Let $P^d$ and $P^f$ denote the respective USD and EUR risk-neutral measures. We assume that, under the USD risk-neutral measure,
\begin{align*}
dS_t = S_t \Big(\big(r^d-r^f \big)dt +\sigma dW_t \Big),
\end{align*}
where
- $r^d$ and $r^f$ denote respectively the USD and EUR interest rates,
- $\sigma$ is the constant volatility, and
- $W_t$ is a standard Brownian motion.
Then
\begin{align*}
\frac{dP^f}{dP^d}\big|_t &= \frac{S_t B^f_t}{S_0 B^d_t}\\
&=\exp\left(-\frac{\sigma^2}{2}t +\sigma W_t\right),
\end{align*}
where $B^d_t$ and $B^f_t$ are the respective USD and EUR money-market account values at time $t$. Consequently,
\begin{align*}
\hat{W}_t = W_t -\sigma t
\end{align*}
is a standard Brownian motion under the EUR risk-neutral measure. Moreover,
\begin{align*}
d\frac{1}{S_t} &= \frac{1}{S_t}\Big(\big(r^f-r^d +\sigma^2\big)dt -\sigma dW_t \Big)\\
&=\frac{1}{S_t}\Big(\big(r^f-r^d \big)dt +\sigma d(-\hat{W}_t) \Big) \\
&=\frac{1}{S_t}\Big(\big(r^f-r^d \big)dt +\sigma d\tilde{W}_t \Big),
\end{align*}
where $\tilde{W}_t = -\hat{W}_t$ is also a standard Brownian motion under the foreign risk-neutral measure.
Let $E^d$ and $E^f$ be expectations corresponding to USD and EUR risk-neutral measures $P^d$ and $P^f$. Then
\begin{align*}
E^f\bigg( \frac{1}{B^f_TS_T} (S_T-K)^+\bigg) &= E^d\bigg(\frac{S_T B^f_T}{S_0 B^d_T} \frac{1}{B^f_TS_T} (S_T-K)^+\bigg)\\
&= \frac{1}{S_0}E^d\bigg(\frac{1}{B^d_T} (S_T-K)^+\bigg)\\
&= e^{-r^d T}\frac{1}{S_0}\left[F\Phi(d_1) - K\Phi(d_2) \right],
\end{align*}
where
- $F= S_0 e^{(r^d-r^f)T}$,
- $\Phi$ is the cumulative distribution function of a standard normal random variable,
- $d_1 = \Big[\ln \frac{S_0}{K} + \big(r^d-r^f + \frac{1}{2}\sigma^2 \big)T\Big]/(\sigma \sqrt{T})$, and
- $d_2 = d_1 -\sigma \sqrt{T}$.
Similarly,
\begin{align*}
E^f\bigg(\frac{K}{B^f_T}\Big(\frac{1}{K} - \frac{1}{S_T}\Big)^+\bigg) &= e^{-r^f T} K\bigg[\frac{1}{K}\Phi(-\hat{d}_2) -\hat{F}\Phi(-\hat{d}_1) \bigg],
\end{align*}
where
- $\hat{F}= \frac{1}{S_0}e^{(r^f-r^d)T}$,
- $\hat{d}_1 = \Big[\ln \frac{K}{S_0} + \big(r^f-r^d + \frac{1}{2}\sigma^2 \big)T\Big]/(\sigma \sqrt{T}) = -d_2$, and
- $\hat{d}_2 = \hat{d}_1 - \sigma \sqrt{T} = -d_1$.
Then,
\begin{align*}
E^f\bigg(\frac{K}{B^f_T}\Big(\frac{1}{K} - \frac{1}{S_T}\Big)^+\bigg) &= e^{-r^f T} K\bigg[\frac{1}{K}\Phi(-\hat{d}_2) -\hat{F}\Phi(-\hat{d}_1) \bigg]\\
&=e^{-r^f T} K\bigg[\frac{1}{K}\Phi(d_1) -\hat{F}\Phi(d_2) \bigg]\\
&= e^{-r^d T}\frac{1}{S_0}\left[F\Phi(d_1) - K\Phi(d_2) \right].
\end{align*}