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Deriving a Weighted Integral from the G2++ Ornstein–Uhlenbeck Solution

Article Quant Q&A · Author: user13232877

Summary

The document explains how to expand the time integral of a mean-reverting factor in the G2++ short-rate model. It uses the solution of the Ornstein–Uhlenbeck stochastic differential equation between the current time and an intermediate time. That solution expresses the factor as its current value multiplied by an exponential decay term, plus a stochastic integral of Brownian increments weighted by exponential decay.

Substituting this expression into the weighted integral separates it into a deterministic integral involving the current factor and a nested stochastic integral. The exponential term therefore comes from the factor’s conditional solution, while the Brownian term’s upper integration limit is the intermediate time. The explanation provides the derivation structure but does not evaluate the resulting integrals or derive the full variance formula. Its focus is the one-factor step, which can be applied to either mean-reverting factor in G2++ with the corresponding parameters.

Key ideas

  • The mean-reverting factor can be written in terms of its value at the starting time and Brownian increments thereafter.
  • The exponential decay factor arises from solving the Ornstein–Uhlenbeck stochastic differential equation.
  • Substituting that solution splits the weighted integral into a current-state term and a stochastic term.
  • The stochastic integral inside the time integral runs only up to the intermediate time.

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Full text
# Deriving the variance of G2++ Model


# Deriving the variance of G2++ Model












I'm studying G2++ Model in Brigo(2007)'s book.

The model constructed as follows,

$$ r(t) = x(t) + y(t) + φ(t), \quad r(0) = r_0\\ $$ with the dynamics of $dx(t)$ and $dy(t)$ described by: \begin{align} dx(t) &= -ax(t)dt + σdW_1(t), \quad x(0) = 0,\\ dy(t) &= -by(t)dt + ηdW_2(t),\quad y(0) = 0,\\ \end{align} and $dW_1(t)\cdot dW_2(t) = ρdt$.

Problem: When we calculate the variance, There is something that I cannot derive, which is described below:

$$∫^T_t(T-u)dx(u) = -a∫^T_t(T-u)x(u)du + σ∫^T_t(T-u)dW_1(u)$$ Then, $$∫^T_t(T-u)x(u)du = x(t)∫^T_t(T-u)e^{-a(u-t)}du + σ∫^T_t(T-u)∫^T_te^{-a(u-s)}dW_1(s)du.$$

I tried to derive the above integral, $\int_t^T (T-u) x(u) \: du$, but failed.

I want to know how to derive this equation. Especially, where in the world $e^{-a(u-t)}$ and $σ∫^T_t(T-u)∫^T_te^{-a(u-s)}dW_1(s)du$ came from, from the above equation?

##### Below picture is just the raw source of my text:

## Answer by Pleb (score 1, accepted)

https://quant.stackexchange.com/a/71111

### It comes from a direct application of the solution to $dx_t$

The solution for this SDE has already been derived by Gordon in this post where the only difference (in your specified SDE) is a sign-change in the drift-term. From the answer in the linked post, we observe that the solution to $dx_t$ in your case is given by:

$$ x_T = x_t e^{-a(T-t)} + \sigma\int_{t}^T e^{-a(T-s)} \: dW_s. $$

The derivations specified in Gordons answer involve the integrating factor method which is also used to derive the solution for the well-known Vasicek one-factor short-rate model.

Let $0\leq t < u < T$. Now we can compute the integral in question:

\begin{align} \int_t^T (T-u) \cdot x_u \: du &= \int_t^T (T-u) \cdot \left[x_t e^{-a(u-t)} + \sigma\int_{t}^u e^{-a(u-s)} \: dW_s \right] \: du \\ &=x_t \int_t^T (T-u) e^{-a(u-t)} \: du + \sigma\int_t^T (T-u)\int_{t}^u e^{-a(u-s)} \: dW_s \: du. \end{align}

In conclusion, $e^{-a(u-t)}$ and $\sigma\int_t^T (T-u)\int_{t}^u e^{-a(u-s)} \: dW_s \: du$ comes from inserting the solution of $dx_t$ in the above integral. I hope this provide some insight.

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