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# Hull–White model

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 Title: Hull–White model Author: World Heritage Encyclopedia Language: English Subject: Collection: Publisher: World Heritage Encyclopedia Publication Date:

### Hull–White model

In financial mathematics, the Hull–White model is a model of future interest rates. In its most generic formulation, it belongs to the class of no-arbitrage models that are able to fit today's term structure of interest rates. It is relatively straightforward to translate the mathematical description of the evolution of future interest rates onto a tree or lattice and so interest rate derivatives such as bermudan swaptions can be valued in the model.

The first Hull–White model was described by John C. Hull and Alan White in 1990. The model is still popular in the market today.

## Contents

• The model 1
• One-factor model 1.1
• Two-factor model 1.2
• Analysis of the one-factor model 2
• Bond pricing using the Hull–White model 3
• Derivative pricing 4
• Trees and lattices 5
• References 7

## The model

### One-factor model

The model is a short-rate model. In general, it has dynamics

dr(t) = \left[\theta(t) - \alpha(t) r(t)\right]\,dt + \sigma(t)\, dW(t)\,\!

There is a degree of ambiguity amongst practitioners about exactly which parameters in the model are time-dependent or what name to apply to the model in each case. The most commonly accepted hierarchy has

θ and α constant – the Vasicek model
θ has t dependence – the Hull-White model
θ and α also time-dependent – the extended Vasicek model

### Two-factor model

The two-factor Hull–White model (Hull 2006:657–658) contains an additional disturbance term whose mean reverts to zero, and is of the form:

d\,f(r(t)) = \left [\theta(t) + u - \alpha(t)\,f(r(t))\right ]dt + \sigma_1(t)\, dW_1(t)\!

where \displaystyle u has an initial value of 0 and follows the process:

du = -bu\,dt + \sigma_2\,dW_2(t)

## Analysis of the one-factor model

For the rest of this article we assume only \theta has t-dependence. Neglecting the stochastic term for a moment, notice that the change in r is negative if r is currently "large" (greater than θ(t)/α) and positive if the current value is small. That is, the stochastic process is a mean-reverting Ornstein–Uhlenbeck process.

θ is calculated from the initial yield curve describing the current term structure of interest rates. Typically α is left as a user input (for example it may be estimated from historical data). σ is determined via calibration to a set of caplets and swaptions readily tradeable in the market.

When \alpha, \theta, and \sigma are constant, Itô's lemma can be used to prove that

r(t) = e^{-\alpha t}r(0) + \frac{\theta}{\alpha} \left(1- e^{-\alpha t}\right) + \sigma e^{-\alpha t}\int_0^t e^{\alpha u}\,dW(u)\,\!

which has distribution

r(t) \sim \mathcal{N}\left(e^{-\alpha t} r(0) + \frac{\theta}{\alpha} \left(1- e^{-\alpha t}\right), \frac{\sigma^2}{2\alpha} \left(1-e^{-2\alpha t}\right)\right).

where \mathcal{N}( \mu ,\sigma^2 ) is the normal distribution with mean \mu and variance \sigma^2.

When \theta(t) is time dependent,

r(t) = e^{-\alpha t}r(0) + \int_{0}^{t}e^{\alpha(s-t)}\theta(s)ds + \sigma e^{-\alpha t}\int_0^t e^{\alpha u}\,dW(u)\,\!

which has distribution

r(t) \sim \mathcal{N}\left(e^{-\alpha t} r(0) + \int_{0}^{t}e^{\alpha(s-t)}\theta(s)ds, \frac{\sigma^2}{2\alpha} \left(1-e^{-2\alpha t}\right)\right).

## Bond pricing using the Hull–White model

It turns out that the time-S value of the T-maturity discount bond has distribution (note the affine term structure here!)

P(S,T) = A(S,T)\exp(-B(S,T)r(S))\!

where

B(S,T) = \frac{1-\exp(-\alpha(T-S))}{\alpha} \,
A(S,T) = \frac{P(0,T)}{P(0,S)}\exp\left( \, -B(S,T) \frac{\partial\log(P(0,S))}{\partial S} - \frac{\sigma^2(\exp(-\alpha T)-\exp(-\alpha S))^2(\exp(2\alpha S)-1)}{4\alpha^3}\right) \,

Note that their terminal distribution for P(S,T) is distributed log-normally.

## Derivative pricing

By selecting as numeraire the time-S bond (which corresponds to switching to the S-forward measure), we have from the fundamental theorem of arbitrage-free pricing, the value at time 0 of a derivative which has payoff at time S.

V(t) = P(t,S)\mathbb{E}_S[V(S)| \mathcal{F}(t)].\,

Here, \mathbb{E}_S is the expectation taken with respect to the forward measure. Moreover that standard arbitrage arguments show that the time T forward price F_V(t,T) for a payoff at time T given by V(T) must satisfy F_V(t,T) = V(t)/P(t,T), thus

F_V(t,T) = \mathbb{E}_T[V(T)|\mathcal{F}(t)].\,

Thus it is possible to value many derivatives V dependent solely on a single bond P(S,T) analytically when working in the Hull–White model. For example in the case of a bond put

V(S) = (K-P(S,T))^+.\,

Because P(S,T) is lognormally distributed, the general calculation used for Black-Scholes shows that

{E}_S[(K-P(S,T))^{+}] = KN(-d_2) - F(t,S,T)N(d_1)\,

where

d_1 = \frac{\log(F/K) + \sigma_P^2S/2}{\sigma_P \sqrt{S}}\,

and

d_2 = d_1 - \sigma_P \sqrt{S}.\,

Thus today's value (with the P(0,S) multiplied back in) is:

P(0,S)KN(-d_2) - P(0,T)N(-d_1)\,

Here σP is the standard deviation of the log-normal distribution for P(S,T). A fairly substantial amount of algebra shows that it is related to the original parameters via

\sqrt{S}\sigma_P =\frac{\sigma}{\alpha}(1-\exp(-\alpha(T-S)))\sqrt{\frac{1-\exp(-2\alpha S)}{2\alpha}}\,

Note that this expectation was done in the S-bond measure, whereas we did not specify a measure at all for the original Hull-White process. This does not matter — the volatility is all that matters and is measure-independent.

Because interest rate caps/floors are equivalent to bond puts and calls respectively, the above analysis shows that caps and floors can be priced analytically in the Hull–White model. Jamshidian's trick applies to Hull-White (as today's value of a swaption in HW is a monotonic function of today's short rate). Thus knowing how to price caps is also sufficient for pricing swaptions.

The swaptions can also be priced directly as described in Henrard (2003). The direct implementation is usually more efficient.

## Trees and lattices

However, valuing vanilla instruments such as caps and swaptions is useful primarily for calibration. The real use of the model is to value somewhat more exotic derivatives such as bermudan swaptions on a lattice, or other derivatives in a multi-currency context such as Quanto Constant Maturity Swaps, as explained for example in Brigo and Mercurio (2001).