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Assume I have a stochastic ODE $$dS = a(S)dt + b(S)dW,$$ with Euler approximation $$\hat{S}_{n+1}=F_n(\hat{S}_n)=\hat{S}_n+a(\hat{S}_n)h+b(\hat{S}_n)Z_n\sqrt{h}.$$ This allows me to create sample paths based on drawing normally distributed random numbers $Z_n$ from $N(0,1)$.

Now the estimated value of my option is $$\hat{V}=\frac{1}{N}\sum_i f(S^i_T)$$ where $f$ is the payoff function and $S^i_T$ is the i-th sample path of the process at time $T$.

Assume the ODE and $f$ have various parameters, for example starting value $S_0$, risk-free interest rate $r$ and volatility $\sigma$. Furthermore, f is sufficiently continous such that the derivatives

$$D_n=\frac{\partial F_n(\hat{S}_n)}{\partial \hat{S}_n } $$

exist.

Based on these quantities, how can I compute sensitivities using the adjoint method?

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Very interesting question - could you insert a link to what the "adjoint method" or "adjoint MC method" is? –  Richard Jan 20 at 12:11
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Furthermore: with "sensitivities" you mean something like the Greeks or more general derivatives w.r.t. to certain parameters. Have you heard of Malliavin-calculus? –  Richard Jan 20 at 12:12

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