# Gamma of interest rate derivatives

consider an interest rate derivative whose value $$V$$ depends on $$n$$ interest rates $$r_1, \dots, r_n$$. Hence $$V$$ is a function in $$n$$ variables $$V(r_1, \dots, r_n)$$. My question concerns the gamma $$\gamma$$ of this derivative with respect to parallel shifts.

Does it "always" hold that $$\gamma(r_1, \dots, r_n) = \sum_{i = 1}^n \frac{\partial^2 V}{\partial r_i^2}(r_1, \dots, r_n).$$

To clarify the notation: I define the delta $$\delta$$ of the derivative with respect to parallel shifts as $$\delta(r_1, \dots, r_n) = \lim_{h \rightarrow 0} \frac{V(r_1 + h, \dots, r_n + h) - V(r_1, \dots, r_n)}{h},$$ and $$\gamma$$ is then defined as $$\gamma(r_1, \dots, r_n) = \lim_{h \rightarrow 0} \frac{\delta(r_1 + h, \dots, r_n + h) - \delta(r_1, \dots, r_n)}{h}.$$

From a mathematical standpoint, I think the identity should not hold in general since mixed partial derivatives (i.e. $$\frac{\partial^2 V}{\partial r_i r_j}$$) should be considered as well. I also assume that $$V$$ is sufficiently smooth function such that all second partial derivatives exist and are continuous. However, literature seems to suggest that the identity holds in general. What do you guys think?

It's my understanding that indeed the cross partial derivative terms do have a contribution - so you're correct to say that what u really have to work with is a gamma matrix $$\gamma=[\frac{d^2V}{dr_idr_j}]_{i,j}.$$ In essence, the cross partial terms $$\frac{d^2}{dr_idr_j}$$ allude to the correlation between the various rates $$(r_1,...,r_n)$$. Assuming a high (c. 90%) correlation these terms generally can be ignored. For this reason the gamma as computed by your stated formula is a kind of 'local' gamma as opposed to a 'global' gamma given by the entire matrix. For calculating this 'global' gamma, banks generally use some form of principal components analysis (or some other linear transformation) as it's a less computationally expensive way of capturing the majority of the variance in rate sensitivity without having to compute the whole matrix.
• could you also look at / use total dollar gamma $\sum_{i,j} V_{ij} dr_i dr_j$, where $dr_i dr_j = \rho_{ij} dt$ defines the covariance structure, instead of PCA? (Subscripts of $V$ denote partial derivatives.) Jan 26 '21 at 18:48
• Thank you for the answer. One thing is not clear to me. You talk about high correlation between the r_i. Of course they are modeled as random variables in the background, but for this particular problem the r_i are just inputs to a function. So the way I understand it, the stochastic correlation between r_i and r_j should not have any impact on $\gamma$. Jan 27 '21 at 7:48