# Variance of Multi-Dimensional OU process

I'm trying to implement this model shown here:

http://www.sciencedirect.com/science/article/pii/S0304407611000388

As part of the modelling process I have to calculate the unconditional variance of X see page 10).

$\sigma_R^2=\int_u^t \exp^{-A s}\Sigma \Sigma^T \exp^{-A^T s}ds$

They say they use a closed form result from here

see the eqn directly below eqn (3.10) on p5 sadly I can not understand this result much less transform it in to the DNS/AFDNS framework

However the solution is presented here in the DNS/AFNS fraemwork

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1974033

as

$\int_u^t \exp^{-A s}\Sigma \Sigma^T \exp^{-A^T s}ds=\Lambda \Gamma \Lambda^{-1}$

where the (i,j)th element of $\Gamma$ is $=\frac{\sigma_{ij}}{\lambda_i+ \lambda_j}(1-\exp^{-(\lambda_i + \lambda_j)\delta T})$ where $\sigma_{i,j}$ is the element (i,j) of the covariance matrix ($\Sigma \Sigma^T$) assumed constant, and $\Lambda$ is the eigenvector of $\kappa(s_t):=A$.

Unfortunately the solution I found does not say what the $\lambda$ are. I assume they must be the eigenvalues of A?

Q1.) Can someone please confirm my hunch about the $\lambda$ being the eigenvalues

Q2.) Can someone either show me or point me to a reference where I can see how this derivation is done.

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I cant get to the Nelson Siegel paper. Can you summarize or post it? – user12348 May 24 '14 at 16:10
Here is an earlier version of the paper: google.co.uk/… – Bazman May 24 '14 at 18:29
In this version they simply calculate the variance numerically using the equation in the footnote on p13. Hoever generally their notation is better because they use \Gamma for the market price of diffusing risk rather than \lambda. In the final paper they say that the use the closed form solution in Fisher and Gilles – Bazman May 24 '14 at 18:31
to calculate the variance. – Bazman May 24 '14 at 18:35

Appendix in p37 of ssrn link says $\lambda$ is market price of diffusion risk. However, in the DNS model the $\lambda$ is eigenvalues of $\kappa$, which then part of covariance matrix elements as you described. See section 5.2 of the ssrn reference for an example. Actually you can the data from Bloomberg for the period and be able to validate their results.
commenting here too, in case you do not get the edit. -- From page 18, the LHS is covariance matrix. $\Gamma$ is eigenvalues $\Lambda$ is eigenvector matrix, such that $\lambda_i$ and $\lambda_j$ are parts of $\Lambda$ that define the transition matrix which is the speed $\kappa$ which shares the $\lambda_{i \space or\space j}$ for different market regimes, and still $\lambda$ is mpor while i,j are the regime states. P16 says "we can solve (39) explicitly;" this explicit solution is resulting in eigenvectors as part of eigenvalues causing the confusion. – user12348 May 24 '14 at 23:04
Thanks for sticking with this I'm starting to come round to your way of thinking but not 100% sure.I think you are saying that each $Lambda$ is the eigenvector relating to $\kappa(s_t)$ as stated in the paper. So assuming we can take out the variance covariance matrix we have $\Lambda e^{-lambda_i}\Lambda^{-1}*\Lambda e^{-lambda_j}\Lambda^{-1}$ This gives $\Lambda e^{-\lambda_i- \lambda_j} \Lambda_{-1}$ which is of the right form but here lambda is simply the eigenvalues of $\kappa(s_t)$ not the market prices of risk. – Bazman May 25 '14 at 11:59
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