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This is a sixth in a series of posts covering chapter 6 of my book (here is the first one). This week: Monte Carlo models.

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### Putting it all together

Having read all about the different pieces of a model in the previous posts, you’d expect me to start assembling them and finally get a working Monte Carlo model. However, before doing so, we still have the task of picking a specific set of pieces among those available in our toolbox. This will be the subject of the next subsection—after which we’ll build the model, developer’s honor.

#### Monte Carlo traits

As I mentioned before, there’s a number of choices we have to make in order to implement a Monte Carlo model. Depending on the dimension of the problem and the stochastic process we choose, we can use one- or multi-dimensional paths and generators; or we can use pseudo-random numbers or low-discrepancy sequences (and even though I haven’t listed them, there’s quite a few algorithms available for either type).

For at least one of those choices, dynamic polymorphism is not an option: one-dimensional and multi-dimensional generators can’t have a common interface, since their methods have different input and return types. Therefore, we’ll go with static polymorphism and supply the required classes to the model as template arguments.

The problem is, this can result very quickly in unwieldy declarations; the sight of something like

can bring shudders to the most seasoned developer. Sure, typedefs can help; by using them, a user might assign mnemonics to default choices and shorten the declaration. Default template arguments might alleviate the pain, too. However, we went for a mechanism that also allowed us on the one hand, to define mnemonics for commonly used groups of related choices; and on the other hand, to add helper methods to the mix. To do this, we decided to use traits [1].

Listing 6.12: Example of random-number generation traits.

Listing 6.12 shows the default traits classes for pseudo-random and low-discrepancy number generation. First comes the GenericPseudoRandom class template. It takes as template arguments the type of a uniform pseudo-random number generator and that of an inverse-cumulative function object, and builds a number of other types upon it. The type of the passed generator itself is defined as urng_type—emphasis on “u” for uniform and “n” for number. Based on this type, it defines rng_type, no longer uniform since it uses the inverse-cumulative function to return numbers according to its distribution; ursg_type, where the “n” is replaced by “s” for sequence; and finally rsg_type, which generates sequences of numbers according to the passed distribution. The compile-time constant allowErrorEstimate, written as an enumeration to satisfy older compilers (it should really be a static const bool) tells us that this generator allows us to estimate the Monte Carlo error as function of the number of samples; and the helper function make_sequence_generator makes it easier to create a generator based on the passed inputs.

Then, we instantiate the class template with our weapons of choice. For the basic generator, that would be the MersenneTwisterUniformRng class; for the function object, the InverseCumulativeNormal class, since we’ll most often want normally distributed numbers. The resulting traits class will be our default for pseudo-random generation; fantasy being not our strong suit, we defined it as the PseudoRandom class.

The GenericLowDiscrepancy class template is defined is a similar way, but with two differences. Since low-discrepancy numbers are generated in sequences, the types for single-number generation are missing; and the enumeration tells us that we can’t forecast the statistical error we’ll get. We define the LowDiscrepancy traits class as the one obtained by selecting the SobolRsg class as our generator and, again, the InverseCumulativeNormal class as our function object.

Finally, we defined a couple of traits classes to hold types related to specific Monte Carlo functionality, such as the types of used paths, path generators, and path pricers. They are shown in listing 6.13: the SingleVariate class holds the types we need for 1-D models, while the MultiVariate class holds the types for multi-dimensional ones. They are both template classes, and take as their template argument a traits class for random-number generation.

Listing 6.13: Example of Monte Carlo traits.

By combining the provided RNG and Monte Carlo traits (or any traits classes that one might want to define, if one wants to use any particular type) not only we can provide a model with all the necessary information, but we can do it with a simpler and more mnemonic syntax, such as

the idea being to move some complexity from users to developers. We have to use some template tricks to get the above to work, but when it does, it’s a bit more readable (and writable) for users. But that’s for next section, in which we finally assemble a Monte Carlo model.

#### The Monte Carlo model

Listing 6.14 shows the MonteCarloModel class, which is the low-level workhorse of Monte Carlo simulations.

Listing 6.14: Implementation of the MonteCarloModel class template.

It brings together path generation, pricing and statistics, and as such takes template arguments defining the types involved: a MC traits class defining types related to the simulation, a RNG traits class describing random-number generation, and a statistics class S defaulting to the Statistics class. (Don’t worry. I’m not going off another tangent, even though an early outline of this chapter had a section devoted to statistics. If you’re interested, the Statistics class is in appendix A of the book.) The MC class is a template template argument, so that it can be fed the RNG traits (as shown in the previous section; see for instance the MultiVariate class).

The class defines aliases for a few frequently used types; most of them are extracted from the traits, by instantiating the MC class template with the RNG class. The resulting class provides the types of the path generator and the path pricer to be used; from those, in turn, the type of the sample paths and that of the returned prices can be obtained.

The constructor takes the pieces that will be made to work together; at least a path generator (well, a pointer to one, but you’ll forgive me for not spelling out all of them), a path pricer, and an instance of the statistics class, as well as a boolean flag specifying whether to use antithetic variates. Then, there are a few optional arguments related to control variates: another path pricer, the analytic value of the control variate, and possibly another path generator. Optional arguments might not be the best choice, since they make it possible to pass a control variate path pricer and not the corresponding analytic value; it would have been safer to have a constructor with no control variate arguments, and another constructor with both path pricer and analytic value being mandatory and with an optional path generator. However, the current version saves a few lines of code. The constructor copies the passed arguments into the corresponding data members, and sets another boolean flag based on the presence or the lack of the control-variate arguments.

The main logic is implemented in the addSamples method. It’s just a loop: draw a path, price, add the result to the statistics; but it includes a bit of complication in order to take care of variance reduction. It takes the number of samples to add; for each of them, it asks the path generator for a path, passes the path to the pricer, and gets back a price. In the simplest case, that’s all there is to it; the price can just be added to the statistics (together with the corresponding weight, also returned from the generator) and the loop can start the next iteration. If the user passed control-variate data, instead, things get more interesting. If no path generator were specified, we pass to the alternate pricer the same path we used for the main one; otherwise, we ask the second generator for a path and we use that one. In both cases, we adjust the baseline price by subtracting the simulated price of the control and adding its analytic value.

It’s not over yet. If the user also asked for antithetic variates, we repeat the same dance (this time asking the generator, or the generators, for the paths antithetic to the ones we just used) and we add to the statistics the average of the regular and antithetic prices; if not, we just add the price we obtained on the original paths. Lather, rinse, and repeat until the required number of samples is reached.

Finally, the full results (mean price and whatnot) can be obtained by calling the sampleAccumulator method, which returns a reference to the stored statistics. “Accumulator” is STL lingo; we should probably have used a method name taken from the financial domain instead. Such as, I don’t know, “statistics.” Oh well.

Next time: Monte Carlo simulations.

#### Bibliography

[1] N.C. Myers, Traits: a new and useful template technique. In The C++ Report, June 1995.