Monday, October 7, 2013

Chapter 3, part 3 of n: bootstrapping an interest-rate curve

Welcome back.

This post is the third in a series covering chapter 3 of my book and starting here. It's a rather large example in which I dissect the code used for bootstrapping an interest-rate curve.

A bit of news: IKB, Quaternion and d-fine are organizing a QuantLib workshop in Düsseldorf on November 13th and 14th, and they were kind enough to ask me to give the keynote on the 13th. As you can guess, it's flattering and scaring at the same time. Possibly more scaring than flattering.

The perks of this will be, first, that I get to meet in person for the first or second time a couple of people that I've worked with on the library, sometimes for years; and second, that I have the opportunity to collect a few data points about the way QuantLib is used in the industry. I'll try to share with you some of the goodies (not the beers, of course: those will stay in Düsseldorf). I'll report on this blog on what I see, and I'll ask the speakers to put their material on the QuantLib site.

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Term Structures

Example: bootstrapping an interpolated yield curve

In this section, we'll build an all-purpose yield-curve template. Building upon the classes described in the previous subsections, we'll give it the ability to interpolate in a number of ways on either discount factors, zero yields, or instantaneous forward rates. The nodes of the curve will be bootstrapped from quoted—and possibly varying—market rates.

Needless to say, this example is a fairly complex one. The class template I'll describe (PiecewiseYieldCurve) makes use of a few helper classes, as well as a few template tricks. I'll try and explain all of them as needed.

Listing 3.7: Implementation of the PiecewiseYieldCurve class template.
template <class Traits, class Interpolator,
          template <class> class Bootstrap = IterativeBootstrap>
class PiecewiseYieldCurve
    : public Traits::template curve<Interpolator>::type,
      public LazyObject {
  private:
    typedef typename Traits::template curve<Interpolator>::type base_curve;
    typedef PiecewiseYieldCurve<Traits,Interpolator,Bootstrap> this_curve;
    typedef typename Traits::helper helper;
  public:
    typedef Traits traits_type;
    typedef Interpolator interpolator_type;
    PiecewiseYieldCurve(
           Natural settlementDays,
           const Calendar& calendar,
           const std::vector<shared_ptr<helper> >& instruments,
           const DayCounter& dayCounter,
           Real accuracy = 1.0e-12,
           const Interpolator& i = Interpolator());

    // inspectors not shown

    void update();
  private:
    void performCalculations() const;
    DiscountFactor discountImpl(Time) const;
    std::vector<shared_ptr<helper> > instruments_;
    Real accuracy_;

    friend class Bootstrap<this_curve>;
    friend class BootstrapError<this_curve>;
    Bootstrap<this_curve> bootstrap_;
};
The implementation of our class template is shown in listing 3.7. The class takes three template arguments. The first two determine the choice of the underlying data and the interpolation method, respectively; our goal is to instantiate the template as, say,
    PiecewiseYieldCurve<Discount,LogLinear>
or some such combination. I'll refer to the first parameter as the bootstrap traits; the second is the interpolator already described in a previous post. The third, and seemingly ungainly, parameter specifies a class which implements the bootstrapping algorithm. The parameter has a default value (the IterativeBootstrap class, which I'll describe later) so you can happily forget about it most of the times; it is provided so that interested developers can replace the bootstrapping algorithm with another one, either provided by the library or of their own creation. If you're not familiar with its syntax, it's a template template parameter [1]. The repetition is not an error, as suspected by my spell checker as I write these lines; it means that the template parameter should, in turn, be an uninstantiated class template (in this case, one taking a single template argument) rather than a typename. (An uninstantiated template is something like std::vector, as opposed to an instantiated one like std::vector<double>. The second names a type; the first doesn't.)

Before declaring the class interface, we need another bit of template programming to determine the parent class of the curve. On the one hand, we inherit our term structure from the LazyObject class; as described here, this will enable the curve to re-bootstrap itself when needed. On the other hand, we want to inherit it from one of the interpolated curves described here; depending on the choice of underlying data (discount, zero yields, or forward rates), we must select one of the available class templates and instantiate it with the chosen interpolator class. This is done by storing the class template in the bootstrap traits. Unluckily, C++ didn't allow template typedefs until recently (template aliases were introduced in the 2011 revision of the C++ standard). Therefore, the traits define an inner class template curve which takes the interpolator as its template parameter and defines the instantiated parent class as a typedef; this can be seen in the definition of the Discount traits, partially shown in listing 3.8.

Listing 3.8: Sketch of the Discount bootstrap traits.
    struct Discount {
        template <class Interpolator>
        struct curve {
            typedef InterpolatedDiscountCurve<Interpolator> type;
        };
        typedef BootstrapHelper<YieldTermStructure> helper;

        // other static methods
    }
The described machinery allows us to finally refer to the chosen class by using the expression
    Traits::template curve<Interpolator>::type
that we can add to the list of parent classes. (For those unfamiliar with the dark corners of template syntax, the template keyword in the expression is a hint for the compiler. When reading this expression, the compiler doesn't know what Traits is and has no means to determine that Traits::curve is a class template. Adding the keyword gives it the information, required for processing the rest of the expression correctly.) For the instantiation shown above as an example, with Discount as bootstrap traits and LogLinear as interpolator, the above works out as
    Discount::curve<LogLinear>::type
which in turn corresponds—as desired—to
    InterpolatedDiscountCurve<LogLinear>
as can be seen from the definition of the Discount class.

We make a last short stop before we finally implement the curve; in order to avoid long template expressions, we define a few typedefs, namely, base_curve, this_curve, and helper. The first one refers to the parent class; the second one refers to the very class we're declaring; and the third one extracts from the bootstrap traits the type of a helper class. This class will be described later in the example; for the time being, I'll just say that it provides the quoted value of an instrument, as well as the means to evaluate such instrument on the term structure being bootstrapped. The aim of the bootstrap will be to modify the curve until the two values coincide. Finally, two other typedefs (traits_type and interpolation_type) store the two corresponding template arguments so that they can be retrieved later.

The actual interface, at last. The constructors (of which only one is shown in the listing) take the arguments required for instantiating the parent interpolated curve, as well as a vector of helpers and the target accuracy for the bootstrap. Next, a number of inspectors (such as times or data) are defined, overriding the versions in the parent class; as we'll see, this allows them to ensure that the curve is fully built before returning the required data. The public interface is completed by the update method.

The protected methods include performCalculations, needed to implement the LazyObject interface; and discountImpl, which (like the public inspectors) overrides the parent-class version. The Bootstrap class template is instantiated with the type of the curve being defined. Since it will need access to the internals of the curve, the resulting class is declared as a friend of the PiecewiseYieldCurve class; the same is done for the BootstrapError class, used in the bootstrap algorithm and described later. Finally, we store an instance of the bootstrap class, as well as the required curve accuracy and the helpers.

Listing 3.7 (continued)
template <class T, class I,
          template <class> class B>
PiecewiseYieldCurve<T,I,B>::PiecewiseYieldCurve(
           Natural settlementDays,
           const Calendar& calendar,
           const std::vector<shared_ptr<helper> >& instruments,
           const DayCounter& dayCounter,
           Real accuracy,
           const I& interpolator)
: base_curve(settlementDays, calendar, dayCounter, i),
  instruments_(instruments), accuracy_(accuracy) {
    bootstrap_.setup(this);
}

template <class T, class I,
          template <class> class B>
void PiecewiseYieldCurve<T,I,B>::update() {
    base_curve::update();
    LazyObject::update();
}

template <class T, class I,
          template <class> class B>
void PiecewiseYieldCurve<T,I,B>::performCalculations() const {
    bootstrap_.calculate();
}

template <class T, class I,
          template <class> class B>
DiscountFactor PiecewiseYieldCurve<T,I,B>::discountImpl(Time t) const {
    calculate();
    return base_curve::discountImpl(t);
}
Let's now have a look at the implementation. The constructor holds no surprises: it passes the needed arguments to the base class and stores in this class the other ones. Finally, it passes the curve itself to the stored Bootstrap instance and makes it perform some preliminary work—more on this later. The update method is needed for disambiguation; we are inheriting from two classes (LazyObject and TermStructure) which both define their implementation of the method. The compiler justly refuses to second-guess us, so we have to explicitly call both parent implementations.

The performCalculations simply delegates its work to the Bootstrap instance. Lastly, a look at the discountImpl method shows us why such method had to be overridden; before calling the parent-class implementation, it has to ensure that the data were bootstrapped by calling the calculate method. This also holds for the other overridden inspectors, all following the same pattern. Instead, there's no need to override the zeroYieldImpl and forwardImpl methods, even if the curve inherits from ZeroYieldStructure or ForwardRateStructure; if you look back at listing 3.5 here, you'll see that those methods are only ever called by discountImpl. Thus, overriding the latter is enough.

At this point, I need to describe the BootstrapHelper class template; its interface is sketched in listing 3.9. For our curve, we'll instantiate it (as you can see in the Discount traits shown earlier) as BootstrapHelper<YieldTermStructure>; for convenience, the library provides an alias to this class called RateHelper that can be used in place of the more verbose type name.

Listing 3.9: Interface of the BootstrapHelper class template.
    template <class TS>
    class BootstrapHelper : public Observer, public Observable {
      public:
        BootstrapHelper(const Handle<Quote>& quote);
        virtual ~BootstrapHelper() {}

        Real quoteError() const;
        const Handle<Quote>& quote() const;
        virtual Real impliedQuote() const = 0;

        virtual void setTermStructure(TS*);

        virtual Date latestDate() const;

        virtual void update();
      protected:
        Handle<Quote> quote_;
        TS* termStructure_;
        Date latestDate_;
    };
Each instance of this class—or rather, of derived classes; the class itself is an abstract one—will help bootstrapping a single node on the curve. The input datum for the node is the quoted value of an instrument; this is provided as a Handle to a Quote instance, since the value will change in time. For a yield curve, such instruments might be deposits or swaps, quoted as the corresponding market rates. For each kind of instrument, a derived class must be provided.

The functionality that is common to all helpers is implemented in the base class. BootstrapHelper inherit from both Observer and Observable; the double role allows it to register with the market value and notify changes to the curve, signaling the need to perform a new bootstrap. Its constructor takes a Handle<Quote> providing the input market value, stores it as a data member, and registers itself as an observer. Three methods deal with the underlying instrument value. The quote method returns the handle containing the quoted value; the abstract impliedValue method returns the value as calculated on the curve being bootstrapped; and the convenience method quoteError returns the signed difference between the two values.

Two more methods are used for setting up the bootstrap algorithm. The setTermStructure method links the helper with the curve being built. The latestDate method returns the latest date for which curve data are required in order to calculate the implied value of the market datum; such date will be used as the coordinate of the node being bootstrapped. (The latest required date does not necessarily correspond to the maturity of the instrument. For instance, if the instrument were a constant-maturity swap, the curve should extend a few years beyond the swap maturity in order to forecast the rate paid by the last coupon.) The last method, update, forwards notifications from the quote to the observers of the helper.

The library provides a few concrete helper classes inherited from RateHelper. In the interest of brevity, allow me to do a little hand-waving here instead of showing you the actual code. Each of the helper classes implements the impliedValue for a specific instrument and includes code for returning the proper latest date. For instance, the DepositRateHelper class forecasts a quoted deposit rate by asking the curve being bootstrapped for the forward rate between its start and maturity dates; whereas the SwapRateHelper class forecasts a swap rate by instantiating a Swap object, pricing it on the curve, and implying its fair rate. In a multi-curve setting, it's also possible to use the bootstrapped curve for forecasting forward rates and an external curve for discounting. If you're interested, more details on this (and a discussion on what helpers to choose for a given curve) are available in Ametrano and Bianchetti [2].

We can finally dive into the bootstrap code. Listing 3.10 shows the interface of the IterativeBootstrap class, which is provided by the library and used by default.

Listing 3.10: Sketch of the \texttt{IterativeBootstrap} class template.
template <class Curve>
class IterativeBootstrap {
    typedef typename Curve::traits_type Traits;
    typedef typename Curve::interpolator_type Interpolator;
  public:
    IterativeBootstrap();
    void setup(Curve* ts);
    void calculate() const;
  private:
    Curve* ts_;
};

template <class Curve>
void IterativeBootstrap<Curve>::calculate() const {
    Size n = ts_->instruments_.size();

    // sort rate helpers by maturity
    // check that no two instruments have the same maturity
    // check that no instrument has an invalid quote

    for (Size i=0; i<n; ++i)
        ts_->instruments_[i]->setTermStructure(
                                       const_cast<Curve*>(ts_));

    ts_->dates_ = std::vector<Date>(n+1);
    // same for the other data vectors

    ts_->dates_[0] = Traits::initialDate(ts_);
    ts_->times_[0] = ts_->timeFromReference(ts_->dates_[0]);
    ts_->data_[0] = Traits::initialValue(ts_);

    for (Size i=0; i<n; ++i) {
        ts_->dates_[i+1] = ts_->instruments_[i]->latestDate();
        ts_->times_[i+1] =
            ts_->timeFromReference(ts_->dates_[i+1]);
    }

    Brent solver;

    for (Size iteration = 0; ; ++iteration) {
      for (Size i=1; i<n+1; ++i) {
          if (iteration == 0)   {
              // extend interpolation a point at a time
              ts_->interpolation_ =
                  ts_->interpolator_.interpolate(
                                        ts_->times_.begin(),
                                        ts_->times_.begin()+i+1,
                                        ts_->data_.begin());
              ts_->interpolation_.update();
          }

          Rate guess;
          // estimate guess by using the value at the previous iteration,
          // by extrapolating, or by asking the traits

          // bracket the solution
          Real min = Traits::minValueAfter(i, ts_->data_);
          Real max = Traits::maxValueAfter(i, ts_->data_);

          BootstrapError<Curve> error(ts_, instrument, i);
          ts_->data_[i] = solver.solve(error, ts_->accuracy_,
                                       guess, min, max);
      }

      if (!Interpolator::global)
          break;      // no need for convergence loop

      // check convergence and break if tolerance is reached; bail out
      // if tolerance wasn't reached in the given number of iterations
    }
}
For convenience, typedefs are defined to extract from the curve the traits and interpolator types. The constructor and the setup method are not of particular interest. The first just initializes the contained term-structure pointer to a null one; the second stores the passed curve pointer, checks that we have enough helpers to bootstrap the curve, and registers the curve as an observer of each helper. The bootstrap algorithm is implemented by the calculate method. In the version shown here, I'll gloss over a few details and corner cases; if you're interested, you can peruse the full code in the library.

First, all helpers are set the current term structure. This is done each time (rather than in the setup method) to allow a set of helpers to be used with different curves. (Of course, the same helpers could not be passed safely to different curves in a multi-threaded environment since hey would compete for it. Then again, much of QuantLib is not thread-safe, so it's kind of a moot point.) Then, the data vectors are initialized. The date and value for the initial node are provided by the passed traits; for yield term structures, the initial date corresponds to the reference date of the curve. The initial value depends on the choice of the underlying data; it is 1 for discount factors and a dummy value (which will be overwritten during the bootstrap procedure) for zero or forward rates. The dates for the other nodes are the latest needed dates of the corresponding helpers; the times are obtained by using the available curve facilities.

At this point, we can instantiate the one-dimensional solver that we'll use at each node (more details on solvers in a future post) and start the actual bootstrap. The calculation is written as two nested loops; an inner one—the bootstrap proper—that walks over each node, and an outer one that repeats the process. Iterative bootstrap is needed when a non-local interpolation (such as cubic splines) is used. In this case, setting the value of a node modifies the whole curve, invalidating previous nodes; therefore, we must go over the nodes a number of times until convergence is reached.

As I mentioned, the inner loop walks over each node—starting, of course, from the one at the earliest date. During the first iteration (when iteration == 0) the interpolation is extended a point at a time; later iterations use the full data range, so that the previous results are used as a starting point and refined. After each node is added, a one-dimensional root-finding algorithm is used to reproduce the corresponding market quote. For the first iteration, a guess for the solution can be provided by the bootstrap traits or by extrapolating the curve built so far; for further iteration, the previous result is used as the guess. The maximum and minimum value are provided by the traits, possibly based on the nodes already bootstrapped. For instance, the traits for bootstrapping zero or forward rates can prevent negative values by setting the minimum to a zero; the traits for discount factors can do the same by ensuring that the discounts are not increasing, i.e., by setting the maximum to the discount at the previous node.

The only missing ingredient for the root-finding algorithm is the function whose zero must be found. It is provided by the BootstrapError class template (shown in listing 3.11), that adapts the helper's quoteError calculation to a function-object interface. Its constructor takes the curve being built, the helper for the current node, and the node index, and stores them. Its operator() makes instances of this class usable as functions; it takes a guess for the node value, modifies the curve data accordingly, and returns the quote error.

Listing 3.11: Interface of the BootstrapError class template.
    template <class Curve>
    class BootstrapError {
        typedef typename Curve::traits_type Traits;
      public:
        BootstrapError(
            const Curve* curve,
            const shared_ptr<typename Traits::helper>& helper,
            Size segment);
        Real operator()(Rate guess) const {
            Traits::updateGuess(curve_->data_, guess, segment_);
            curve_->interpolation_.update();
            return helper_->quoteError();
        }
      private:
        const Curve* curve_;
        const shared_ptr<typename Traits::helper> helper_;
        const Size segment_;
    };
At this point, we're all set. The inner bootstrap loop creates a BootstrapError instance and passes it to the solver, which returns the node value for which the error is zero—i.e., for which the implied quote equals (within accuracy) the market quote. The curve data are then updated to include the returned value, and the loop turns to the next node.

When all nodes are bootstrapped, the outer loop checks whether another iteration is necessary. For local interpolations, this is not the case and we can break out of the loop. For non-local ones, the obtained accuracy is checked (I'll spare you the details here) and iterations are added until convergence is reached.

This concludes the bootstrap; and, as I don't want to further test your patience, it also concludes the example. Sample code using the PiecewiseYieldCurve class can be found in the QuantLib distribution, e.g., in the swap-valuation example.

Aside: a friend in need

"Wait a minute," you might have said upon looking at the PiecewiseYieldCurve declaration, "wasn't friend considered harmful?" Well, yes—friend declarations break encapsulation and force tight coupling of classes.

However, let's look at the alternatives. The Bootstrap class needs write access to the curve data. Beside declaring it as a friend, we might have given it access in three ways. On the one hand, we might have passed the data to the Bootstrap instance; but this would have coupled the two classes just as tightly (the curve internals couldn't be changed without changing the bootstrap code as well). On the other hand, we might have exposed the curve data through its public interface; but this would have been an even greater break of encapsulation (remember that we need write access). And on the gripping hand, we might encapsulate the data in a separate class and use private inheritance to control access. At the time of release 1.0, we felt that the friend declaration was no worse than the first two alternatives and resulted in simpler code. The third (and best) alternative might be implemented in a future release.

Bibliography

[1] V. Simonis and R. Weiss, Exploring Template Template Parameters. In Perspectives of System Informatics, Lecture Notes in Computer Science 2244, Springer Berlin/Heidelberg, 2001.
[2] F. Ametrano and M. Bianchetti, Everything You Always Wanted to Know About Multiple Interest Rate Curve Bootstrapping but Were Afraid to Ask, SSRN working papers series n. 2219548, 2013.

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