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CLEAN - Patented MBS Prepayment and Valuation Model

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CLEAN - Patented MBS Prepayment and Valuation Model Presentation Transcript

  • 1. * C oupled L attice E fficiency An alysis (PATENTED) CLEAN™* A Patented Approach to MBS Valuation
  • 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. 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. 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.
    • 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. 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.
    • 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. 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. 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. 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. OAS Implied by TBA Prices
  • 12. CLEAN™ OAS Closely Tracks Agency Debenture Spread
  • 13. CLEAN™ OAS Movements Comparable to JPM OAS Movements
  • 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. TBA Price Movement vs. Model Implied Delta Movement
  • 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.
    • 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. 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