Loan Level Analysis Of Cmbs Linkedin

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  • 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