Xiaorong Zou has over 10 years of experience in model validation and risk management. She currently works as a Senior Manager at BMO Financial Group, where she manages a team that validates market risk models. Prior to this role, she worked as a lecturer teaching mathematics and finance courses. She has a PhD in Mathematics and masters degrees in Electrical Engineering, Actuarial Science, and Applied Math.

1. Xiaorong Zou PhD
Email: xiaorzou@gmail.com Tel: 647-989-9618
QUALIFICATION HIGHLIGHTS
 Advanced knowledge in asset pricing model across broad range of financial products
 Familiar with industry practice and regulatory requirement on market risk management
 Strong background in math and computational algorithms used in asset pricing and risk management
 Strong analytical and implementation skills
 Excellent documentation skills
 Proficient in C++/C#, Matlab, MS Office suite, working knowledge on SQL
PROFESSIONAL EXPERIENCE
1) BMO Financial Group 2011– present
a) Senior Manager, Model Validation, 2014 – 2015
b) Senior Model Risk Specialist, Model Validation, 2013 – 2014
c) Model Risk Specialist, Model Validation, 2011 – 2013
Accountabilities
1) Managing market risk validation group with eight team members
a) Providing supervisions for all projects in market risk model group; b) Coordinating with model developers, model
users, audit team, technical support team regarding model governance and model validation supports;
2) Validating wide range of risk and asset valuation models used for capital calculation and pricing derivatives in Equity, FX,
IR, Commodity and Credit. The accountabilities include:
a) Validating model based on theoretically soundness, correct implementation, fitness on business application,
alignment with industry practice and regulatory requirement. b) Data quality; c) Replicating model outputs; d)
Building independent benchmark; e) Model governance.
Highlights on some validation projects
Asset Valuation models: (replication of model output is mandate for all asset valuation vetting projects)
 Fixed Income: The model is used to price financial product in fixed income category, which includes a wide range of bonds
such as standard bond, floating bond, callable bond, real return bonds, structured products and asset backed security. The
model uses optional adjusted spread method (OAS) for products with projected cash flow. The standard reduced form
intensity model was used as independent benchmark to analyze the impact of issuer specific credit risk. The model built
credit spread curve to price corporate bonds based on the issuer specific credit risk information embedded in market price,
CDS spread and recovery rate.
 Commodity Swaption: The two-factor model is used to price commodity swaption for commodity products with three
different underlying future assets NG, WTI and AECO. The model has flexibility to capture seasonality, long term and short
term uncertainties and volatility skew. The model outputs turns out to align properly with industry practices (Totem census)
across a range of maturities and moneyness.
 American Option: The model is used to price American option in Equity category and it uses the standard binomial tree.
Risk Models:
 VaR for Market risk (project driver): The model produces VaR for regulatory capital and economical capital calculations
on market risk and captures general market risk and specific market risk of the trading portfolio. The major components of
the model includes: data quality, pricing engine, risk factor/proxy selection, calibration, simulation, evaluations, risk
aggregation and model governance control.
 sVaR for market risk (project driver): The model defines the stressed market conditions and produces related VaR. The
major components include stressed period selection, stressed period recalibration and proxies for risk factors with missing
data during stressed period.
 Market Risk Economic Capital: The model provides VaR with one year horizon for trading portfolio and is used for
economic capital calculation and capital allocation. The model captures such effects on portfolio as market movement,
liquidity of portfolio and management action.
 Stress Testing: The model provides stress scenarios and the model output is used for capital monitoring purpose.
 Incremental Risk Charge (IRC): IRC is required in BASEL2.5 to cover credit risk due to default and credit migration.
The model captures liquidity risk, general market risk and specific risk and correlation of risk factors. The independent
benchmark based on industry standard (Credit Metrics) was implemented and compared against the model outputs.

2.  Profit attribution and Analysis (PAA): The model is used to calculate and attribute profit and loss (P&L) at the end of
each trading day. The model decomposes P&L into several components for one to better understand the nature of the risk
of the portfolio.
 Volatility Skew: The model is used to capture volatility skews for the risk factors in Equity, Interest Rate, Foreign
Exchange Rate, Commodity and Credit used in VaR system.
 Dividend Risk: The model is used to capture dividend risk in VaR system used for regulatory capital.
2) Lecturer, Dept of stat and actuarial science, Univ. of Waterloo, Canada 2009 – 2011
Teaching several courses for graduate/senior in mathematical finance and actuarial. The courses include
 Math model in Finance: The key ideas of asset pricing and hedging strategies used in contemporary finance industry and
the mathematical tools for derivatives such as information filtration, martingales, diffusion process and computational
algorithms to price derivatives
 Asset liability management : The models and the risk measurements used in portfolio management with focus on interest
rate risk and credit risk management
 Analysis of Survival Data: Statistical tools to estimate the risk of loss, particularly, construct loss distribution based on
samples for the quantitative measurement of risk
 Random Process : The important random processes and their applications in real world such as Brownian motion,
Martingales, Renewal Processes, and Markov processes
3) Associate Professor, Nanjing University, Nanjing, China 2003 – 2007
Teaching math/statistics courses, supervising students, and researching in analysis and geometry
4) Lecturer, Univ. of Sci. and Tech. of China, Hefei, China, 1990 – 1995
Teaching math/statistics courses and researching in geometry
5) Software Engineer, WebEver Inc, San Jose, California, USA 2001 – 2003
Developed a web server agent and designed testing cases to validate data integrity and model outputs
PROFESSIONAL SKILLS
 Thorough understanding of advanced asset pricing theory with details at implementation level.
 Solid skills in implementing asset pricing models across wide range of products in equity, interest rate, commodity, credit
and foreign exchange. Working experience with QuantLab.
 Advanced knowledge of interest rate models including major short rate model and their extensions, lognormal
forward-LIBOR model (LFM) and lognormal forward-swap model (LSM), volatility smile models such as local volatility
model, stochastic volatility model and uncertain-parameters model.
 Working knowledge of pricing model involved with two interest rate curves such as foreign exchange and inflation-indexed
derivatives
 Working experience of major market risk models such as VaR/stressed VaR, Debt Specific Risk (DSR), Incremental Credit
Charge (IRC), Economic Capital, Stress Testing and Counterparty Credit Risk (CCR), together with a good understanding
of industry practice and regulatory requirements on those models.
 Sound knowledge of regulatory requirement and guideline in market risk area as Comprehensive Capital Analysis and
Review (CCAR).
 Professional knowledge in 1) probability theory and statistical analysis; 2) stochastic processes and time series analysis; 3)
PDE with working experience of its numerical solutions; and 4) computational algorithms used in Monte Carlo Simulation,
optimization, and data analysis
OTHER RELEVANT VALUATION PROJECTS
 American option on exponential Levy processes: pricing American option under the assumption that underlying asset
follows Levy process of exponential type. The method avoids simulations and is computational efficient by using FFT. The
model also has the flexibility to implement volatility term structure. The implementation covers 6 underlying dynamics:
Black-Scholes, Merton, Kou, Variance Gamma, Normal Inverse Gaussian, and Tempered Stable. This is a joint research
project with Professor A. Kolkiewicz.
EDUCATIONS
PhD, Mathematics, University of Southern California, Los Angeles, USA
MS, Electrical Engineering, University of Southern California, Los Angeles, USA
MS, Actuarial Science, University of Waterloo, Waterloo, Canada
BA, Applied Math, Southeast University, Nanjing, China
2001
2001
2009
1983