Lummezen Mondal, PhD, MSFM

(773) 383 1158 Lummezen@uchicago.edu

EXECUTIVE SUMMARY:

Results-oriented quantitative finance professional with more than ten years of work

experience in the fifth largest accounting firm in US. One of the two key personnel who

created the quantitative finance desk primarily for valuations of derivatives and structured

finance securities within the firm. Completed PhD in Finance with particular expertise in

Financial Modeling. Graduated MS in Financial Mathematics (MSFM - rank #3 in US) from

University of Chicago in December 2016.

Financial Modeling expertise include:

- Programing Skills: R, Python - Bayesian model: Ridge, Lasso - Swaps, Swaptions, CDS

C++, Matlab, SQL, SAS, VBA

- Monte Carlo Simulation

- Robust regression models:

Hubert, Tukey, OLS

- Multivariate Data: SVD,

FA, PCA, CCA, LDA

- Binomial/ Trinomial Model - HJM, Ho-Lee, Hull-White - Bloomberg, Quandl

- Risk Models: CCR, CVA, DVA, - Black-Scholes, Black’s model - Volatility: Garch, Heston

VAR, IRR, EL, LGD, PD - Options Risk: Delta, Gama - Model Validation

PROFESSIONAL EXPERIENCE:

Quantitative Finance Analyst (Summer Project) in Lincoln International LLC, Chicago,

from 06/30/2016 – 09/30/2016:

Worked closely with Credit Risk team to help build credit risk curves to compute market implied yields for CCC rated loan portfolio using Principal Component Analysis (PCA) and HJM framework; thereby generated customer confidence in the firm’s discount curves used for risky loan portfolio valuation purposes;

Built valuation models for mortgage based securities (MBS) incorporating

Prepayment risk and Make-Whole call provisions;

Quantitative Finance Manager in McGladrey, LLP, Chicago, from 11/2005 – 11/2015:

Worked closely with valuation partner to launch the Quantitative Finance Division within McGladrey’s Financial Advisory Services in 2005, for valuation of OTC equity and fixed income derivatives and mortgaged based structured finance securities thus saving millions of dollars every year spent on hiring external valuation firms for performing similar analyses;

Generated new revenue stream by developing several models for valuation of complex over-the- counter (OTC) Derivatives which include:

Warrant/ ESOP valuation model using Monte Carlo Simulation methodology

incorporating: o Correlation with returns of comparable companies

o Probability of down-round financing

o Adjustment to exercise price based on market conditions

o Adjustment to conversion ratio from warrant to stock

Binomial/ Trinomial model for valuation of stock options;

Using Excel- VBA built a model to generate marks for a portfolio of ~3,000 plain

vanilla interest rate swaps in six different currencies.

Building the above models helped our firm to generate credibility with internal audit clients and external valuation clients resulting into creation of an entirely new line of revenue generated from quantitative finance analyses;

Performed Model Risk Validation (based on SR 11-7 guidelines) of client’s models prepared for derivatives valuation purposes so as to streamline audit functions during QE and FYE;

Expert in using Bloomberg models to price Interest Rate Swaps, Swaptions, Cancellable (European, Bermudan, American) swaps, Variance swaps, Total Return swaps, Cross Currency swaps, Equity Basket Options, Caps and Floors, Options on Commodity Futures, Foreign Exchange Currency Forwards, Options of Foreign Exchange Currency Futures and Credit Default Swaps;

Performed CVA, DVA adjustments to fixed income derivatives valuations to reflect counterparty credit risks (CCR) in the post-crisis era;

Corroborated deep dive analysis on mortgage related structured fixed income securities like MBS, CMBS, CDO etc. which helped audit team to issue 10K filings for our audit clients specially during financial crisis in 2007-08 thus generating confidence in our external audit clients thereby helping in revenue generation for our Assurance practice;

Performed variance analyses on derivatives valuations per FASB guidelines thereby helping audit partners to gain confidence in client’s marks to be presented in their 10K filings;

Performed Value At Risk (VAR) and hedge effectiveness analyses helping our audit clients to remain complaint with SEC and FASB guidelines under current regulatory environment;

Trained junior employees to perform analyses of complex derivatives and improved realized margin for our Valuation/ Consulting practice;

Regularly prepared detailed memo on complex derivatives analyses for top management with a focus to present the material in a simplistic manner so that the analyses are easily comprehensible for non-quantitative personnel;

Performed rigorous statistical analyses on daily trading data from 1995-2013 for stocks traded on OTC-BB – for research and publication on idiosyncratic behavior of bulletin board stocks thus contributing to the research on bid-ask spreads for securities from one of the least researched areas of capital markets.

EDUCATION:

MS in Financial Mathematics (MSFM), University of Chicago (rank #4 in US)

12/2016;

PhD in Quantitative Finance, Stuart Graduate Business School, Illinois Institute of

Technology, Chicago [Scholarship recipient]

Dissertation Title: Two-Stage Dynamic Hybrid Scoring model that is able to

(i) distinguish between a financially weak and strong publicly traded firm and

(ii) predict the distance, measured in fiscal years, the firm with poor financial

health is away from its bankruptcy.

Made original contribution to the cutting-edge sector of Hybrid Scoring

model for bankruptcy prediction of public companies;

Dissertation thesis covers 100% of all public companies that bankrupted

between 1995 and 2005, in addition to DJIA and DJTA constituents during

this period;

Generated hybrid scores for publicly traded companies from a combination of

13 liquidity, profitability and solvency ratios and 4 market related ratios:

‘probability of default’, ‘distance to default’, ‘credit spread’ and ‘asset volatility’ computed from Merton model based on KMV framework;

Applied Principal Component Analysis, Markowitz Multifactor model and

optimization model based on Markowitz’s Portfolio Selection Theory to the

ten – year time series of the selected ratios and generated Probability Scores

to be assigned to each of the companies in our samples for each of the year;

Probability Scores correctly categorized 100% of out-sample firms into

financially weak and strong groups as measured by Receiver Operating

Characteristics (ROC) and Area Under Curve (AUC) parameters;

Based on Cumulative Accuracy Profile (CAP) results Probability Scores

have correctly identified 89% - 100% of out-sample companies by the number

of years to bankruptcy within the range of 1 year through 10 years to

bankruptcy;

Outperformed Altman Z’ and Z” model by 40 and 50 percentage points for

each of the nine years (two through ten) to bankruptcy for all out-sample

firms. During the period one year prior to bankruptcy, the results for all the

models are comparable.

MS in Applied Mathematica, Jadavpur University, India;

BS in Mathematics, Jadavpur University, India.

PUBLICATIONS:

Co-authored ‘Estimating the marketability discounts: a comparison between bid-ask spreads and Longstaff’s Upper Bound’, published in Journal of Applied Finance, Volume 23, No. 1, 2013;

Authored ‘A dynamic hybrid credit scoring model: a two-stage prediction of credit

quality’ Illinois Institute of Technology 2008, 244 pages; 3338042 – (Dissertation).