CLEAN - Patented MBS Prepayment and Valuation Model

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CLEAN - Patented MBS Prepayment and Valuation Model

  1. 1. *Coupled Lattice Efficiency Analysis (PATENTED) CLEAN™* A Patented Approach to MBS Valuation
  2. 2. 2 Heard It Through The Grapevine "The actual sensitivity of MSRs to implied volatility is complex and somewhat controversial” Ben Golub in "Mark-to-Market Methodology, Mortgage Servicing Rights, and Hedging Effectiveness“ “The model we use doesn’t even get the sign right for volatility hedging of MSRs” A/L management advisor “The price response to skew adjustment seems exaggerated” Hedge fund manager Why do intuition and model disagree when it comes to volatility?
  3. 3. 3 Observations Modeling prepayments is only a means to an end The goal is proper valuation and risk measurement A mortgage is a callable amortizing bond Prepayment models should be consistent with callable bond models Bonds (mortgages) are refunded (refinanced) when the call option is worth more dead than alive Therefore bond and mortgage models should respond similarly to interest rate levels and volatility changes
  4. 4. 4 Dynamic Versus Static Variables In an MBS model Interest rate driven prepayments Dynamically hedged Modeled using a stochastic interest rate process Other prepayments, such as turnover and defaults Either not hedged or statically hedged Modeled statically in CLEAN
  5. 5. 5 A financial engineer homeowner uses an option valuation model Refinances optimally Others refinance too early or too late Early refinancers are called “leapers” Rarely occurs Late refinancers are called “laggards” AKA analysis shows that the 50 bps rule of thumb is sensible Most homeowners refinance near-optimally! Optimum Option Exercise Provides Benchmark for Suboptimal Behavior
  6. 6. 6 MBS Valuation Using CLEAN™ Two separate yield curves are required One calibrated to mortgage rates Other calibrated to MBS yields Modeled using coupled lattice Mortgage rates used to determine refis Using notion of call efficiency MBS rates used for discounting MBS cash flows
  7. 7. 7 Benchmark yield curve and volatility USD swap curve and appropriate swaption vol Prepayment parameters Laggard distribution Turnover speed vector Default/buyout speed and recovery percentage vectors Refinancing cost Fixed percentage of original principal Homeowner credit spread Analogous to corporate credit spread MBS price/OAS For EOD pricing, use OAS calibrated to TBA prices CLEAN™ Model Input Parameters
  8. 8. 8 Calibration of CLEAN™: Straightforward and Intuitive Rarely adjusted Laggard distribution Turnover speed Default recovery percentage Refinancing cost Occasionally adjusted Homeowner credit spread Default/buyout speed
  9. 9. 9 Calibrating Homeowner Credit Spread For Agency Pools Should be consistent with prevailing mortgage rates Approximately 120 bps for current coupon pools Implies refi option premium of approximately 40 bps Higher credit spread for higher coupon collateral Implies weaker credit, ceteris paribus Calibrated to dealer consensus duration/convexity, and specified pool pay-up grids Additional factors that can be incorporated: Fannie/Freddie vs. Ginnie/FHA LTV, FICO Year of origination Loan size Percent of non-owner occupied (low refi rate, high turnover rate) Average points paid Credit migration
  10. 10. 10 Estimated Homeowner Credit Spread FNMA TBA Coupon Stack – July 23, 2010 TBA WAC (%) Homeowner Credit Spread (bps) FNCL 4 4.585 110 FNCL 4.5 4.950 110 FNCL 5 5.428 175 FNCL 5.5 5.949 240 FNCL 6 6.517 320 FNCL 6.5 6.975 400 FNCL 7 7.645 480
  11. 11. 11 OAS Implied by TBA Prices FNMA 30-yr TBA OAS of CLEAN (July 23, 2010) 0 40 80 120 4.0 4.5 5.0 5.5 6.0 6.5 MBS coupon (%) OAS(bp) 10-yr agency spread CLEAN
  12. 12. 12 CLEAN™ OAS Closely Tracks Agency Debenture Spread -50 0 50 100 150 1/1/2009 6/1/2009 10/30/2009 3/30/2010 Spreads(bps) FNCL 4.5 OAS vs. Agency Debenture Spreads (1/2/2009 - 4/13/2010) FNCL4.5 OAS 10-yragency spreads
  13. 13. 13 CLEAN™ OAS Movements Comparable to JPM OAS Movements CLEAN vs. JPM FNCL 4.5 OAS (1/2/2009 - 7/19/2010) -40 0 40 80 120 1/2/2009 7/3/2009 1/1/2010 7/2/2010 Spread(bps) CLEAN OAS (bp) JPM OAS (bp)
  14. 14. 14 TBA Duration and Convexity: CLEAN™ vs. Other Models 7/23/2010 Turnover/ default rate Homeowner credit spread TBA price OAS CLEAN JPM Dealer model BAML (new) BAML (old) FNCL 4 7% 110 101.84 23 8 25 1 -5 FNCL 4.5 9% 110 103.97 33 -2 34 -1 -11 FNCL 5 13% 175 106.19 53 -25 50 0 -33 FNCL 5.5 18% 240 107.72 67 -61 25 11 6 FNCL 6 24% 320 108.83 71 -71 29 16 24 FNCL 6.5 24% 400 109.81 97 -14 101 45 53 Duration Convexity CLEAN JPM Dealer model BAML (new) BAML (old) CLEAN JPM Dealer model BAML (new) BAML (old) FNCL 4 4.9 5.0 5.2 4.5 4.5 -1.8 -2.0 -2.8 -3.0 -3.3 FNCL 4.5 3.5 2.9 4.2 2.6 2.8 -2.9 -3.4 -3.1 -3.9 -1.9 FNCL 5 2.7 1.3 3.7 1.4 1.8 -2.7 -2.9 -2.5 -2.8 0.2 FNCL 5.5 2.1 0.5 1.8 1.1 2.5 -2.2 -0.8 -1.4 -2.0 0.2 FNCL 6 1.9 0.5 1.3 0.6 2.5 -1.8 0.3 -0.9 -1.3 0.4 FNCL 6.5 2.3 1.4 2.9 0.5 2.5 -0.8 0.1 -0.4 -1.4 0.5
  15. 15. 15 TBA Price Movement vs. Model Implied Delta Movement Actual Change in TBA Market Price vs. Sum of Implied Price Change Due to Risk Factors (1/2/2009 - 7/1/2010) -3 -2 -1 0 1 2 3 1/1/2009 4/2/2009 7/2/2009 10/1/2009 12/31/2009 4/1/2010 7/1/2010 %ofpar FNCL 4.5 Δ price Σ implied price change due to risk factors
  16. 16. 16 Why CLEAN™ Is Ideal for Trading, Hedging, and Risk Management Realistic transparent behavior Based on well established financial and economic principles Instead of mysterious mathematical formulas and parameters Consistent with valuation models for callable bonds and cancelable swaps Calibration is straightforward and intuitive Concretely defined model parameters Easier to simulate Model behavior always realistic Based on fundamental financial and economic principles Not on statistical fitting of historical behavior And ridiculously fast Criticial for simulation
  17. 17. 17 Modeling prepayments Turnover and defaults modeled using deterministic speeds Refinancings modeled using stochastic interest rate model Modeling a mortgage As a callable amortizing bond A financial engineer will refinance when the option is worth more dead than alive Others will refinance too early (never really happens) or too late (“laggards”) Modeling heterogeneous refinancing behavior Divide mortgage pool into 10 buckets according to laggard parameter Use a standard laggard distribution for a new pool Modeling seasoned pools Fastest refinancing buckets disappear first Automatically accounts for ‘burnout’ The CLEAN™ Way
  18. 18. 18 References Andrew Kalotay & Qi Fu (June 2009), A Financial Analysis of Consumer Mortgage Decisions, Mortgage Bankers Association. Andrew Kalotay & Qi Fu (May 2008), Mortgage servicing rights and interest rate volatility, Mortgage Risk. Andrew Kalotay, Deane Yang, & Frank Fabozzi (Vol. 1, 2008), Optimum refinancing: bringing professional discipline to household finance, Applied Financial Economics Letters. Andrew Kalotay, Deane Yang, & Frank Fabozzi (Vol. 3, 2007), Refunding efficiency: a generalized approach, Applied Financial Economics Letters. Andrew Kalotay, Deane Yang, & Frank Fabozzi (December 2004), An option-theoretic prepayment model for mortgages and mortgage-backed securities, International Journal of Theoretical and Applied Finance. Available from http://www.kalotay.com/research

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