Lummezen Mondal, MSFM, PhD

33 W Ontario Street

Unit # 52D

Chicago, Illinois 60654

Phone: (773) 383 1158

Email: 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:

Derivatives Model Validation Bayesian models: Ridge, Lasso Swaps, Swaptions, CDS

Volatility Models: Garch,

Heston, CEV, SABR

Robust regression models:

Hubert, Tukey vs OLS

Multivariate Data analysis:

SVD, FA, PCA, CCA, LDA

Monte Carlo Simulation HJM, Ho-Lee, Hull-White Bloomberg, Quandl

Binomial/ Trinomial Model Black-Scholes, Black’s model R, Python, C++

Options Risk: Delta, Gama Forward measures, CVA Matlab, SAS, VBA

PROFESSIONAL EXPERIENCE:

Quantitative Finance Analyst (Part-time) in Lincoln International LLC, Chicago, from

06/30/2016 – onwards:

Working closely with Credit Risk team to help build credit risk curves to compute market

implied yields for risky mortgage backed securities, thereby generating customer confidence;

Building valuation models for 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 derivatives and 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

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;

Validation of client’s models prepared for derivatives valuation purposes;

Expert in using Bloomberg models to price Credit Default Swaps, Interest Rate Swaps,

Swaptions, Cancellable (European, Bermudan, American) swaps, Variance swaps, Total

Return swaps, Cross Currency swaps, Equity Basket Options, Caps and Floors, Commodity

Options, Currency Options and Credit Default Swaps;

Corroborated deep dive analysis on structured fixed income securities like MBS, CMBS 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 hedge effectiveness analysis helping our audit clients to remain complaint with

SEC and FASB guidelines;

Trained junior employees to perform analyses of less 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.

Project Finance Manager in Small Industries Development Bank of India (SIDBI), India from

03/1996 – 11/1998

Prepared guidelines and evaluated marketing projects for small businesses that could be

financed under existing project finance guidelines– thus helped the bank expand its revenue

sources by financing marketing projects which are not within its standard financing practices;

Prepared market research reports for bank’s internal use so as to identify products/ services

generated by small businesses with little available financing options for their marketing

related activities – identified new areas where the bank can effectively target its promotional

activities.

EDUCATION:

MS in Financial Mathematics, 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.

Relevant Courses:

Mathematical Foundation of

Option Pricing

Foreign Exchange and Fixed

Income Derivatives

Robust Regression and

Quantitative Trading Strategies

Statistical Risk Management Portfolio Theory and Risk

Management

Computing for Finance I, II, III

(C++)

Stochastic Calculus Futures, Options and OTC

Derivatives

Bayesian Methods

Numerical methods Probability and Statistics Multivariate Data Analysis

MBA in Finance & Accounting, University of Findlay, OH [Scholarship recipient];

PGDM (equivalent to MBA) in Marketing & Finance – Indian Institute of Management,

Lucknow (IIML);

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).