Puttable Bond
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A puttable bond is a bond in which the investor has the right to sell the bond back to the issuer at specified times for a specified price.
1. Puttable Bond Introduction |
At each puttable date prior to the bond maturity, the investor may get the investment money back by selling the
bond back to the issuer. The underlying bonds can be fixed rate bonds or
floating rate bonds. A puttable bond can therefore be considered a vanilla underlying
bond with an embedded Bermudan style option. Puttable bonds
protect investors. Therefore, a puttable bond normally pays investors a lower coupon than a non-puttable
bond.
Although a puttable bond is a higher cost to the investor and an uncertainty to the issuer comparing to a regular
bond, it is actually quite attractive to both issuers and investors. For investors, puttable
bonds allow them to reduce interest costs at a future date should rate increase. For issuers, puttable bonds allow
them to pay a lower interest rate of return until the bonds are sold back. If interest rates
have increased since the issuer first issues the bond, the investor is like to call its
current bond and reinvest it at a higher coupon. This presentation gives an overview of
puttable bond and valuation model.
2. Puttable Bond Payoffs |
Assume that the maturity of a fixed rate puttable bond is T. there are n put dates denoted as .
The payoff of a puttable bond at maturity can be expressed as
The payoff of the puttable bond at any call date T_i can be represented as
3. Valuation Model Selection Criteria |
Given the complexity of puttable bond valuation, there is no closed form solution. Therefore, we need to select an interest rate term structure model and a numeric solution to price Bermudan swaptions.
Popular IR term structure models in the market are Hull-White, Linear Gaussian Model (LGM), Quadratic Gaussian Model (QGM), Heath Jarrow Morton (HJM), Libor Market Model (LMM). HJM and LMM are too complex while Hull-White is inaccurate for computing sensitivities. Therefore, we choose either LGM or QGM.
After selecting a term structure model, we need to choose a numeric approach to approximate the underlying stochastic process of the model.Commonly used numeric approaches are tree, partial differential equation (PDE), lattice, and Monte Carlo simulation. Tree and Monte Carlo are notorious for inaccuracy in sensitivity calculation. Therefore, we choose either PDE or lattice. Our final decision is to use LGM plus lattice.
4. LGM Model |
5. LGM Assumptions and Calibration |
The LGM model is mathematically equivalent to the Hull-White model but offers significant improvements in
calibration stability and accuracy. It is also more accurate and stable in sensitivity calculation. The state variable
is normally distributed under the appropriate measure. The LGM model has only one stochastic driver (one-factor),
thus changes in rates are perfected correlated.
At time t, X(0)=0 and H(0)=0. Thus Z(0,0;T)=D(T). In other words, the LGM automatically fits today’s discount curve or yield curve. To calibrate swaption implied volatilities, first select a group of market swaptions and then solve parameters by minimizing the relative error between the market swaption prices and the LGM model swaption prices.
6. Valuation Practical Guide |
7. Related Topics |