# Implication of unique risk neutral measure

I'm reading Shreve Stochastic Calculus II, theorem 5.4.9 (Second fundamental theorem of asset pricing),

This is the part that confuses me :

suppose there is only one risk-neutral measure. This means first of all that the filtration for the model is generated by the $$d$$-dimensional Brownian motion driving the assets. If that were not the case (i.e., if there were other sources of uncertainty in the model besides the driv­ing Brownian motions), then we could assign arbitrary probabilities to those sources of uncertainty without changing the distributions of the driving Brow­nian motions and hence without changing the distributions of the assets. This would permit us to create multiple risk-neutral measures

This seems to be a proof by contradiction of the statement: only one risk-neutral mesure $$\implies$$ the driv­ing Brownian motions are the only source of uncertainty.

I think the example of market model is the GBM: $$dS_i(t) = \alpha_i(t)S_i(t)dt+\sigma_i(t)S_i(t)dW_i(t) \quad i=1,...,d$$ Here the driving Brownian motion seems to refer to $$W_i(t), i=1,...,d$$, so I wonder what could be "other sources of uncertainty in the model besides the driv­ing Brownian motions", could they be $$\alpha_i(t)$$ or $$\sigma_i(t)$$ or some $$dB_i(t)$$ for some random variable B added to the model?

Then the writer says we could "assign arbitrary probabilities to those sources of uncertainty", does it refer to the risk neutral probability measure we construct? But that measure only acts on the (preimage of ) random stock price, we cannot assign probabilities to any uncertainty...

I think "to create multiple risk-neutral measures", is related to the definition of risk neutral probability measure namely (i) It must be equivalent to the original probability measure and (ii) it must make the discounted stock price process a martingale

Anyone understand what the writer is saying?

• To give a concrete example. suppose we are interested in real (inflation adjusted) profits and not (as in most models) nominal profits. Then there is an additional source of uncertainty beyond the $dW(t)$, namely the inflation between the time you buy and sell securities, that is not accounted by the $dW_i(t)$ in your equation. By making different assumptions about the prob of future inflation scenarios, i.e. different $I(t)=\cdots dt + \cdots dI(t)$ processes we would have different risk neutral measures. The problem could be overcome by adding $dI(t)$ to the list of sources of uncertainty Apr 13, 2022 at 13:28
• Once we know that $dW_1(t),\cdots,dW_d(t),dI(t)$ are all the sources of randomness and we have equations that connect all the state variables of the problem to these rv's, then we can compute the probabilities of any scenario and the risk neutral measure is again unique. Apr 13, 2022 at 13:42
• @noob2 Please see if I get it right : if we have a unique risk neutral prob measure , then we've already specified all sources uncertainty under our market model's assumption , and used those information ( Filtration generated the vector containing all specified uncertainties ) to build the unique risk neutral prob measure . Such prob measure is final and there cannot legitimately be another one without changing our model assumptions .
– C.C.
Apr 13, 2022 at 14:13

I think you can think of that part of Shreve's by noting that you're using $$d$$ stocks (and a bank account, with the instantaneous rate being an adapted process, i.e. you know at time $$t$$ what the rate covering the interval $$[t, t+dt]$$ would be) to hedge away the risks in the portfolio. In other words, you can design a perfect hedging strategy by using what you have at hand (the $$d$$ stocks and the bank account).
A good way of thinking about a new redundant source, as in the first case of the previous paragraph could be having $$d$$ stocks (and the associated $$d$$-dimensional BM) and throwing a dice. The outcome of the dice tells you nothing about what your hedging strategy should be for your portfolio, and therefore it does not matter how you assign probabilities to the outcome of that dice, all the probability measures you define are equivalent, as the market price of risk equations remain unchanged.