Static Replication of a Squared Payoff with European Calls
Summary
The document derives a static replication for a claim paying the square of an asset’s terminal price. It identifies the method as the Carr–Madan representation, which writes a sufficiently regular payoff as a linear position in the underlying and a weighted strip of calls and puts. For a squared payoff, choosing a zero reference forward leaves a continuum of calls weighted by twice the strike integration measure. This gives a direct replication identity without relying on the original integration-by-parts route.
The discussion also examines the boundary term in that route. The strike derivative of a call is the negative risk-neutral tail probability, so the term involving squared strike vanishes only when the tail probability decays sufficiently quickly. The note cites thin-tail behavior, such as for a normal distribution, as an example; that condition is an assumption, not a universal guarantee. The formula also presumes the relevant integrals and payoff expectations exist, and practical replication is limited by available strikes and trading costs.
Key ideas
- A sufficiently regular terminal payoff can be represented with a position in the underlying and a weighted strip of calls and puts.
- For a squared payoff, choosing a zero reference forward yields a replication using calls across nonnegative strikes.
- The strike derivative of a call equals the negative risk-neutral probability that the terminal asset price exceeds the strike.
- The integration-by-parts boundary term vanishes only under suitable tail decay assumptions.
- The replication identity requires finite integrals and may be difficult to implement exactly with a discrete set of traded options.
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Full text
# Derivation of static replication formula
# Derivation of static replication formula
I know that a way of computing the price of a derivative paying $S^2$ at time $T$ is by making use of the following strategy:
$V=\int_{0}^{\infty} s^2 \frac{\partial^2 C}{\partial K^2}(K=s)ds$
Where $\frac{\partial^2 C}{\partial K^2}(K=s)$ is simply the risk neutral distribution of $S$.
Now, one should apply integration by parts twice in order to get an integral of the call prices. My question is, what is the name of this strategy/formula? Where can I find a derivation of it?
EDIT: By integrating by parts once, I get:
$V=\left[ s^2 \frac{\partial C}{\partial K}(K=s) \right]^{\infty}_{0} -\int_{0}^{\infty} 2s \frac{\partial C}{\partial K}(K=s)ds$
The first term is zero, but I don't know why. Can you please provide an explanation? Now by integrating by parts a second time the first derivative should become simply the price of options. But as I don't know why the first term is zero I hesitate to continue the derivation. Can you please help with next steps? EDIT: By taking into account the answer provided, the first term is zero,
$V= -\int_{0}^{\infty} 2s \frac{\partial C}{\partial K}(K=s)ds$
then we integrate by parts a second time:
$V= -\left(\left[2kC(k)\right]_{0}^{\infty}-\int_{0}^{\infty} 2 C(s)ds \right)$
Now, as
$C(k=\infty)=0$
Then:
$V= 2\int_{0}^{\infty}C(K=s)ds $
Is that correct?
## Answer by Count (score 6)
https://quant.stackexchange.com/a/67816
Since your payoff only depends on $S_T$, you could use the Carr-Madan-formula
$$f(S_T)=f(F_t) + f'(F_t) (S_T - F_t) + \int_0^{F_t} f''(K) (K-S_T)^+ \ d K + \int_{F_t}^{\infty} f''(K) (S_T-K)^+ \ d K$$
to get a static replication formula. In your example you have $f(S_T)=S_T^2$ and therefore $f'(F_t)=2F_t$ and $f''(F_t)=2$. Then choose $F_t=0$ to get:
$$ S_T^2=\int_0^\infty2(S_T-K)^+\mathrm{d}K=2\int_0^\infty(S_T-K)^+\mathrm{d}K $$
So you can replicate the payoff by using a portfolio of European-calls. Maybe this solution is easier than this integration by parts stuff.
## Answer by ir7 (score 5)
https://quant.stackexchange.com/a/67810
Proof sketch for
$$ \lim_{S\rightarrow \infty} S^2 \frac{\partial C}{\partial K}(K=S) = 0. $$
We have:
$$C(K) = E\left[(S_T-K)^+\right] = \int_K^\infty (u-K)f_{S_T}(u) du$$ $$ \frac{\partial C}{\partial K} = \int_K^\infty \frac{\partial }{\partial K}(u-K)f_{S_T}(u) du = -\int_K^\infty f_{S_T}(u) du =-(1-F_{S_T}(K))$$
$$ \frac{\partial C}{\partial K}(K=\infty) = 0 $$
Next, I think one might need to assume that the complementary cdf has a thin tail:
$$ \lim_{x\rightarrow \infty} x^2 (1-F_{S_T}(x)) = 0$$
In the case of a normal distribution this is true as, according to WolframAlpha, we have: $$ \lim_{x\rightarrow \infty} x^2 {\rm erfc(x)} = 0.$$
Note: See also this Quant SE direct solution.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.