Bond Valuation Under DiscreteTime RegimeSwitching TermStructure Models


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1 Bond Valuation Under DiscreteTime RegimeSwitching TermStructure Models Alex Badescu Department of Mathematics and Statistics University of Calgary Calgary, Alberta, Canada This is joint work with Robert J. Elliott from Haskayne School of Business, University of Calgary, Alberta, Canada and Tak Kuen Siu from Department of Mathematics and Statistics, Curtin University of Technology, Perth, Australia.
2 Outline of the presentation: Literature review Motivation Markovianregime switching for interest rate Change of measure Bond valuation formula Summary 1
3 1. Literature review Singlefactor models  early models for describing the stochastic behavior of interest rates (e.g. Merton (1973), Vasicek (1977), Cox, Ingersoll and Ross (1985))  closedform pricing formula for zerocoupon bonds are available. Imply that the prices of bonds with different maturities are perfectly correlated  not consistent with the empirical behavior of yield curves Volatility structure of the short rate is more complex than those arising from the singlefactor models (e.g. Chapman and Pearson (2001)) 2
4 Multifactor models  the evolution of the term structure of interest rates over time depends on several (macroeconomic) factors (e.g. Brennan and Schwartz (1979), Longstaff and Schwartz (1992)) Chen (1996)  threefactor model with factors including the short rate, the shortterm mean rate and the short rate volatility Duffie and Kan (1996)  a general multifactor affine term structure model, which nests many of the singlefactor and multifactor models in the existing literature 3
5 Many existing stochastic term structure models were primarily developed for pricing, hedging and risk management of some short or mediumterm interestrate products For modelling the longterm behavior of interest rates and managing the risk of longterm interestrate related products including insurance products, it is important to model the longterm behavior of interest rates For a long time period, there may be structural changes in the term structure of interest rates, which can be attributed to the structural changes of macroeconomic conditions 4
6 Regimeswitching models  capture these structural changes of macroeconomic conditions, economic fundamentals and monetary policies (e.g. Hamilton (1989), Evans and Lewis (1991), Ang and Bekaert 2002a, b) Ang and Bekaert (2002a) study the performance of regimeswitching models in fitting interest rate data from the United States Switching of regimes in interest rates matches well with business cycles in the United States and that regimeswitching models have better outofsample forecasts than models without switching regimes 5
7 It is desirable to have a term structure model, which is flexible and general enough to describe various empirical features of interest rate data and is tractable for valuation Duffie and Kan (1996)  if the price of a zero coupon bond has an exponentialaffine form, then the underlying short rate dynamics must be of either linear/gaussian form or squareroot/affine form Elliott and van der Hoek (2001)  if the continuoustime models for short rates have either a linear/gaussian form or a squareroot/affine form, the bond price has an exponentialaffine form. 6
8 Discretetime term structure models  seem more easy to implement and estimate than their continuoustime counterparts (e.g. Evans 1998, Dai and Singleton 2000, and Ang and Bekaert 2002c) Dai et al. (2007)  empirically examined a discrete time, regime switching model where the short rate follows a threefactor Gaussian model with statedependent market prices of risk Ang et al. (2007)  employed a threefactor representation for yield curves by incorporating regime changes for the inflation factor and the term structure factor, and assuming a regimeinvariant price of risk for the factors 7
9 2. Motivation There are two sources of risk one should take into account for pricing interest derivatives By analogy with continuous time, most of discretetime research studies rely on an exponential affine form for bond prices 8
10 3. Markovianregime switching for interest rate Consider a discretetime economy with time index set T = {k k = 0, 1, 2,, T } Describe the evolution of the states of the economy by a discretetime, Nstate, Markov chain X := {X k } k T on (Ω, F, P) with state space E := {e 1, e 2,, e N } a finite set of unit vectors with e i = (0,..., 1,..., 0) R N A := {a ij } i,j=1,2,,n, transition probability matrix under P a ij = P (X k+1 = e j X k = e i ) 9
11 Let F X := {Fk X} k T denote the P augmented filtration generated by the history of the chain X The semimartingale representation of the chain X (Elliott et al., 1994): X k+1 = AX k + M k+1 Here M := {M k } k=1,2,,t is an R N valued martingale increment process so that E[M k+1 F X k ] = 0. Define α := (α 1, α 2,, α N ) R N β := (β 1, β 2,, β N ) R N σ := (β 1, β 2,, β N ) R N 10
12 ε := {ε k } k=1,2,,t ε k N(0, 1) a sequence of iid random variable with ε and X are stochastically independent Let r := {r k } k T rate denote the process of short term interest F r := {Fk r} k T for the P augmented filtration generated by the interest rate process r G k = Fk r FX k  current and historical information about both the short term interest rates and the economic regimes  observed by economic agents 11
13 Let θ k := (θ 1k, θ 2k,, θ Nk ) R N, for k = 1, 2,, T θ ik is G k 1 measurable  the price of risk at time k when the state of the economy is in the i th regime at time k θ k := θ k, X k For any k = 0, 1,, T 1, under P : r k+1 = α, X k + θ k, X k σ, X k 2 + β, X k r k + σ, X k ε k+1 In practice one may assume θ k does not depend on time  θ k = θ 12
14 4. Change of measure Two sources of risk: Diffusion risk and the regimeswitching risk Incomplete market  more than one equivalent martingale measures Key question: How to select an equivalent martingale measure so that the regimeswitching risk and the diffusion risk are priced? Generate a family of probability measures by a product of two density processes, one for r and one for the Markov chain X 13
15 Introduce a density process for r Let Λ θ = {Λ θ k } k T a Gadapted processes: Λ θ 0 = 1 Λ θ k k := λ θ t t=1 ( = exp 1 2 k t=1 θ 2 t 1 σ2 t 1 k t=1 θ t 1 σ t 1 ε t ) Λ θ is a (G, P )martingale Λ θ is a discretetime version of the Girsanov s change of measures in a continuoustime setting 14
16 Introduce a density process for the Markov chain X For each t T, let t := (δ ijt ) 1 i,j N, be an (N N)matrix of G t measurable components Write δ t = t X t R N Define λ ij,t 1 := exp( t 1 e j, e i ) = exp(δij,t 1 ), i, j = 1, 2,, N Write L t 1 := (λ ij,t 1 ) i,j=1,2,,n 15
17 Let Λ δ := {Λ δ k } k T a Gadapted process: Λ δ 0 = 1 Λ δ k := k t=1 λ δ t = k t=1 ( 1 + X t 1 L t 1 M t X t 1 (L t 1 A)X t 1 ) Λ δ is a (G, P )martingale define the (G, P )adapted process Λ θ,δ := {Λ θ,δ k } k T by the product of the density processes Λ θ and Λ δ as: Λ θ,δ k = Λ θ k Λδ k Λ θ,δ is a (G, P )martingale 16
18 We can define a new probability measure Q P on G k, for each k T, by dq := Λ θ,δ dp k Gk This construction of Q extends one proposed in (Elliott et al. 2005) for option valuation when the return process is governed by a regimeswitching Gaussian process We accommodate our method to price two sources of risk Price of risk corresponding to the short term interest rate r k is embedded in the parameter θ k, while δ k represents the market price of the regime shift from X k 1 to X k 17
19 Proposition 1 (a) Under the riskneutralized probability measure Q, the short term interest rate has the following dynamics: r t+1 = α, X t + β, X t r t + σ, X t ε t+1, t = 0, 1,, T 1, ε t s are independent and identically distributed, Gaussian, random variables which are independent of X t. (b) The elements of the transition matrix C t = (c ijt ) 1 i,j N of the Markov chain X under Q are given by: c ijt = a ij exp (δ ijt ) N j=1 a ij exp (δ ijt ) 18
20 5. Bond valuation formula Let B(k, T ) the timek price of a zerocoupon bond with maturity at time T Write Z(k, n) for the discounted bond price at time k: Z(k, T ) := exp ( k t=1 r t )B(k, T ). If Q is a new probability measure under which the discounted bond price process Z(T ) := {Z(k, T )} k T is a martingale with respect to G then: [ ( ) ] B(k, T ) = E Q T exp r t G k t=k+1 19
21 Example  Continuous time Consider a continuoustime financial market with a finite time horizon T := [0, T ] Let X := X(t) t T be a continuoustime, finitestate observable Markov chain on (Ω, F, P) X(t) = X(0) + t 0 QX(s)ds + M(t) Q is the rate matrix and M(t) t T filtration generated by X is a P martingale wrt the 20
22 dr(t) = a(α(x(t)) r(t))dt + σ(x(t))dw (t) Denote by P (t, T, X, r) the price of a zerocoupon bond at time t with maturity T given the values at t are X and r P (t, T, X, r) = ea(t,t,x) B(t,T )r B(t, T ) = 1 a (1 e a(t t) ) A(t, T, X) satisfies the following regime switching O.D.E: da dt α(t)ab(t, T ) σ2 (t)b 2 (t, T ) + e A K, QX(t) = 0 where A(T, T, X) = 0 and K(t, T, X) := e A(t,T,X) 21
23 Traditional approach in discrete time Assume β, X k = β Conjecture the bond price is given by: B(k, T ) = e A 1k(X k )+A 2k r k Find recursive relationships between the coefficients from the exponentialaffine formula by imposing the martingale condition on the discounted bond price process Z(T ) Dai, Q., Singleton, K.J., Yang, W. (2007), Ang, A., Bekaert, G., Wei, M. (2007) 22
24 Following this approach for any k = 1, 2,, T we find: A 2k 1 = A 2k (β 1) α, 1 A 1k 1 (X k 1 ) = (A 2k 1)( Xk (A 2k 1) σ, X 2 k 1 + log X k c Xk 1 X k ke A 1k(X k ) with initial conditions: A 1T = A 2T = 0 ) If β is regime dependent there is no closedform relation between the above coefficients loglinear approximation might be used (Bansal and Zhou (2002)) 23
25 Our approach does not require β to be constant Let H k := FT X Fr k the enlarged information set generated by histories of the Markov chain up to the maturity time T and the short rate process up to time k The conditional price of the zerocoupon bond at time t given H k is defined by: B(k, T ) := E Q [ exp ( ) T r t t=k+1 H k ], k = 0, 1,, T 24
26 Proposition 2 The conditional bond price B(k, T ) has the following exponential affine form: B(k, T ) = exp(a 1k + A 2k r k ), k = 0, 1,, T, (1) where the stochastic processes {A 1k } k=0,1,,t and {A 2k } k=0,1,,t satisfy the following system of coupled stochastic recursions: A 1T = A 2T = 0 α, 1 A 1,k 1 = A 1k + (A 2k 1)( Xk (A 2k 1) σ, X 2 k 1 A 2,k 1 = (A 2k 1) β, X k 1, k = 1, 2,, T ) Prove this result by backward induction 25
27 B(k, T ) is a function of X k, X k+1,, X T 1 : B(k, T, X k, X k+1,, X T 1 ) Note that A 1k and A 2k are measurable wrt the tail σalgebra generated by X k, X k+1, and X T 1 Proposition 3 Given G k, the bond price has the following form: B(k, T ) = N i k,i k+1,,i T 1 =1 [ T 1 l=k c il i l+1 ] B(k, T, e ik, e ik+1,, e it 1 ) This result is obtained from taking expectation of B(k, n) conditioning on G k under Q and by enumerating transition probabilities of the Markov chain from time k to time T 1. 26
28 6. Summary Develop a discretetime, Markov, regimeswitching, affine term structure model for valuing bonds and other interestrate sensitive securities Introduce a pricing kernel by the product of two density processes for measure changes, one for the interest rate process and another for the Markov chain Control both the market risk and the longterm economic risk 27
29 Provide a simple derivation of a weighted average of exponentialaffine forms for zero coupon bond Empirical investigation of this pricing model  future work Thank you! 28
30 References Ang, A., Bekaert, G. (2002a). Short rate nonlinearities and regime switches. Journal of Economic Dynamics and Control, 26(78), Ang, A., Bekaert, G. (2002b). International asset allocation with regime shifts. Review of Financial Studies, 15(4), Ang, A., Bekaert, G. (2002c). Regime switches in interest rates. Journal of Business and Economic Statistics, 20(2),
31 Ang, A., Bekaert, G., Wei, M. (2007). The term structure of real rates and expected inflation. Working Paper, Columbia University and NBER. Bansal, R., Zhou, H. (2002). Term structure of interest rates with regime shifts. Journal of Finance, 57(4), Dai, Q., Singleton, K.J. (2000). Specification analysis of affine term structure models. Journal of Finance, Vol. LV, Dai, Q. and Singleton, K.J. (2003). Term structure dynamics in theory and reality. Review of Financial Studies, 16,
32 Dai, Q., Singleton, K.J., Yang, W. (2007). Regime shifts in a dynamic term structure model of U.S. treasury bond yields. Review of Financial Studies, 20(5), Duffie, D., Kan R. (1996). A yieldfactor model of interest rates. Mathematical Finance, 6(4), Elliott, R.J., Aggoun, L., Moore, J.B. (1994). Hidden Markov models: estimation and control. SpringerVerlag: Berlin HeidelbergNew York. Elliott, R.J., J. van der Hoek. (2001). Stochastic flows and the forward measure. Finance and Stochastics, 5,
33 Elliott, R.J., Chan, L., Siu, T.K. (2005). Option pricing and Esscher transform under regime switching. Annals of Finance, 1(4), Elliott, R.J., Wilson, C.A. (2007). The term structure of interest rates in a hidden Markov setting. In: R.S. Mamon and R.J. Elliott (Ed.), Hidden Markov Models in Finance. Springer, Elliott, R.J., Siu, T.K. (2008). On Markovmodulated exponentialaffine bond price formulae. Applied Mathematical Finance. Accepted. Vasicek, O.A. (1977). An equilibrium characterization of the term structure. Journal of Financial Economics, 5,
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