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Applying Itô’s Lemma to an HJM Bond Price Process

Article Quant Q&A · Author: none

Summary

This note works through the stochastic differential of an exponential process built from an HJM forward-rate expression. It first differentiates the exponent, accounting for the time-dependent Brownian coefficient and the moving boundary of a nested integral. The time derivative of the Brownian integral terms cancels, while Leibniz’s rule leaves the integral of the drift over the remaining maturity range.

It then applies Itô’s lemma to the exponent and to its negative exponential. The resulting bond-price differential contains a Brownian term proportional to the remaining maturity and volatility, a drift term from the integrated HJM drift, and the quadratic-variation correction. The derivation assumes a standard Brownian motion and usual regularity conditions; it is a worked calculation rather than a broader discussion of model calibration or the restrictions on HJM drift.

Key ideas

  • Differentiate the time-dependent exponent with Itô’s lemma while treating its Brownian argument as the stochastic variable.
  • The time derivative of the Brownian integral terms cancels in the exponent calculation.
  • Leibniz’s rule for the moving upper integration boundary produces the integrated drift term.
  • The exponential transformation contributes a quadratic-variation correction to the price differential.
  • The derivation relies on Brownian motion and suitable regularity assumptions for the integrals.

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# Baxter & Rennie HJM: differentiating Ito integral


# Baxter & Rennie HJM: differentiating Ito integral












From Baxter and Rennie, page 138: $$f(t,T)=\sigma W_t+f(0,T)+\int_0^t\alpha(s,T)ds$$ $$Z_t=\exp-\bigg(\sigma(T-t)W_t+\sigma\int_0^tW_sds+\int_0^Tf(0,u)du+\int_0^t\int_s^T\alpha(s,u)ds\bigg)$$ $$dZ_t=Z_t\bigg(-\sigma(T-t)dW_t-\bigg(\int_t^T\alpha(t,u)du\bigg)dt+\frac{1}{2}\sigma^2 (T-t)^2dt\bigg)$$

How would Ito's Lemma be applied here?

I have tried: $$Z_t=\exp-\bigg(\sigma(T-t)W_t+\sigma\int_0^tW_sds+\int_0^Tf(0,u)du+\int_0^t\int_s^T\alpha(s,u)ds\bigg)=e^{-X_t}$$ $$X_t=\sigma(T-t)W_t+\sigma\int_0^tW_sds+\int_0^Tf(0,u)du+\int_0^t\int_s^T\alpha(s,u)ds$$ \begin{align} dX_t &=\sigma(T-t)W_t-\sigma W_tdt+\sigma(W_tdt-W_0d0)+f(0,T)dT-f(0,0)d0+\bigg(\int_t^T\alpha(t,u)du\bigg)dt-\bigg(\int_t^T\alpha(0,u)du\bigg)d0\\ &=\sigma(T-t)W_t+f(0,T)dT+\bigg(\int_t^T\alpha(t,u)du\bigg)dt \end{align} \begin{align} dZ_t&=-Z_tdX_t+\frac{1}{2}Z_t(dX_t)^2\\ &=Z_t\bigg(-\sigma(T-t)W_t-f(0,T)dT-\bigg(\int_t^T\alpha(t,u)du\bigg)dt+\frac{1}{2}\sigma^2(T-t)^2dt\bigg) \end{align}

A few concerns are that I've written $d0$ and that I have $f(0,T)dT$ remaining. I do think that I've applied Ito's Lemma correctly, the issue is with $dX_t$.

Any help is appreciated.

## Answer by Quantuple (score 7, accepted)

https://quant.stackexchange.com/a/30632

Let $$Z_t = \exp(-X_t)$$ with $$X_t = \sigma(T-t)W_t+\sigma\int_0^tW_sds+\int_0^Tf(0,u)du+\int_0^t\int_s^T\alpha(s,u)du ds $$ and $W_t$ a standard Brownian motion, along with the usual assumptions.

We can write $X_t=f(t,W_t)$ and apply Itô's lemma to get: $$ dX_t = \frac{\partial f}{\partial t}(t,W_t) dt + \frac{\partial f}{\partial W_t} (t,W_t) dW_t + \frac{1}{2}\frac{\partial^2 f}{\partial W_t^2}(t,W_t) d \langle W, W \rangle_t $$ \begin{align} \frac{\partial f}{\partial t}(t,W_t) &= -\sigma W_t + \sigma W_t + \int_t^T \alpha(t,u) du\\ \frac{\partial f}{\partial W_t}(t,W_t) &= \sigma(T-t)\\ \frac{\partial^2 f}{\partial W_t^2}(t,W_t) &= 0\\ \end{align} where we have used Leibniz integral rule (see here) to express the time derivatives of integral terms, notably the following: \begin{align} \partial_t \int_0^t \underbrace{\int_s^T \alpha(s,u) du}_{\tilde{\alpha}(s,T)} ds &= \partial_t \int_0^t \tilde{\alpha}(s,T) ds \\ &= \int_0^t \underbrace{\partial_t \tilde{\alpha}(s,T)}_{=0} ds + \underbrace{\partial_t(t)}_{=1} \tilde{\alpha}(t,T) - \underbrace{\partial_t(0)}_{0} \tilde{\alpha}(0,T) \\ &= \tilde{\alpha}(t,T) \\ &= \int_t^T \alpha(s,u) du \end{align} Wrapping up, yields the following differential for the process $X_t$ $$ dX_t = \left(\int_t^T \alpha(t,u) du\right) dt + \sigma(T-t) dW_t$$ from which one can deduce $$ d\langle X, X\rangle_t = \sigma^2(T-t)^2 dt $$ and finally, applying Itô's lemma to the continuous semi martingale $Z_t = \tilde{f}(t,X_t) = \exp(-X_t)$ \begin{align} dZ_t &= - Z_t dX_t + \frac{1}{2} Z_t d\langle X, X \rangle_t \\ &= Z_t \left( \left(\frac{1}{2} \sigma^2(T-t)^2 - \int_t^T \alpha(t,u) du \right)dt - \sigma(T-t) dW_t \right) \end{align}

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.