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Pricing a Two-Rate Option with a Gaussian Copula

Article Quant Q&A · Author: InnocentR

Summary

The document considers pricing an exotic interest-rate option when the individual rate distributions are modeled with calibrated SABR marginals and their dependence is represented by a Gaussian copula. It expresses the option value as the pricing-measure expectation of the payoff multiplied by the discount factor, then writes that expectation as an integral over the joint distribution. The joint density can be represented using marginal densities and a copula density.

The response highlights that discounting may need explicit treatment and that including a stochastic discount factor as a third variable changes the dependence model: a three-variable Gaussian copula requires three pairwise correlations. A separate answer gives rank-correlation relationships for estimating a Gaussian copula parameter from paired observations. These are pointers rather than a full calibration procedure; they do not specify payoff integration limits, numerical methods, or how market-price fitting handles the discount factor and model assumptions.

Key ideas

  • Option value is the pricing-measure expectation of the payoff times its discount factor.
  • A copula combines marginal distributions into a joint distribution for pricing dependent rates.
  • A model with two rates and a stochastic discount factor may require three dependence parameters.
  • Rank correlations can provide estimates of a Gaussian copula parameter under the stated relationships.
  • The discussion does not provide a complete numerical calibration workflow.

Tags

Full text
# Gaussian copula calibration to option price


# Gaussian copula calibration to option price












I have an "exotic" option that is a function of two interest rates (say 3m Libor at 1y maturity and 2y maturity). I assume both the rates follow sabr model (already calibrated to vanillas), so I have both the marginals fully defined. Price of this option is observable in the market. I assume Gaussian copula to model the dependence of two rates. So only parameter left to estimate is the correlation.

How do I calibrate this copula to market prices i.e. how do I estimate the correlation? I vaguely know it will involve iteratively solving the double integral of gaussian copula to match market price but I don't know how to get the limits of the integral and also how to go from copula to option price.

Please feel free to assume a reasonable payoff of option in case it helps explain. Thanks for any pointers for this copula beginner.

## Answer by g g (score 4, accepted)

https://quant.stackexchange.com/a/65620

You did not mention it, but I think you also need to include the discount factor $D$ at the time $T$ of maturity of your option as a third variable. Denote the two interest rates as $r$ and $s$ and the pay-out function of your option as $f=f(r,s).$

The price of your option is then the expectation of the discounted cash flow: $$ \text{price }=\mathbb{E}[f(r,s)D]$$ under your pricing measure. Denote the density function of your pricing measure by $\phi$. Since it is the joint density of the three risk factors $\phi=\phi(r,s,D)$. I assume that the measure, hence also the density, is supported on $\mathbb{R}^3.$

Then $$ \mathbb{E}[f(r,s)D] = \int_{-\infty}^\infty\int_{-\infty}^\infty\int_{-\infty}^\infty f(r,s)D\, \phi(r,s,D) \,d r\,d s\,d D.$$

Up to now this is almost completely generic. To introduce your copula assumption you need to write the joint density in terms of the marginal densities and the copula density. It is true in general that a density can be written as: $$ \phi(r,s,D)=m_1(r)m_2(s)m_3(D)*c(r,s,D)$$ where $m_i$ are the marginal and $c$ is the copula density (see for example Proposition 4.2.14 in Actuarial Theory for Dependent Risks). Pulling it all together your price is the triple integral $$ \text{price }= \int_{-\infty}^\infty\int_{-\infty}^\infty\int_{-\infty}^\infty f(r,s)D\, m_1(r)m_2(s)m_3(D)*c(r,s,D) \,d r\,d s\,d D. $$

Note that this Gaussian copula $c$ requires three correlation parameters, not just one!

## Answer by simzoor (score 1)

https://quant.stackexchange.com/a/65435

If I remember e.g. McNeil et. al (2005: Proposition 5.29) correctly and if I understand you correctly, it should be possible to estimate the correlation parameter $\varrho$ of the Gaussian copula from two time series of the desired interest rates via calculating Kendall's $\tau$ or Spearman's rank correlation $\rho$ using the relationships:

$\varrho = \sin(\frac{\pi}{2}\tau)$

or

$\varrho = 2\sin(\frac{\pi}{6}\rho)$

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