Loan Level Analysis Of Cmbs Linkedin


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Loan Level Analysis Of Cmbs Linkedin

  1. 1. ANSHUL LAAD 227, SIP Avenue, Jersey City, NJ - 07306 • (201) 218-4371 • 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. 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. 3. Loss Severity Input: Dynamic Data for CMBS Loans Output: {Loss Severity v/s Time} X Property type
  4. 4. Delinquency Rates Input: Dynamic Data for CMBS Loans Output: {Delinquency Rate v/s Time} X Property type
  5. 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. 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. 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. 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. 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. 10. Tool to generate Covariance Matrices Generate dataset with deal level loss (%) amount Generate Covariance Matrices by Vintage