Swap Curve construction using Global Optimization

In my previous post, I discussed option Implied Volatility and Binomial Model. In this post, we switch gears and discuss Swap Curve construction. Building a swap curve is so fundamental to interest rate derivatives pricing that it is probably one of most closely guarded proprietary information on the trading desk.

Pricing an interest rate swap involves bootstrapping a blended curve of different instruments based on their maturities and market liquidity. Usually cash deposits, Eurodollar futures are used at the short end and market swap rates are used at the long end. At the long end, however, we have only a subset of market swap rates available and bootstrapping requires all the missing rates to be interpolated from known rates. This makes interpolation methodology a critical part of curve building. Also, since forward rates are a gradient of the discount rates, any misalignment in the latter is magnified in the former.

There is an alternative approach to bootstrapping called global optimization approach, where the short end of the curve is bootstrapped as usual but at the longer end we “guess” the forward rates, compute the par rates of the market swaps and minimize the error between the actual par rates and the computed par rates. We also add a smoothness constraint to the minimization procedure so that overall gradient of the curve is minimized. This approach is illustrated in the excellent book Swaps and Other derivatives 2nd Ed by Richard Flavell

I will use QuantLib to generate swap schedules and to deal with business day conventions. QuantLib can of course generate a fully built swap curve but I will use Scipy’s optimize package for curve building. My objective was to match Spreadsheet 3.9 “Building a blended curve” from the above book. Unfortunately, the spreadsheet does not actually show the equations for Excel’s Solver used for optimization but shows the final result, so that leaves considerable ambiguity in understanding which I hope I will be able to clear.

Note on QuantLib and Python

There are numerous resources online on how to build QuantLib from sources and then build the Python extensions, I would like to point you to the precompiled package for QuantLib-Python maintained by Christoph Gohlke. If you are on windows, you can just install the whl package and get started.

First some common formulae we will be using:

$$Discount Factor : DF_t = \frac{1}{(1 + r_t * d_t)}$$ where $d_t$ is year fraction and $r_t$ is annual rate

$$Forward Rate : F_{t/T} = \frac{[(DF_t/DF_T)- 1]}{(T-t)}$$ where $DF_t$ is disocunt factor to t and $DF_T$ is disocunt factor to T (both from today)

$$Par  Rate  of  Swap: \frac{(1-D_T)}{\sum_{n=1}^n(\Delta_n * D_n)}$$ where $D_T$ is maturity date discount factor, $\Delta_n$ are the time fractions between 2 reset dates and $D_n$ are the various reset date discount factors.

gtol termination condition is satisfied.
Function evaluations: 20, initial cost: 1.7812e-03, final cost 1.5585e-09, first-order optimality 1.04e-08.
### Scipy optimization took:2.5262434 seconds

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