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Who We Are. Knowledge Decision Securities, LLC provides the financial engineering On-Demand Services for investment banks, mortgage originators and servicers, and portfolio management (mortgage securities
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3. On Demand Services Mortgage
POD/DOD: Prepayment/Default On Demand
A portal service provides slice and dice of Agency prepayment data for MBS analytics
VOD: Valuation On Demand
A portal service provides all asset classes Monte Carlo Simulations (MCS) OAS and Scenarios valuations
SOD: SCW On Demand
A portal service for Structured Cashflow Waterfall (SCW) product issuance, analytics, and surveillance
Equity
EOD: Equity On Demand
A portal service for equity derivatives Monte Carlo Simulation valuations 3
5. Monte Carlo Workflow 5
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8. Structured Assets Valuation EngineSAVE integrates the following 5 subsystems: Three-factor LIBOR market interest rate model
Prepayment, Delinquency, Default & Loss model
Stochastic macro-econometric model
Structured Cashflow Waterfalls (SCW) model
Monte Carlo Simulations (MCS) OAS model
8 Three factor LMM key features -
Construction is arbitrage free
No yield curve calibration
Intuitive volatility and correlation calibration
Accommodate arbitrary number of factors
No need to mean-reversion parameters in LMM (no true economic meaning)
Better than Hull-White methodologies
Monte Carlo/OAS Pricing Model
Precision and speed of convergence for pricing
Based on 3x360high dimensional quasi-random sequence generator
Proprietary moment matching algorithms and controlled variable techniques to achieve faster convergence
Parallel processing of large portfolio implemented by application distributed on CPU farm in San Jose
Back-end system
Patented “Virtual Pocket Sorter” with all fields indexing we achieve
7x floating point calculation
5x integer calculation
3x character strings
Parallel modeling application on Monte Carlo simulation – an effective architecture pipeline
Flexibility and open source componentsThree factor LMM key features -
Construction is arbitrage free
No yield curve calibration
Intuitive volatility and correlation calibration
Accommodate arbitrary number of factors
No need to mean-reversion parameters in LMM (no true economic meaning)
Better than Hull-White methodologies
Monte Carlo/OAS Pricing Model
Precision and speed of convergence for pricing
Based on 3x360high dimensional quasi-random sequence generator
Proprietary moment matching algorithms and controlled variable techniques to achieve faster convergence
Parallel processing of large portfolio implemented by application distributed on CPU farm in San Jose
Back-end system
Patented “Virtual Pocket Sorter” with all fields indexing we achieve
7x floating point calculation
5x integer calculation
3x character strings
Parallel modeling application on Monte Carlo simulation – an effective architecture pipeline
Flexibility and open source components
9. Structured Assets Valuation Engine 9
10. Collateral Data ETL 10
11. Collateral Data Management 11
12. SCW Deal Structuring Collateral CF Engine
Period based (amortization, scheduled payment/coupon, calendar, fee, OPT/ARM, Strips, Interest Reserve, Tax, etc..)
Scripting Engine
Python based waterfall programming with Customizable and Modulated Script Command Call
Y/H/SEQ/ProRata/OC/Shifting-Interest
Credit Enhancement
Bond/Pool Insurance Policies
Surety Bond Guarantee
Derivatives (SWAP, Cap/Floor)
Reserve Account
Triggers Modules – DLQ, Loss
NAS/PAC/TAC
RE-REMIC
Pricing/Update/Payment Modes
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13. SCW Deal Structuring Application
Valuation On Demand
MCS_OAS
Econ Scenarios
Payment and performance surveillance & verification
Risk Management
Market Risk Hedging
MSR
REMIC (Projected) Tax
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14. 14 SCW Structuring Scripting Module SetDealParameters(('strike_rate', 5.05),
('index_name', 'LIBOR_1MO'),
('cuc_level_pct', 10),
('sen_enhance_threshold_pct', 40.20),
('stepdown_month', 37),
('oc_floor_pct', 0.50),
('oc_target_pct', 4.25),
('dlq_trigger_threashold_pct', 39.80),
('loss_trigger_threashold_pct', 1.35)
SetTrancheParameters(('A1A','A1B','A2','A3','A4','A5')
('target_paydown_pct',59.80)
)
SetTrancheParameters('A1A',
('cuc_multiplier', 2),
('coupon_spread', 0.17)
)
SetTrancheParameters('M1',
('cuc_multiplier', 1.5),
('coupon_spread', 0.30),
('target_paydown_pct',66.20)
15. Example I: GNMA 2010-054 Diagram and KDS Waterfall Programming
16. 16 Example II: FNMA 07082 Structuring Diagram
17. 17 Example III:JP MORGAN MORTGAGE TRUST 2007-CH3 Closing Date 5/15/2007
Collateral Type
Subprime Home Equity
Capital Structure:
Overcollateralization
SEN/MEZZ/JUN Y Structure
Net SWAP cover OC Deficiency, Interest Shortfall, Realized Loss, NetWAC Carryover
Cross-Collateralization
Triggers in
Enhancement Delinquency
Cumulative Loss
Sequential Trigger
OC and Subs Test
18. 18 Example IV:NEW CENTURY HEL TRUST 2006-2 Closing Date 06/29/2006
Collateral
Subprime Home Equity
Capital Structure:
Overcollateralization
SEN/JUN Sequential
Net SWAP cover OC Deficiency, Interest Shortfall, Realized Loss, NetWAC Carryover
Cross-Collateralization (on Group I & I Notes Sen)
Triggers in
Enhancement Delinquency
Cumulative Loss
Sequential Trigger
OC and Subs Test
19. Prepay, Default, Severity, Delinquency
Modeling Approach
Delinquency Transitions
Prepay/Default Competing Risks
Agency and Non-Agency Collateral:
Prime Jumbo
Alt-A
Option ARM
Subprime
HELOC
Fannie/Freddie
FHA/VA
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20. Prepay, Default, Timing of Default, Severity, Extension
Key Inputs: Property Type, LTV, DSCR, NOI, Underwriting,
MSA, Cap Rate, Refi Threshold, Call Protection, Tenant Attributes
Subsystems
APOLLO: NOI Generator, Scenario/Monte Carlo Simulation
HELIOS: Loan Level Prepay/Default Generator
Market Calibration
CMBX, TRX
Conversion from TRX to OAS
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22. Index Derivative Analytics Complete coverage in PRIMEX, ABX, CMBX, MBX/IOS/PO
Calculate Market Implied Spread(OAS) based on Economic Scenarios and 3000 paths Monte Carlo Simulation
Monte Carlo Simulation based risk measures in
Mode
Skewness (Pearson's first)
Mean
Sigma
Var
1-dVar
Risk Score
Daily and Weekly Reports based on Market Close Price 22
23. Index Derivative Analytics 23
24. Prepay/Default/Severity Overview Projects monthly prepayment, delinquency, default and loss severity rates of new (at purchase) or seasoned (portfolio) loans.
Takes into account of loan, borrower and collateral risk characteristics as well as macro economic variables on rates and home prices.
Based on a hybrid delinquency transition rate and competing risks survivorship model where the prepay & default risk parameters are estimated from historical loan-level data.
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25. Based on a proprietary highly non-linear non-parametric methodology with parameters estimated from non-agency loan-level data.
Prepay and default are jointly estimated in a competing risk framework.
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26. Model Inputs
Collateral type (e.g., alt-a, non-conforming balance, no prepay penalty).
Age, Note rate, Mortgage rates, Yield curve slope.
Home price (zip/CBSA-level if used at loan-level, otherwise state- or national-level)
Unemployment rate
Loan size, Documentation, Occupancy, Purpose, State, FICO, LTV, Channel.
Delinquency history and status (past due, bankruptcy, REO)
Negative amortization limit (recast) for option ARM
Modification type, size, and timing
Servicer
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27. Model Outputs
Prepayment and default probabilities at each time step
Delinquency rates
Loss severity
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28. Equity Valuation 28
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39. 39 Competitor I Interest Rate Models
40. 40 Competitor II Interest Rate Models
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42. KDS Interest Rate Model 42
43. Interest Rate Model Summary 43
44. Home Price Model 44
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