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Choosing a Pricing Measure for Monte Carlo Derivative Valuation

Article Quant Q&A · Author: Landscape

Summary

The document asks how to choose a probability measure when pricing derivatives with Monte Carlo simulation. It starts from risk-neutral valuation, where price is the discounted expected payoff, and notes that simulations may require modeling state variables before deriving the market variables used in the payoff. It also distinguishes measure changes from variance reduction and other simulation techniques that improve speed or precision.

For an interest-rate example, it rewrites a discounted payoff expectation under the risk-neutral measure as an expectation under the maturity forward measure, using a zero-coupon bond as numeraire. This can simplify simulation by avoiding a joint calculation involving the bank account and payoff in a short-rate model. The answer frames the alternative measure as a computational reformulation: under standard assumptions, the price remains the risk-neutral price, while the most convenient measure depends on the product and model. It gives no general selection algorithm or numerical comparison.

Key ideas

  • Risk-neutral valuation prices a claim as the discounted expected payoff under the risk-neutral measure.
  • A change of measure requires corresponding changes to the modeled dynamics and numeraire.
  • The maturity forward measure can simplify interest-rate valuation by incorporating discounting into a bond numeraire.
  • The choice of computationally convenient measure depends on the product and model.
  • Changing measure reformulates the calculation without changing the price under the stated assumptions.

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Full text
# Monte Carlo methods: Choosing the best measure


# Monte Carlo methods: Choosing the best measure












When pricing derivatives using Monte Carlo methods, we take outset in the risk neutral pricing formula which states that we need to calculate the expected value of the discounted cashflows. To do this, we simulate the market variables which - depending on the model and the product of consideration - might require that we actually simulate some state variables and then transform these into the market variables.

We can do all sorts of tricks to improve the accuracy / speed of the simulation such as using methods of variance reduction, quasi-MC methods with "different random variables", running the simulations in parallel, etc.

However, we might also use a change of measure: To get the correct price under a new measure, we will have to change the dynamics of the state / market variables and the numeraire accordingly. One example where such change of measure is benificial is for interest rate derivatives where we can avoid simulating the joint probability of the numeraire and the payoff function, $g$, by changning from the risk-neutral measure, $Q$, to the $T$-forward measure $Q^T$, that is

$$V_0 = B(0) \cdot E^Q_0 \left[\frac{1}{B(T)} \cdot g(r_T)\right] = P(0,T)\cdot E^T_0 \left[g(r_T)\right],$$

where $B$ denotes the bank account, $P$ denotes the zero coupon bonds. In a short rate model (such as Vasicek) this would allow us to easily use an exact simulation scheme without first finding the joint distribution of the $B$ and $g(r_T)$ (which both depend on $r$) or using a discretization scheme.

My question is: How do we decide which measure to price a claim under?

Does it entirely depend on the model and the product? Or does there exist any ways of deciding this a prior whichout performing numerical tests?

## Answer by Rylan (score 1)

https://quant.stackexchange.com/a/76764

I think you touch on these ideas in your question, so I'm sure some (or all) of this is review for you. But just to hopefully tie it all together:

The risk-neutral measure is used (loosely speaking) to price any claims we can hedge. It's a shortcut to finding the value of a self-financing portfolio to hedge with, and if the risk neutral pricing formula you mention comes up with some value $V_0$, that means that if we start with $V_0$ and we trade in a certain way (adapting to market conditions) then we can always get the payoff of the option we seek to price.

In the formula you give you your question, we have that the expectations are equal. You have the "discounted expectation of the payoff, under $Q$", which is always going to be the price but it may be very difficult to actually compute, and you have "something that looks easier to calculate, under $Q^T$".

So in other words, with the standard assumptions, you are always pricing under the risk-neutral measure. However, it might be easier to perform that pricing if you rewrite it to be the expectation under some other measure -- that depends typically on the product and model as you stated.

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.