This resume summarizes Anshul Laad's experience in commercial mortgage-backed securities (CMBS) research and modeling. At Deutsche Bank, he developed tools to analyze loan-level data, project cash flows, model default rates, and estimate losses. He also created a surveillance tool to monitor loans. Previously, he held internships conducting financial analysis and building models. Laad has strong skills in financial modeling, programming languages like C++ and Excel VBA, and deal-level analysis of mortgage and asset-backed securities.

1. ANSHUL LAAD
227, SIP Avenue, Jersey City, NJ - 07306 • (201) 218-4371 • anshul.laad@iitbombay.org
EDUCATION BARUCH COLLEGE, CITY UNIVERSITY OF NEW YORK (CUNY), NY
MS – Financial Engineering, Expected graduation - January 2009
9/07 – Present
INDIAN INSTITUTE OF TECHNOLOGY, BOMBAY, INDIA
7/02 – 8/07
Master of Technology and Bachelor of Technology, August 2007
Major: Process Engineering; Minor: Metallurgical Engineering and Materials Science
EXPERIENCE DEUTSCHE BANK SECURITIES INC. New York, USA
6/08 – Present Securitization Research – CMBS Group
• Generated time series of loss severity for historically liquidated commercial loans
• Calculated delinquency rate variations with time for different commercial property types
• Developed a mapping module for deal level cash flow data and MSA level real estate data
• Projected net operating income (NOI) data using the historical cash flow and real estate data
• Developed term default model to project loan level default rates incorporating the changes
in datasets mapping and projections
• Developed maturity default model to determine refinancing ability of loans at balloon term
• Developed a tool to generate covariance matrices for various deal level quantities
• Simulate default scenarios and calculate implied bond and deal level losses using implied
CDRs for spreads of all CMBX indices
• Developed surveillance tool to monitor loans using changes in chosen deal level quantities
LAWRENCE N. FIELD CENTER FOR ENTERPRENEURSHIP, BARUCH COLLEGE New York, USA
9/07 – 5/08
Field Fellow, Small Business Development Center
• Assisted business advisors and faculty to serve the clients, providing them with technical
support services including assistance in developing business and marketing plans
• Developed income projections, break even and industry ratio analysis templates for clients
EQUITY DEVELOPMENT LTD London, UK
7/07– 8/07; 1/08
Equity Research Division – Intern Analyst
• Conducted fundamental analysis, valuation, financial modeling and stock price estimation for
technology and financial sector companies
• Built models to initiate research coverage on KBC Adv Tech, Eredene Capital
• Updated earnings and revenue forecasting for 15 stocks
• Generated research articles pertaining to Indian infrastructure, global oil & natural gas sector
TRI TECHNOSOLUTIONS PVT LTD. Mumbai, India
12/05 – 4/07
Core Group Member
• Involved in all aspects ranging from conceptualization to execution for this startup
• Supervised front-end operations including workshops and marketing and the back-end
operations including product design and material procurement
ENEA CENTRO RICERCHE Brindisi, Italy
6/05 – 7/05
Materials Engineer
• Calibrated and Suggested modifications in the deposition process of Pt-Au thin films
RELEVANT SKILLS FINANCIAL MODELING
• Option pricing using Monte-Carlo method, binomial trees and the Black-Scholes model
• Priced Barrier, Asian, Digital options using Finite Element and Monte Carlo methods
• Portfolio managing/P&L tool for fixed income and equities.
• Modeled assets and liability side cash flows for various waterfall scenario to value ABS
• Modeled Auto loan deal with depreciation and delinquency modeling
• Delinquency, loss severity and default rate analysis for Mortgage Backed Securities
• Mathematical modeling of real time events (e.g. Tree growth, effects of smoking)
• Financial valuation, earnings and revenue forecasting
PROGRAMMING
• Proficient in C++, Excel, VBA, SAS and Matlab
• Co-Founder, ‘In Service of the Lady’ Fund Raising Campaign, IIT Bombay, 2006–Present
POSITIONS OF
• Chairman (Expenditure Committee), HATS-Hostel 7, 2007–Present
RESPONSIBILITY
• Research Associate, Sensor’s Lab, IIT Bombay, 2005–2007
• Teaching Assistant (Modelling & Analysis), Dept. of MEMS, IIT Bombay, 2006
COMPUTER O/S: Windows, Mac, Linux
SKILLS Tools: Bloomberg, Polypath, Intex, Trepp, MS Office, Autocad, Fluent, Origin, LaTex

2. Work Presentation
(Part Time job at Deutsche Bank)
Loan Level Analysis of CMBS
Anshul Laad
Jun 08
Jun-08 to Present
Deutsche Bank Securities
New York

3. Loss Severity
Input: Dynamic Data for CMBS Loans
Output: {Loss Severity v/s Time} X Property type

4. Delinquency Rates
Input: Dynamic Data for CMBS Loans
Output: {Delinquency Rate v/s Time} X Property type

5. Mapping Module + DSCR Modeling
This project creates necessary datasets
Dataset with
for CMBS Term Default Model Default
DSCR Model
Projections
Perfectly mapped Loan
level and Real Estate data
Input: Dynamic Data for
CMBS Loans + Real Estate
DSCR
Data
Output: Combined Dataset Modeling
for further analysis
Real
Loan Level
Estate Data
Dynamic Data
(MSA Level)
(Location Details)

6. Default Rate Estimation
Historical suggestions combined with Macro Economic
forecasts estimate the Default Rates for individual
Commercial Loans
Main parameters CDR Vector
under consideration: 2.5
Quality of Asset
2
Mortgage spread
1.5
CDR (%)
DSCR
Delay in obtaining 1
financials 0.5
Msa vacancy 0
1 2 3 4 5 6 7 8 9
etc... Age (yr)
Sample Output

7. Maturity Default Estimation
Calculate the remaining balance at the end of the amortizing loan
Estimate the Capitalization Rates
Recalculating LTV t check th ability t re-finance th l
R l l ti to h k the bilit to fi the loan
with & without Maturity Default
Main parameters
under consideration: 100
DSCR Default Rate (%)
10
Amortization schedule w/o
w/
t
Cap rates 1
LTV
0.1
1 2 3 4 5 6 7 8 9 10
Age (years)
Sample Output

8. CMBX Spreads &
Implied Losses
Run default
R d f lt scenariosi
based on Implied
CDR for all the CMBX
tranches
Output
Implied Tranche Loss
Implied Deal Loss

9. Surveillance Tool
Tool for in-house surveillance.
Selected loans would be monitored via a series of black
boxes for various indicators suggesting further analysis.
Black Box(es): calibrated to take signals from changes in
DSCR, Vacancy, Rating, Special Servicing, Upcoming
Maturity etc…
Live
Data Feed
Dynamic Data
(monthly) Black Box(es) Loans for
for (work with ∆’s) further research
Selected Loans

10. Tool to generate Covariance Matrices
Generate dataset with deal level loss (%) amount
Generate Covariance Matrices by Vintage