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Constraints on Risk-Neutral Distributions in a Frictionless Market

Article Quant Q&A · Author: SBF

Summary

The document asks which terminal stock-price distributions can serve as risk-neutral pricing distributions in a frictionless market with a constant interest rate and a non-dividend-paying stock. No-arbitrage fixes the distribution’s expected value to the forward price, but the question is whether that condition determines higher moments as well.

One response argues that, absent further traded claims or model restrictions, many distributions with the required expectation can be represented by a martingale model. Another points out a support constraint: events possible under the real-world measure cannot be assigned zero risk-neutral probability. A contrasting response says additional moments can be constrained in a standard Black–Scholes setting with specified dynamics, illustrating the point through the second moment. The discussion therefore distinguishes broad model freedom from restrictions imposed by traded securities, probability support, and a chosen model; it does not offer one unconditional answer for every market setup.

Key ideas

  • No-arbitrage requires the risk-neutral expected terminal stock price to equal the forward price.
  • That first-moment condition alone can allow many terminal distributions when no further model restrictions apply.
  • Risk-neutral probability cannot exclude events that have positive probability under the real-world measure.
  • A specified model or additional traded claims can constrain higher moments.
  • The amount of distributional freedom depends on the assumptions and available market information.

Tags

Full text
# How free are we in risk-neutral distributions?


# How free are we in risk-neutral distributions?












Suppose we do not have a particular pricing model, we have just a frictionless market with constant interest rate (say $0$), and some traded stock $S$ which does not pay dividends. For any expiry $T$ to price options/contingent claims consistently we need a pricing rule, or equivalently a pricing (risk-neutral) probability measure. In particular, to price European call options we only need a marginal of such probability measure being the distribution of $S_T$.

Standard non-arbitrage arguments imply that the futures price $F(0,T) = \Bbb E^{\Bbb Q}S_T$ must satisfy $F(0,T) = S_T$, otherwise using a static replication strategy we can exploit mispricing in case of inequality. Hence, for the pricing distribution $\Bbb Q$ at least the first moment is fixed externally. What about the rest of the distribution? Say, we only focus on distributions that can be completely recovered from their moments. The first one is fixed, what about others, are we completely free in choosing them given the conditions above?

## Answer by SBF (score 1)

https://quant.stackexchange.com/a/18846

The answer is: yes. We can consider a model that assumes there is only one jump with distribution $p$, and otherwise the stock value does not change. Then for $p$ to be a martingale measure the only condition is on expectation of $p$. Hence, any distribution with desired expectation can be a marginal of some pricing measure.

## Answer by Mark Joshi (score 1)

https://quant.stackexchange.com/a/18847

basically, you have very few constraints. The main other constraint you might consider is that if the real-world probability of lying in a given set is positive, the risk-neutral probability must be too.

So a point mass at the futures price is not valid unless you believe that this is the case in real-world too.

(see chapter 6 of my book "concepts etc" for further discussion.)

## Answer by phdstudent (score 0)

https://quant.stackexchange.com/a/18843

The other moments are not free.

Suppose we are in the standard BS environment with one stock and one bond and a single brownian motion. Suppose we have a derivative that at maturities pays: $V_T=S^2_T$ and we want to price it. Under the martingale measure we know that: $E_t^Q[S_T]=S_t e^{r(T-t)}$ and $Var^Q_t(S_T)=S_t^2e^{2r(T-t)}(e^{\sigma^2(T-t)}-1)$. Using the fact that $Var(X)=E(X^2)-E(X)$ we can pin-down the value of the security $V$ at time $t$.

Hope this example shows that we are not free to pick other moments. I am not sure whether I fully understood your question though.

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