I'm trying to include interest rate derivatives in some Value at Risk calculations and am having trouble getting trustworthy values. My current approach is to look at the appropriate risk factor for the interest rate derivative, so for example I look at the 10-year treasury yield time series when handling a US treasury bond, and find the volatility of that yield. I then simulate the evolution of the risk factor 1 day forward in time and revalue the bond, I do this several times to obtain final prices of the bond and then find the value at risk form those final prices, just as I would with an equity derivative. Simple.
So my problems are how do I find the volatility of the yield, and what stochastic process can I use to simulate its evolution? Currently I am taking the differences (not return) in the yield each day, so $(r_2 - r_1), (r_3 - r_2)$ etc. and finding the sample standard deviation of that series to find my volatility. I then simulate the process with a Brownian (not geometric) Motion assuming zero drift. I do this based on short factor models like the Vasicek model seeming to be based on the absolute interest rate changes being normally distributed rather than the relative change. Obviously I make a simplifying assumption in assuming zere drift. Is this methodology correct? From my results I obtain far higher volatility and risk measure than is reasonable.