: US7664328, US735521, US7593913)

Software Engineering in C++

AutoEF: Knowledgebase-guided real-time proprietary modeling & detection for echocardiography algorithmic development

Cardiobiometrics: Knowledgebase-guided measurement detection for proprietary echocardiography algorithmic development

The algorithm uses progressive pattern recognition technology based on a comprehensive dataset, and uses discriminative learning to detect cardiac quantification from ultrasound image. Our algorithmic implementation was embedded syngo® US Workplace software for ACUSON Sequoia ultrasound system, which achieves unparalleled accuracy in cardiac quantification.

Data Analysis, Machine Learning and Pattern Recognition applications

Algorithmic identification of transcription factor binding sites for ChIP-chip data (scientific milestone published in Science) .

EDUCATION:

Ph.D. Columbia University 2004

Bioinformatics (a computational science on machine learning & predictive modeling) GPA 3.9

M.S. Peking University 1999

Biophysics GPA 3.8

B.S. Nankai University 1995

Physics GPA 3.8

SKILLS:

• Proficient in C++. Knowledgeable with C++11, Perl, Java, SQL, Python, R, XML, openGL, HTML, VBA, KDB+, WebFocus, CGI, FORTRAN, LISP

• Proficient in Matlab, Bloomberg, Office, Excel, Photoshop. Knowledgeable with UNIX/Linux

• Proficient in machine learning and predictive algorithms.

• OOP and design experience; knowledge of software designs, multithreading and relational databases.

• Knowledgeable in Numerical Methods in Computation, Calculus, Mathematical Physics, and Statistics

• Understanding of Derivatives: Pricing methodologies, Black-Scholes, Monte Carlo Simulation, Stochastic Calculus, SABR, Trade Operations, Interest Rates Derivatives, Credit Products, Risk Management methodologies, VaR, Basel, CVA, DVA

PERSONAL TRAITS

Smart, logical, easy learner, excellent writing and presentation skills, easy going, team player, naturally curious with a wide range of interest. Able to work in a fast-paced environment and meet deadlines under high pressure.

HONORS

99-04 Columbia University Faculty Fellowship

Peking University Academic Scholarship

Peking University P&G Scholarship

Nankai University Scholarship, First-Prize

SELECTED PUBLICATIONS

JOURNAL PUBLICATIONS:

Wang, L.Y., “A mean shift computational approach for array-CGH data analysis”, Gen. Res. (ISI impact factor 11.4) (2009)

Borneman, A, Wang, L.Y. et al Science (2007), 10: 815-819

Wang, L.Y. et al “BoCaTFBS: a Boosted Cascade Learner to Refine the Binding Regions Suggested by ChIP-chip Data” Computational Method, Gen. B. (ISI impact factor 9.712) (2006), 7(11): R102.

Wang, L.Y. et al Journal of Computational Biol (2006) 13(10): 1673-84.

Wang, L.Y. et al Lecture Notes in Computer Science (2007), 4532: 119-129

Wang L.Y. Journal of Bioinformatics & Computational Biol, 2005, 3(6), 1391-1410

Wang, L.Y. et al “Local structure prediction with structure-based sequence profiles” Bioinformatics (2003) (10), 1267-1274

Wang, L.Y. et al “Local structure-based profile database for structure predictions” Bioinformatics (2002), (12), 1650-1657

CONFERENCE PUBLICATIONS

10 first-author publications in IEEE Computer Science Society CSB/EMBC/RECOMB

PATENTS

US PATENTS: US7664328, US735521, US7593913

REFERENCES AVAILABLE UPON REQUEST

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