Pricing a Power Claim in the Black–Scholes Model
Summary
The document discusses the arbitrage-free valuation of a claim paying a power of the terminal stock price in the standard Black–Scholes model. The questioner applies Itô’s lemma first to the power process and then to its logarithm, but obtains an exponent that differs from the solution manual. The response recommends pricing by the discounted risk-neutral expectation instead: derive the terminal stock distribution from the dynamics of its logarithm, substitute that distribution into the payoff, and use the exponential moment of a normal variable.
The response also points out that stochastic integrals should be taken over time from the valuation date to maturity, and that the Brownian increment has a variance determined by that time interval. It presents a closed-form expression for the expectation-based price and contrasts analytic valuation with numerical PDE or simulation methods. The source is a worked forum exchange, not a full derivation; its notation for the normal variable and some exponent terms are potentially confusing, so readers should verify the exact formula against the risk-neutral measure and discounting convention.
Key ideas
- A power payoff can be priced as a discounted risk-neutral expectation of the terminal stock value raised to a constant power.
- Applying Itô’s lemma to the logarithm of the stock gives a normally distributed log return in the Black–Scholes model.
- The stochastic integral runs over calendar time from valuation to maturity, rather than using Brownian values as integration bounds.
- The normal exponential moment supplies the additional variance contribution in the power payoff expectation.
- The response’s notation is compressed and its formula should be checked carefully.
Tags
Full text
# Pricing of a simple contingent claim
# Pricing of a simple contingent claim
Earlier I had the question (5.11 Tomas Bjork): $$ \frac{\partial F}{\partial t}+\frac{1}{2}x^2\frac{\partial^2 F}{\partial t^2}+x = 0 $$ $$ F(T,x) = ln(x^2) $$ And solve it using Feynman-Kac. The PDE gives the stochastic differential $dX=XdW$ so to solve the PDE I need what $X$ that solves the stochastic differential. An example in the book showed how to solve a differential of the type above and did it by letting $Z=lnX$, compute its differential $dZ$ (which then become independent of Z, which I guess is the point), solve for Z, and then for X just raise the exponential function with the solution for Z (since $Z=lnX$). This all went fine. Now I have a question considering the standard BS-model where I´m supposed to derive the arbitrage free proce process for the contingent claim $X=(S(T)^{\beta})$, $\beta=const.$
Standard BS-model gives the differential for $S$ which is $dS=rSdt+\sigma S dW$. Thinking of the earlier question I gave it a try doing as before. Setting $Y=S^{\beta}$, and computing $dY$ gives: $dY = Y(r\beta +\frac{\beta(\beta-1)}{2}\sigma^2)dt+\sigma \beta Y dW$ for which I then need the $Y$ that solves this $dY$. Doing as above however, setting $Z=ln Y$ (with $Z_0 = ln s_0$), computing $$dZ = \frac{1}{Y}dY-\frac{1}{2Y^2}(dY)^2 = dt(\beta r+\frac{\beta(\beta-1)}{2}\sigma^2-\frac{\beta^2\sigma^2}{2})+\beta \sigma dW$$ Integrating and raising the exponential function would give the solution $Y$ for $dY$: $$ Y = s_0 exp(\int^T_t(\beta r+\frac{\beta(\beta-1)}{2}\sigma^2-\frac{\beta^2\sigma^2}{2})dt + \int^{W_T}_{W_t}\beta \sigma dW)$$ which then would give the price process $$\pi = s_0^\beta exp[\beta(\beta r+\frac{\beta(\beta-1)}{2}\sigma^2-\frac{\beta^2\sigma^2}{2})(T-t)]$$ This result, according to the solutions manual is wrong however. It has this extra term $\frac{\beta^2\sigma^2}{2}$ which appears when doing the extra step of letting $Z=lnY$ and so on but I can´t understand what the difference is from the first case and why this doesn´t work. Please let me know if something is unclear and I´ll try to revise.
## Answer by Good Guy Mike (score 3, accepted)
https://quant.stackexchange.com/a/10922
You're making it much more complicated than it has to be. Remember that the arbitrage free price is given by $e^{-r(T-t)}\mathbb{E}(\Phi(S_T))$ (to be correct, this should be conditioned on the appropriate sigma-algebra. I'll exclude that for ease of writing). Now use Itos on $\ln(S_t) = X_t$, this yields, after integration (Note that you integrate from $t$ to $T$ in all integrals. I see that you've written the integral from $W_t$ to $W_T$, this is incorrect):
$S_T = S_t \cdot \text{exp}((r-\sigma^2/2)(T-t) + \sigma Z$ where $Z \in N(0,\sqrt{T-t})$.
Now insert this into the expression for the price:
$e^{-r(T-t)}\mathbb{E}(S_T^{\beta})=S_t^{\beta}e^{-r(T-t)+\beta(r-\sigma^2/2)(T-t)}\mathbb{E}(e^{\beta Z})=S_t^{\beta}e^{-r(T-t)+\beta(r-\sigma^2/2)(T-t)+\beta^2\sigma^2(T-t)}$
This you can rewrite as you'd like.
As I mentioned previously. Note that you integrate from $t$ to $T$ and you'll get:
$X_T - X_t = \int_t^T (...)du + \int_t^T(...)dW_u$. Writing the last integral with limits $W_t$ to $W_T$ is incorrect!
Hope this makes it clear to you! This is the usual way to price contingent claims, using the PDE directly is only necessary when you can't compute the price analytically and must approximate it using numerical methods (you can use Monte-Carlo simultation also, for this the PDE is not required either).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.