Pricing Futures Options with Characteristic Functions
Summary
The document explains how Fourier pricing formulas for European options can be adapted from spot assets to futures. It focuses on the Variance Gamma model and notes that its raw process characteristic function needs a drift adjustment so the exponentiated process is a martingale under the risk-neutral measure. The resulting log-futures characteristic function uses the initial futures price and omits the spot model’s interest-rate drift term.
The answer then reuses the probability terms calculated from the appropriate characteristic function and prices the call by discounting the futures-based payoff probabilities. It says the same general approach largely applies to Heston, while directing readers to fuller derivations for detail. The key modeling caveat is that a characteristic function must match the underlying and pricing measure; substituting a futures price into a spot formula without adjusting its dynamics is insufficient. The response provides equations and references but no numerical example or empirical validation.
Key ideas
- Futures option pricing uses the characteristic function of the futures price under the relevant pricing measure.
- The Variance Gamma process requires a drift adjustment to make its exponentiated process a martingale.
- The futures log-price characteristic function uses the initial futures price without the spot model’s interest-rate drift term.
- The probability terms in Fourier pricing can be reused with the appropriate futures characteristic function.
- The call price discounts the futures-based payoff expression.
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Full text
# Characteristic functions for options on futures
# Characteristic functions for options on futures
Using simple delta-probability decomposition, the price European call options a non- dividend paying asset can be computed as
\begin{equation} C(T,K) = {S_0}{\rm{ }}{\Pi _1} - {e^{ - rT}}K{\rm{ }}{\Pi _2}, \end{equation}
with
\begin{equation} {\Pi _1} = \frac{1}{2} + \frac{1}{\pi }\int_0^\infty {{\mathop{\rm Re}\nolimits} \left[ {\frac{{{e^{ - iw\ln (K)}}{\psi _{\ln {S_T}}}(w - i)}}{{iw{\psi _{\ln {S_T}}}( - i)}}} \right]} \;dw, \end{equation}
\begin{equation}\label{pi2} {\Pi _2} = \frac{1}{2} + \frac{1}{\pi }\int_0^\infty {{\mathop{\rm Re}\nolimits} \left[ {\frac{{{e^{ - iw\ln (K)}}{\psi _{\ln {S_T}}}(w)}}{{iw}}} \right]} \;dw, \end{equation}
and where ${\psi _{\ln {S_T}}}$ is the characteristic function of the log-asset price. For instance, for the Heston and Variance Gamma models, the corresponding $\psi^{H}_{\ln {S_T}}$ and $\psi^{VG}_{\ln {S_T}}$ are given by:
- Variance Gamma
\begin{equation} \psi _{\ln ({S_t})}^{VG}(w) = {\left( {\frac{1}{{1 - i\theta vw + ({\sigma ^2}v/2){w^2}}}} \right)^{t/v}} \end{equation}
- Heston
\begin{equation} \psi_{\ln(S_t)}^{H} (w) = e^{ C(t,w) \overline{V}+ D(t,w) V_0 +iw \ln(S_0 e^{rt})}, \end{equation} where \begin{eqnarray*} C(t,w) &=& a \left[ r_{-} \cdot t - \frac{2}{\eta^2} \ln \left( \frac{1-g e^{-ht}}{1-g} \right) \right], \\ D(t,w) &=& r_{-} \frac{1-e^{-ht}}{1-g e^{-ht}}, \\ \alpha &=& - \frac{w^2}{2}- \frac{iw}{2}, \quad \beta = a - \rho \eta i w , \quad \gamma = \frac{\eta^2}{^2}, \\ r_{\pm} &=& \frac{ \beta \pm h}{\eta^2}, \quad h= \sqrt{ \beta^2- 4 \alpha \gamma}, \quad g= \frac{r_{-}}{r_{+}}. \end{eqnarray*}
These equations, however, are intended for options on spot prices. If I were interested in options on futures, which modifications should be made to the formulas above?(because it is not as straightforward as simply using $F_0$ instead of $S_0$ and removing the effects of $r$, right?)
## Answer by LocalVolatility (score 3, accepted)
https://quant.stackexchange.com/a/31542
I generally agree with Quantuple's comment.
I explicitly discuss the case of the variance gamma model, though most of this also applies to Heston. First note that in case of the variance gamma model, the characteristic function that you presented is not the one of the logarithmic stock price under the risk-neutral probability measure. Let $X$ be a variance gamma process as in Madan et al. (1998). The characteristic function of $X_t$ is
\begin{equation} \phi_{X_t}(\omega) = \left( 1 - \mathrm{i} \theta \nu \omega + \frac{1}{2} \sigma^2 \nu \omega^2 \right)^{-t / v}; \end{equation}
see Equation (7) in the original paper (note that there was originally a typo in your question). We now look for a drift term $\gamma$, such that the process
\begin{equation} Y_t = \exp \left\{ \gamma t + X_t \right\} \end{equation}
is a martingale. You find that
\begin{equation} \gamma = -\ln \left( \phi_{X_t}(-\mathrm{i}) \right). \end{equation}
Your model for the stock and forward prices respectively is then
\begin{eqnarray} S_t & = & S_0 \exp \left\{ (r + \gamma) t + X_t \right\}\\ F_t(T) & = & F_0(T) \exp \left\{ \gamma t + X_t \right\} \end{eqnarray}
with characteristic functions
\begin{eqnarray} \phi_{\ln \left( S_t \right)}(\omega) & = & \exp \left\{ \mathrm{i} \left( \ln \left( S_0 \right) + r + \gamma \right) \omega t \right\} \phi_{X_t}(\omega)\\ \phi_{\ln \left( F_t(T) \right)}(\omega) & = & \exp \left\{ \mathrm{i} \left( \ln \left( F_0(T) \right) + \gamma \right) \omega t \right\} \phi_{X_t}(\omega) \end{eqnarray}
You can now re-use your general expressions for $\Pi_1$ and $\Pi_2$ in terms terms of the appropriate characteristic function as these are just the corresponding exercise probabilities under the respective measure. Your pricing formula then becomes
\begin{equation} C_0 = e^{-r T} \left( F_0(T) \Pi_1 - K \Pi_2 \right). \end{equation}
If you want to convince yourself that this is correct, then you could work through the detailed steps given e.g. in Schmelzle (2010).
References
Madan, Dilip B, Peter P. Carr and Eric C. Chang (1998) "The Variance Gamma Process and Option Pricing", European Finance Review, Vol. 2, pp. 79-105
Schmelzle, Martin (2010) "Option Pricing Formulae Using Fourier Transform: Theory and Application", Technical ReportShown 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.