ACTSC 445: Asset-Liability Management

Department of Statistics and Actuarial Science, University of Waterloo

Unit 8 (Part I) – Discrete-Time Interest Rate Models

References (recommended readings): Chap. 7 of Financial Economics, chapter 37 of Fabozzi.

Introduction

In this unit, we will discuss term structure models, i.e., models for the evolution of the term structure

of interest rates. Such models can be used to price ﬁxed income securities (such as callable bonds),

and interest-rate derivatives (such as interest caps and ﬂoors).

Most of the models we will look at in Part I are discrete-time,single-factor,no-arbitrage models...

What does it mean?

•discrete-time: rates change at each period (e.g., 6 months, one year), rather than continuously

(e.g., Vasicek model dr =a(b−r)dt +σdZt).

•single-factor: model only has one source of randomness (e.g., short rates), by contrast with

multi-factor models, where e.g., we would model short rate + another asset

•no-arbitrage: model prevents arbitrage opportunities. Alternative is equilibrium model, in which

economic agents determine, through their behavior/preferences, equilibrium prices (e.g., Cox-

Ingersoll-Ross model)

When dealing with discrete-time interest rate models, sometimes we move forward in time, sometimes

we move backward:

•To use these models for pricing, one approach that we’ll see is based on backward induction.

•To calibrate these models (which in our case means ﬁnd parameters from data so that there is

no arbitrage), we’ll use forward induction.

The plan for Part I of this unit is as follows:

•First, we’ll look at a simple generic model and see how we can use it to price bonds.

•Second, we’ll go over interest-rate derivatives, and explain how to price them.

•An important tool both for pricing and calibrating is the use of Arrow-Debreu securities, which

we’ll discuss next.

•We’ll then look more closely at models and how we can calibrate them.

•Finally, we’ll discuss how to price embedded options in bonds, and use this to revisit the notion

of eﬀective duration/convexity.

1

A Generic Binomial Model

Let us ﬁrst introduce some notation:

T= number of time periods.

it= short rate at time t(random variable), t= 0, . . . , T −1.

i(t, n) = nth possible value that itcan take, n= 0, . . . , Nt.

In other words, we will be modeling the process i0, . . . , iT−1by assuming that the state space for each

short rate itis of the form {i(t, 0), . . . , i(t, Nt)}.

Abinomial model for the short-rate process {i0, . . . , iT−1}means that we are making the following

assumptions:

•i0is ﬁxed to some value i(0,0).

•For each time t≥0, it+1 can only take two possible values, which depend on the value i(t, n)

taken by the previous short rate: with probability q(t, n), it will take the value i(t+ 1, n + 1),

and with probability 1 −q(t, n), it will take the value i(t+ 1, n).

Figure 1 illustrates how the process evolves from time tto time t+ 1.

i(t,n)

time time t t+1

i (t+1,n+1)

i (t+1,n)

1−q(t,n)

q(t,n)

Figure 1: Binomial model: one step

Hence if we start from time 0, the short rate process will proceed along one path of a binomial interest

rate lattice as shown on Figure 2:

A few remarks are in order:

•The probabilities q(t, n) in our model are conditional probabilities, i.e.,

q(t, n) = P(it+1 =i(t+ 1, n + 1)|it=i(t, n))

is associated with an up-move from state i(t, n), and

1−q(t, n) = P(it+1 =i(t+ 1, n)|it=i(t, n))

is associated with a down-move from state i(t, n).

•The binomial interest rate lattice (or tree) is recombining. That is, at any given node in the

lattice, an up move followed by a down move (the latter being represented by a straight line on

the graph) will reach the same node as a down move followed by an up move. Hence the number

of possible states at each time tis equal to t+ 1. A non-recombining tree would double the

number of nodes at each step, ending with 2Tnodes at time T.

2

0 1 2 3

i(0,0) i(1,0) i(2,0)

i(1,1) i(2,1)

i(3,3)

i(3,0)

i(3,1)

i(3,2)i(2,2)

1−q(0,0)

q(1,0)

q(0,0)

1−q(1,0)

q(1,1)

1−q(1,1)

q(2,2)

q(2,1)

q(2,0)

1−q(2,0)

1−q(2,2)

1−q(2,1)

i(T,0)

i(T,1)

i(T,T−1)

i(T,T)

i(T,T−2)

q(T,T−2)

q(T,T−1)

1−q(T,T−1)

1−q(T,T−2)

1−q(T,0)

q(T,0)

Figure 2: Binomial interest rate lattice

•The behavior of itonly depends on the previous value taken by it−1. In other words, the short-rate

process {it, t = 0, . . . , T }is Markovian, i.e.,

P(it=i(t, n)|it−1, it−2, . . . , i0) = P(it=i(t, n)|it−1) = 1−q(t−1, n) if it−1=i(t−1, n)

q(t−1, n −1) if it−1=i(t−1, n −1).

Figure 3 illustrates the idea.

i (t,n)

i(t−1,n−1)

i(t−1,n) 1−q(t−1,n)

q(t−1,n−1)

Figure 3: How to get to a state i(t, n)

•How many possible paths from time 0 to time Tare there?

Example: Consider the binomial interest-rate lattice shown on Figure 4, where we assume for sim-

plicity that q(t, n)=1/2 for n= 0, . . . , t, t = 0, . . . , 4. In what follows we’ll illustrate how to perform

various tasks with interest-rate models using this particular example.

3

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###### Document Summary

Department of statistics and actuarial science, university of waterloo. Unit 8 (part i) discrete-time interest rate models. 7 of financial economics, chapter 37 of fabozzi. In this unit, we will discuss term structure models, i. e. , models for the evolution of the term structure of interest rates. Such models can be used to price xed income securities (such as callable bonds), and interest-rate derivatives (such as interest caps and oors). Most of the models we will look at in part i are discrete-time, single-factor, no-arbitrage models Alternative is equilibrium model, in which economic agents determine, through their behavior/preferences, equilibrium prices (e. g. , cox- = short rate at time t (random variable), t = 0, . , t 1. it i(t, n) = nth possible value that it can take, n = 0, . In other words, we will be modeling the process i0, .

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