Assistant Professional Experience

Ning (Daniel) Wang

+1-347-***-**** *** **nd St. Apt: 22R, New York NY *******@*******.***

EDUCATION

Fordham University, Gabelli School of Business New York, United States Master of Science in Quantitative Finance GPA:3.7 December 2017

• Coursework: Financial Modeling, Stochastic Calculus, Fixed Incomes, Equity Derivatives, Econometrics, Machine Learning, Time Series, Quantitative Data Analysis, etc.

• Led Fordham trading team for CME trading summit held at Chicago and interacted with founder of financial futures Simon Fraser University Vancouver, Canada

Bachelor of Science in Actuarial Science Major GPA:3.7 August 2015

• Coursework: Linear Algebra, Time Series, Ordinary Differential Equation, Mathematical Statistics & Probability, etc.

• Led SFU representatives for CAE meeting held in Chicago and presented to 200 professionals on a Pension case study PROFESSIONAL EXPERIENCE

BNP Paribas New York, United States

Trading Assistant in G10 Rates, Consultant January 2018 – Present

• Built P&L reconciliation on CDS, MBS, Treasuries and interest rate swaps using sensitivities-based/step-based approaches to explain P&L in terms of Greeks and secondary level of effects

• Developed automation tools/algorithms upon traders’ request on G10 rates & credit derivative desk in Python & VBA

• Monitored sign off groups’ risk exposures and sent clean P&L to market risk group for Value-at-Risk calculations Morgan Stanley Capital International Beijing, China Quantitative Analyst, Project Intern December 2015 – May 2016

• Reported daily Value-at-Risk estimates on equity indexes on 95% and 99% level using Monte Carlo simulations and equally weighted/exponentially weighted historical simulations on MATLAB

• Conducted back test on Value-at-Risk estimates’ performance with unconditional methods and validated the entire forecast distribution through realized p-value by Kolmogorov Simonov statistics test RESEARCH & PROJECTS

Fordham University, Gabelli School of Business New York, United States American Option Pricing (MATLAB) Fall 2016

• Applied LS-Monte Carlo approach on American option pricing by evaluating expected holding values through 2nd/3rd/ 4th degree polynomial linear regression on explanatory variables of stock price

• Compared the calculated option price with realized bid-ask options’ price intervals on Bloomberg, with over 65% average accuracy and concluded that 2nd degree regression method is the most optimal and time efficient Stock Price Prediction (Python) Fall 2017

• Researched predictable patterns in stock price direction using SVM technique and applied radial/linear kernel function

• Modeled stocks’/index’s momentum and volatility of price changes in past n-day as explanatory variables and back tested the trained model with over 60% mean prediction accuracy

• Further investigated effects on PE ratio/market capitalization of stocks and interest rate on the pricing model Agency-type & Private-type MBS Pricing (VBA) Spring 2017

• Implemented Vasicek model to simulate 100 interest rate paths for discount factors based on 1-month Treasury rates

• Employed 100-PSA on prepayments’ process with a limit of conditional prepayment rate of 6% annually

• Developed MBS pricing model with/without default risk and further modeled the price with a range of recovery rates and monthly default intensities given by Standard & Poor’s rating system SKILLS, DERIVATIVE CLASSES & INTERESTS

• Programming Skills: Proficient in VBA, Python, MATLAB, SQL, R & Bloomberg

• Derivative Classes: Interest Rate Swap, Forward & Future, CDS, RMBS, Exotic Options & Equity Derivatives

• Interests: Snowboarding, Climbing, Working Out, Running & Blockchain

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