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Hull–White Theta Depends on Discount Curve Interpolation

Article Quant Q&A · Author: user59093

Summary

The document explains how the time-dependent drift parameter, theta, in the Hull–White short-rate model is obtained from the market discount curve. The formula presented requires the instantaneous forward rate and its maturity derivative, which in turn depend on derivatives of the discount factor. With discount factors available only at discrete maturities, estimating these quantities requires constructing values between the supplied curve points.

The response emphasizes that this interpolation choice is a practical modeling decision rather than something uniquely determined by the model. Linear interpolation makes the curve simple between input points, but its second derivative is zero within segments and problematic at the knots, as the question observes. Higher-order interpolation can use several neighboring points, so changing one market input may affect theta over intervals beyond its immediate location. The document does not recommend one interpolation scheme; it highlights the tradeoff between smooth derivatives and the spread of input sensitivity, which matters when building a finite-difference implementation.

Key ideas

  • Hull–White theta is calculated from the market instantaneous forward rate and its maturity derivative.
  • Discrete discount factors require an interpolation method to estimate the derivatives used in theta.
  • Linear interpolation can produce unsuitable second derivatives for this calculation.
  • Higher-order interpolation may spread the effect of one curve input across remote intervals.
  • The appropriate interpolation method is a practical modeling judgment rather than a unique model result.

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Full text
# How do I calculate Hull White's Theta from the discount curve?


# How do I calculate Hull White's Theta from the discount curve?












### The Question

I'm currently implementing the a finite difference method for the Hull-White model, shown below:

$$\mathrm{d}r(t)=\lambda[\theta(t) − r(t)]\mathrm{d}t + \sigma\mathrm{d}W(t)\tag{1}$$

This requires that I calculate $\theta(t)$ at each point in time, $t$.

I assume I am given a discount curve, an example of which is given below:

| Time $t$ in years | Discount Factor |
| 0 | 1 |
| 0.003 | 0.9998843 |
| 0.083 | 0.9968031 |
| 0.167 | 0.9935687 |
| ... | ... |
| 0.917 | 0.9629143 |
| 1 | 0.9599047 |
| 2 | 0.9200919 |
| ... | ... |
| 30 | 0.2699292 |

How do I calculate $\theta$ from such a chart?

### My Attempt

On page 73 of Interest Rate Models — Theory and Practice by Brigo and Mercurio, we are given a formula for $\theta(t)$, given below:

$$ \theta(t) = \frac{1}{\lambda} \frac{\partial f^{M}(0, T)}{\partial T}\bigg\rvert_{T = t} + f^{M}(0, t) + \frac{\sigma^2}{2\lambda^2}\Big(1 - e^{-2\lambda t} \Big) \text{,}\tag{2}$$

where $f^{M}(0, t)$ is the market instantaneous forward rate at time $0$ for the maturity $t$. This can be calculated by

$$f^{M}(0, T) = -\frac{\partial P^M(0, T)}{\partial T}\text{,}\tag{3}$$

where $P^M(0, T)$ is the market discount factor for the maturity $T$.

Suppose we wanted to calculate $\theta(t)$. Then we just need to calculate

$$\frac{\partial P^M(0, T)}{\partial T}\tag{4}$$ and $$\frac{\partial^2 P^M(0, T)}{\partial T^2}\tag{5}$$

at $t$ using Taylor series discretizations and plug the formulas into (2).

Choose an $\epsilon$ such as $\epsilon = .000001$. We can try to calculate (5) at $t$ using the three points, $P^M(0, t-\epsilon)$, $P^M(0, t)$, and $P^M(0, t+\epsilon)$.

Well, now our answer boils down to how we estimate $P^M(0, t \pm \epsilon)$, which we estimate using (4) and (5). If we assume that $P$ is piecewise linear, for example, then (5) will always be $0$, except at times $t$ appearing in the chart, where it will blow up.

A different question might be "How do we interpolate $P^M$ for the purpose of calculating $\theta$?"

## Answer by Kurt G. (score 1)

https://quant.stackexchange.com/a/67840

In practical situations you will never know $P^M(0,t\pm\epsilon)$ for a continuum of $t$ and $\epsilon\,.$ In other words, $\theta$ will practically always depend on an interpolation method between the $t$-points in your chart. There is no model that will tell you what the right interpolation method should be. Many different ones can be used in practice. Interpolation methods of order higher than linear typically use not only adjacent points $t_{i-1}$ and $t_i$ to interpolate inbetween but also $t_{i-2}$ and $t_{i+1}$ and perhaps more. This will have the effect that changing, say, the input $P^M(0,t_{i+1})$ affects your $\theta$ in a 'remote' interval $[t_{i-2},t_{i-1}]\,.$ It is a matter of judgment if such spillover effects of interest rate risk are desired.

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

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