Hello,
Being a beginner of lyx (no knowledge of latex), I have this lyx-generated 
latex source (lyxout.txt attached)
When doing view / PDF (PDFLatex), latex run 1, I get a few "Missing ... 
inserted"
and these errors:

>>>>>LaTeX Error: \begin{tabular} on input line 90 ended by \end{eqnarray*}.

... & = & d_{1}-\sigma\sqrt{T}\end{eqnarray*}

Your command was ignored.

Type I <command> <return> to replace it with another command,

or <return> to continue without it.

>>>>>Too many }'s.

\end{tabular}

\begin{tabular}{|c|c|c||c|}

You've closed more groups than you opened.

Such booboos are generally harmless, so keep going.

>>>>LaTeX Error: \begin{document} ended by \end{tabular}.

\end{tabular}

\begin{tabular}{|c|c|c||c|}

Your command was ignored.

Type I <command> <return> to replace it with another command,

or <return> to continue without it.

No pdf is generated. The lyx doc contains tables, and a lot of math formulae.

Any ideas?

Rds,

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[EMAIL PROTECTED]"C:/BlackScholes/\string"/}}
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\begin{document}

\title{Black Scholes Merton}

\maketitle
The standard Black-Scholes framework is extended to cope with time-dependency
of the risk-free rate, dividend and volatility parameters.

In the following,

\begin{lyxlist}{00.00.0000}
\item [{$V$}] is the value of the option being priced,
\item [{$c$}] the price of a European call
\item [{$p$}] the price of a European put
\item [{$X$}] the option's strike
\item [{$T$}] the option's time to expiration date
\item [{$S$}] the price of the contract underlying the option
\item [{$\sigma$}] the standard deviation of the continuously compounded
log returns, per sqrt of annum, can be constant or time-dependent
$\sigma(t)$
\item [{$D$}] the present value of the dividends during the life of the
option, discounted at the risk-free rate, in case the underlying contract
pays discrete dividends
\item [{$q$}] the continuously compounded dividend yield, can be constant
or time-dependent $q(t)$
\item [{$r$}] the continuously compounded risk-free rate, can be constant
or time-dependent $r(t)$
\item [{$N(.)$}] the cumulative standard normal distribution function
\item [{$n(.)$}] the standard normal distribution density function
\end{lyxlist}

\section{Partial differential equation}

$V$ value of the option

\[
\frac{\partial V}{\partial 
t}+\frac{1}{2}\sigma^{2}S^{2}\frac{\partial^{2}V}{\partial 
S^{2}}+\left[r-q\right]S\frac{\partial V}{\partial S}=rV\]


or its time-dependent equivalent\[
\frac{\partial V}{\partial 
t}+\frac{1}{2}\sigma^{2}(t)S^{2}\frac{\partial^{2}V}{\partial 
S^{2}}+\left[r(t)-q(t)\right]S\frac{\partial V}{\partial S}=r(t)V\]


In general, usual greek formulae remain valid with these replacements

$rT$ by $\int_{0}^{T}r(\tau)d\tau$

$qT$ by $\int_{0}^{T}q(\tau)d\tau$

$\sigma^{2}T$ by $\int_{0}^{T}\sigma^{2}(\tau)d\tau$

see Wilmott


\section{European call and put (upfront premium)}


\subsection{Price}

\begin{tabular}{|c|c|c||c|}
\hline 
const $\sigma$ & Discrete dividends $D$ & Continuous dividend yield $q$ & 
time-dependent $q$\tabularnewline
\hline
\hline 
$r$ & \begin{eqnarray*}
c & = & (S-D)N(d_{1})-Xe^{-rT}N(d_{2})\\
p & = & Xe^{-rT}N(-d_{2})-(S-D)N(-d_{1})\\
d_{1} & = & \frac{\ln\frac{S-D}{X}+rT+\frac{1}{2}\sigma^{2}T}{\sigma\sqrt{T}}\\
d_{2} & = & d_{1}-\sigma\sqrt{T}\end{eqnarray*}
 & \begin{eqnarray*}
c & = & Se^{-qT}N(d_{1})-Xe^{-rT}N(d_{2})\\
p & = & Xe^{-rT}N(-d_{2})-Se^{-qT}N(-d_{1})\\
d_{1} & = & 
\frac{\ln\frac{S}{X}+\left(r-q\right)T+\frac{1}{2}\sigma^{2}T}{\sigma\sqrt{T}}\\
d_{2} & = & d_{1}-\sigma\sqrt{T}\end{eqnarray*}
 & \begin{eqnarray*}
c & = & Se^{-\int_{0}^{T}q(\tau)d\tau}N(d_{1})-Xe^{-rT}N(d_{2})\\
p & = & Xe^{-rT}N(-d_{2})-Se^{-\int_{0}^{T}q(\tau)d\tau}N(-d_{1})\\
d_{1} & = & 
\frac{\ln\frac{S}{X}+rT-\int_{0}^{T}q(\tau)d\tau+\frac{1}{2}\sigma^{2}T}{\sigma\sqrt{T}}\\
d_{2} & = & d_{1}-\sigma\sqrt{T}\end{eqnarray*}
\tabularnewline
\hline 
 $r(t)$ & \begin{eqnarray*}
c & = & (S-D)N(d_{1})-Xe^{-\int_{0}^{T}r(\tau)d\tau}N(d_{2})\\
p & = & Xe^{-\int_{0}^{T}r(\tau)d\tau}N(-d_{2})-(S-D)N(-d_{1})\\
d_{1} & = & 
\frac{\ln\frac{S-D}{X}+\int_{0}^{T}r(\tau)d\tau+\frac{1}{2}\sigma^{2}T}{\sigma\sqrt{T}}\\
d_{2} & = & d_{1}-\sigma\sqrt{T}\end{eqnarray*}
 & \begin{eqnarray*}
c & = & Se^{-qT}N(d_{1})-Xe^{-\int_{0}^{T}r(\tau)d\tau}N(d_{2})\\
p & = & Xe^{-\int_{0}^{T}r(\tau)d\tau}N(-d_{2})-Se^{-qT}N(-d_{1})\\
d_{1} & = & 
\frac{\ln\frac{S}{X}+\int_{0}^{T}r(\tau)d\tau-qT+\frac{1}{2}\sigma^{2}T}{\sigma\sqrt{T}}\\
d_{2} & = & d_{1}-\sigma\sqrt{T}\end{eqnarray*}
 & \begin{eqnarray*}
c & = & 
Se^{-\int_{0}^{T}q(\tau)d\tau}N(d_{1})-Xe^{-\int_{0}^{T}r(\tau)d\tau}N(d_{2})\\
p & = & 
Xe^{-\int_{0}^{T}r(\tau)d\tau}N(-d_{2})-Se^{-\int_{0}^{T}q(\tau)d\tau}N(-d_{1})\\
d_{1} & = & 
\frac{\ln\frac{S}{X}+\int_{0}^{T}\left(r(\tau)-q(\tau)\right)d\tau+\frac{1}{2}\sigma^{2}T}{\sigma\sqrt{T}}\\
d_{2} & = & d_{1}-\sigma\sqrt{T}\end{eqnarray*}
\tabularnewline
\hline
\end{tabular}\begin{tabular}{|c|c|c||c|}
\hline 
$\sigma(t)$ & Discrete dividends $D$ & Continuous dividend yield $q$ & 
time-dependent $q$\tabularnewline
\hline
\hline 
$r$ & \begin{eqnarray*}
c & = & (S-D)N(d_{1})-Xe^{-rT}N(d_{2})\\
p & = & Xe^{-rT}N(-d_{2})-(S-D)N(-d_{1})\\
d_{1} & = & 
\frac{\ln\frac{S-D}{X}+rT+\frac{1}{2}\int_{0}^{T}\sigma^{2}(\tau)d\tau}{\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\\
d_{2} & = & d_{1}-\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}\end{eqnarray*}
 & \begin{eqnarray*}
c & = & Se^{-qT}N(d_{1})-Xe^{-rT}N(d_{2})\\
p & = & Xe^{-rT}N(-d_{2})-Se^{-qT}N(-d_{1})\\
d_{1} & = & 
\frac{\ln\frac{S}{X}+\left(r-q\right)T+\frac{1}{2}\int_{0}^{T}\sigma^{2}(\tau)d\tau}{\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\\
d_{2} & = & d_{1}-\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}\end{eqnarray*}
 & \begin{eqnarray*}
c & = & Se^{-\int_{0}^{T}q(\tau)d\tau}N(d_{1})-Xe^{-rT}N(d_{2})\\
p & = & Xe^{-rT}N(-d_{2})-Se^{-\int_{0}^{T}q(\tau)d\tau}N(-d_{1})\\
d_{1} & = & 
\frac{\ln\frac{S}{X}+rT-\int_{0}^{T}q(\tau)d\tau+\frac{1}{2}\int_{0}^{T}\sigma^{2}(\tau)d\tau}{\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\\
d_{2} & = & d_{1}-\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}\end{eqnarray*}
\tabularnewline
\hline 
$r(t)$ & \begin{eqnarray*}
c & = & (S-D)N(d_{1})-Xe^{-\int_{0}^{T}r(\tau)d\tau}N(d_{2})\\
p & = & Xe^{-\int_{0}^{T}r(\tau)d\tau}N(-d_{2})-(S-D)N(-d_{1})\\
d_{1} & = & 
\frac{\ln\frac{S-D}{X}+\int_{0}^{T}r(\tau)d\tau+\frac{1}{2}\int_{0}^{T}\sigma^{2}(\tau)d\tau}{\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\\
d_{2} & = & d_{1}-\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}\end{eqnarray*}
 & \begin{eqnarray*}
c & = & Se^{-qT}N(d_{1})-Xe^{-\int_{0}^{T}r(\tau)d\tau}N(d_{2})\\
p & = & Xe^{-\int_{0}^{T}r(\tau)d\tau}N(-d_{2})-Se^{-qT}N(-d_{1})\\
d_{1} & = & 
\frac{\ln\frac{S}{X}+\int_{0}^{T}r(\tau)d\tau-qT+\frac{1}{2}\int_{0}^{T}\sigma^{2}(\tau)d\tau}{\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\\
d_{2} & = & d_{1}-\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}\end{eqnarray*}
 & \begin{eqnarray*}
c & = & 
Se^{-\int_{0}^{T}q(\tau)d\tau}N(d_{1})-Xe^{-\int_{0}^{T}r(\tau)d\tau}N(d_{2})\\
p & = & 
Xe^{-\int_{0}^{T}r(\tau)d\tau}N(-d_{2})-Se^{-\int_{0}^{T}q(\tau)d\tau}N(-d_{1})\\
d_{1} & = & 
\frac{\ln\frac{S}{X}+\int_{0}^{T}\left(r(\tau)-q(\tau)\right)d\tau+\frac{1}{2}\int_{0}^{T}\sigma^{2}(\tau)d\tau}{\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\\
d_{2} & = & d_{1}-\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}\end{eqnarray*}
\tabularnewline
\hline
\end{tabular}


\subsection{Delta}

\begin{tabular}{|c|c||c|}
\hline 
Discrete dividends $D$ & Continuous dividend yield $q$ & time-dependent 
$q$\tabularnewline
\hline
\hline 
\begin{eqnarray*}
\Delta_{c} & = & N(d_{1})\\
\Delta_{p} & = & N(d_{1})-1\end{eqnarray*}
 & \begin{eqnarray*}
\Delta_{c} & = & e^{-qT}N(d_{1})\\
\Delta_{p} & = & e^{-qT}\left[N(d_{1})-1\right]\end{eqnarray*}
 & \begin{eqnarray*}
\Delta_{c} & = & e^{-\int_{0}^{T}q(\tau)d\tau}N(d_{1})\\
\Delta_{p} & = & 
e^{-\int_{0}^{T}q(\tau)d\tau}\left[N(d_{1})-1\right]\end{eqnarray*}
\tabularnewline
\end{tabular}


\subsection{Gamma}

\begin{tabular}{|c|c|c||c|}
\hline 
 & Discrete dividends $D$ & Continuous dividend yield $q$ & time-dependent 
$q$\tabularnewline
\hline
\hline 
$\sigma$ & \begin{eqnarray*}
\Gamma_{c}=\Gamma_{p} & = & \frac{n(d_{1})}{(S-D)\sigma\sqrt{T}}\end{eqnarray*}
 & \begin{eqnarray*}
\Gamma_{c}=\Gamma_{p} & = & 
e^{-qT}\frac{n(d_{1})}{S\sigma\sqrt{T}}\end{eqnarray*}
 & \begin{eqnarray*}
\Gamma_{c}=\Gamma_{p} & = & 
e^{-\int_{0}^{T}q(\tau)d\tau}\frac{n(d_{1})}{S\sigma\sqrt{T}}\end{eqnarray*}
\tabularnewline
\hline 
$\sigma(t)$ & \begin{eqnarray*}
\Gamma_{c}=\Gamma_{p} & = & 
\frac{n(d_{1})}{(S-D)\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\end{eqnarray*}
 & \begin{eqnarray*}
\Gamma_{c}=\Gamma_{p} & = & 
e^{-qT}\frac{n(d_{1})}{S\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\end{eqnarray*}
 & \begin{eqnarray*}
\Gamma_{c}=\Gamma_{p} & = & 
e^{-\int_{0}^{T}q(\tau)d\tau}\frac{n(d_{1})}{S\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\end{eqnarray*}
\tabularnewline
\end{tabular}


\subsection{Vega}

Only defined when the volatility is constant.

\begin{tabular}{|c|c||c|}
\hline 
Discrete dividends $D$ & Continuous dividend yield $q$ & time-dependent 
$q$\tabularnewline
\hline
\hline 
\begin{eqnarray*}
\upsilon_{c}=\upsilon_{p} & = & (S-D)n(d_{1})\sqrt{T}\end{eqnarray*}
 & \begin{eqnarray*}
\upsilon_{c}=\upsilon_{p} & = & Se^{-qT}n(d_{1})\sqrt{T}\end{eqnarray*}
 & \begin{eqnarray*}
\upsilon_{c}=\upsilon_{p} & = & 
Se^{-\int_{0}^{T}q(\tau)d\tau}n(d_{1})\sqrt{T}\end{eqnarray*}
\tabularnewline
\end{tabular}


\subsection{Theta}

We will demonstrate for the most general case:

\begin{eqnarray*}
c & = & 
Se^{-\int_{0}^{T}q(\tau)d\tau}N(d_{1})-Xe^{-\int_{0}^{T}r(\tau)d\tau}N(d_{2})\\
p & = & 
Xe^{-\int_{0}^{T}r(\tau)d\tau}N(-d_{2})-Se^{-\int_{0}^{T}q(\tau)d\tau}N(-d_{1})\\
d_{1} & = & 
\frac{\ln\frac{S}{X}+\int_{0}^{T}\left(r(\tau)-q(\tau)\right)d\tau}{\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}+\frac{1}{2}\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}\\
d_{2} & = & 
\frac{\ln\frac{S}{X}+\int_{0}^{T}\left(r(\tau)-q(\tau)\right)d\tau}{\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}-\frac{1}{2}\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}\end{eqnarray*}


\[
\Theta_{c}=-\frac{\partial c}{\partial T}=-S\frac{\partial}{\partial 
T}\left[e^{-\int_{0}^{T}q(\tau)d\tau}N(d_{1})\right]+X\frac{\partial}{\partial 
T}\left[e^{-\int_{0}^{T}r(\tau)d\tau}N(d_{2})\right]\]


\[
=-S\left[-q(T)e^{-\int_{0}^{T}q(\tau)d\tau}N(d_{1})+e^{-\int_{0}^{T}q(\tau)d\tau}n(d_{1})\frac{\partial
 d_{1}}{\partial 
T}\right]+X\left[-r(T)e^{-\int_{0}^{T}r(\tau)d\tau}N(d_{2})+e^{-\int_{0}^{T}r(\tau)d\tau}n(d_{2})\frac{\partial
 d_{2}}{\partial T}\right]\]


\[
=Sq(T)e^{-\int_{0}^{T}q(\tau)d\tau}N(d_{1})-Xr(T)e^{-\int_{0}^{T}r(\tau)d\tau}N(d_{2})-Se^{-\int_{0}^{T}q(\tau)d\tau}n(d_{1})\frac{\partial
 d_{1}}{\partial T}+Xe^{-\int_{0}^{T}r(\tau)d\tau}n(d_{2})\frac{\partial 
d_{2}}{\partial T}\]


\[
\frac{\partial d_{1}}{\partial 
T}=\frac{\left[r(T)-q(T)\right]\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}-\left[\ln\frac{S}{X}+\int_{0}^{T}\left(r(\tau)-q(\tau)\right)d\tau\right]\frac{\sigma^{2}(T)}{2\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}}{\int_{0}^{T}\sigma^{2}(\tau)d\tau}+\frac{1}{2}\frac{\sigma^{2}(T)}{2\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\]


\[
\frac{\partial d_{2}}{\partial 
T}=\frac{\left[r(T)-q(T)\right]\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}-\left[\ln\frac{S}{X}+\int_{0}^{T}\left(r(\tau)-q(\tau)\right)d\tau\right]\frac{\sigma^{2}(T)}{2\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}}{\int_{0}^{T}\sigma^{2}(\tau)d\tau}-\frac{1}{2}\frac{\sigma^{2}(T)}{2\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\]


\[
n(d_{1})=\frac{1}{\sqrt{2\pi}}e^{-\frac{d_{1}^{2}}{2}}=\frac{1}{\sqrt{2\pi}}\exp\left[-\frac{\left[\ln\frac{S}{X}+\int_{0}^{T}\left(r(\tau)-q(\tau)\right)d\tau\right]^{2}}{2\int_{0}^{T}\sigma^{2}(\tau)d\tau}\right]\exp\left[-\frac{1}{8}\int_{0}^{T}\sigma^{2}(\tau)d\tau\right]\exp\left[-\frac{1}{2}\left[\ln\frac{S}{X}+\int_{0}^{T}\left(r(\tau)-q(\tau)\right)d\tau\right]\right]\]


\[
n(d_{2})=\frac{1}{\sqrt{2\pi}}e^{-\frac{d_{2}^{2}}{2}}=\frac{1}{\sqrt{2\pi}}\exp\left[-\frac{\left[\ln\frac{S}{X}+\int_{0}^{T}\left(r(\tau)-q(\tau)\right)d\tau\right]^{2}}{2\int_{0}^{T}\sigma^{2}(\tau)d\tau}\right]\exp\left[-\frac{1}{8}\int_{0}^{T}\sigma^{2}(\tau)d\tau\right]\exp\frac{1}{2}\left[\ln\frac{S}{X}+\int_{0}^{T}\left(r(\tau)-q(\tau)\right)d\tau\right]\]


\[
=n(d_{1})\exp\left[\ln\frac{S}{X}+\int_{0}^{T}\left(r(\tau)-q(\tau)\right)d\tau\right]=n(d_{1})\frac{S}{X}e^{^{\int_{0}^{T}r(\tau)d\tau}}e^{^{-\int_{0}^{T}q(\tau)d\tau}}\]


\[
\Theta_{c}=Sq(T)e^{-\int_{0}^{T}q(\tau)d\tau}N(d_{1})-Xr(T)e^{-\int_{0}^{T}r(\tau)d\tau}N(d_{2})\]
\[
-Se^{-\int_{0}^{T}q(\tau)d\tau}n(d_{1})\frac{\partial d_{1}}{\partial 
T}+Xe^{-\int_{0}^{T}r(\tau)d\tau}n(d_{2})\frac{\partial d_{2}}{\partial T}\]


\[
=Sq(T)e^{-\int_{0}^{T}q(\tau)d\tau}N(d_{1})-Xr(T)e^{-\int_{0}^{T}r(\tau)d\tau}N(d_{2})\]


\[
-Se^{-\int_{0}^{T}q(\tau)d\tau}n(d_{1})\frac{\partial d_{1}}{\partial 
T}+Xe^{-\int_{0}^{T}r(\tau)d\tau}n(d_{1})\frac{S}{X}e^{^{\int_{0}^{T}r(\tau)d\tau}}e^{^{-\int_{0}^{T}q(\tau)d\tau}}\left[\frac{\partial
 d_{1}}{\partial 
T}-\frac{\sigma^{2}(T)}{2\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\right]\]


\[
=Sq(T)e^{-\int_{0}^{T}q(\tau)d\tau}N(d_{1})-Xr(T)e^{-\int_{0}^{T}r(\tau)d\tau}N(d_{2})\]


\[
-Se^{-\int_{0}^{T}q(\tau)d\tau}n(d_{1})\frac{\partial d_{1}}{\partial 
T}+n(d_{1})Se^{^{-\int_{0}^{T}q(\tau)d\tau}}\left[\frac{\partial 
d_{1}}{\partial 
T}-\frac{\sigma^{2}(T)}{2\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\right]\]


\[
\Theta_{c}=Sq(T)e^{-\int_{0}^{T}q(\tau)d\tau}N(d_{1})-Xr(T)e^{-\int_{0}^{T}r(\tau)d\tau}N(d_{2})-Se^{-\int_{0}^{T}q(\tau)d\tau}n(d_{1})\frac{\sigma^{2}(T)}{2\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\]


Similarily, the theta for a european put is 

\[
\Theta_{p}=-Sq(T)e^{-\int_{0}^{T}q(\tau)d\tau}N(-d_{1})+Xr(T)e^{-\int_{0}^{T}r(\tau)d\tau}N(-d_{2})-Se^{-\int_{0}^{T}q(\tau)d\tau}n(d_{1})\frac{\sigma^{2}(T)}{2\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\]



\subsection{Rho}

Only defined when the riskfree rate is constant.

\begin{tabular}{|c}
\hline 
\begin{eqnarray*}
\rho_{c} & = & XTe^{-rT}N(d_{2})\\
\rho_{p} & = & -XTe^{-rT}N(-d_{2})\end{eqnarray*}
\tabularnewline
\end{tabular}


\subsection{DDiv}

Only defined when the dividend yield is a constant.

\begin{tabular}{|c}
\hline 
\begin{eqnarray*}
\left.\frac{\partial}{\partial q}\right\rfloor _{c} & = & -STe^{-qT}N(d_{1})\\
\left.\frac{\partial}{\partial q}\right\rfloor _{p} & = & 
STe^{-qT}N(-d_{1})\end{eqnarray*}
\tabularnewline
\end{tabular}


\section{European call put (futures-style margining)}

Note that all exchange-listed options where the premium is handled
with futures-style margining are american. However, we can consider
the European case. Let $c$and $p$ be the upfront premium options,
and $C$ and $P$ the equivalent margined options.


\subsection{Price}

$C=e^{rT}c$ or $C=e^{\int_{0}^{T}r(\tau)d\tau}c$ , the same for
puts.


\subsection{Delta, Gamma, Vega, ddiv}

All these are derivatives w.r.t. a parameter that is neither $r$
nor $T$. So:

\[
\Delta_{C,P}=e^{\int_{0}^{T}r(\tau)d\tau}\Delta_{c,p}\]
\[
\Gamma_{C,P}=e^{\int_{0}^{T}r(\tau)d\tau}\Gamma_{c,p}\]
\[
\upsilon_{C,P}=e^{\int_{0}^{T}r(\tau)d\tau}\upsilon_{c,p}\]
\[
\left.\frac{\partial}{\partial q}\right\rfloor 
_{C,P}=e^{\int_{0}^{T}r(\tau)d\tau}\left.\frac{\partial}{\partial 
q}\right\rfloor _{c,p}\]



\subsection{Theta\[
\theta_{C,P}=-\frac{\partial C,P}{\partial 
T}=-\frac{\partial\left[e^{\int_{0}^{T}r(\tau)d\tau}c,p\right]}{\partial T}\]
\[
\theta_{C,P}=-r(T)e^{\int_{0}^{T}r(\tau)d\tau}c,p+e^{\int_{0}^{T}r(\tau)d\tau}\theta_{c,p}\]
\[
\theta_{C,P}=-e^{\int_{0}^{T}r(\tau)d\tau}\left[r(T)c,p-\theta_{c,p}\right]\]
\[
\theta_{C}=-e^{\int_{0}^{T}r(\tau)d\tau}\left[r(T)\left[Se^{-\int_{0}^{T}q(\tau)d\tau}N(d_{1})-Xe^{-\int_{0}^{T}r(\tau)d\tau}N(d_{2})\right]-Sq(T)e^{-\int_{0}^{T}q(\tau)d\tau}N(d_{1})+Xr(T)e^{-\int_{0}^{T}r(\tau)d\tau}N(d_{2})+Se^{-\int_{0}^{T}q(\tau)d\tau}n(d_{1})\frac{\sigma^{2}(T)}{2\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\right]\]
\[
=-e^{\int_{0}^{T}r(\tau)d\tau}\left[Sr(T)e^{-\int_{0}^{T}q(\tau)d\tau}N(d_{1})-Xr(T)e^{-\int_{0}^{T}r(\tau)d\tau}N(d_{2})-Sq(T)e^{-\int_{0}^{T}q(\tau)d\tau}N(d_{1})+Xr(T)e^{-\int_{0}^{T}r(\tau)d\tau}N(d_{2})+Se^{-\int_{0}^{T}q(\tau)d\tau}n(d_{1})\frac{\sigma^{2}(T)}{2\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\right]\]
\[
=-e^{\int_{0}^{T}r(\tau)d\tau}\left[S\left[r(T)-q(T)\right]e^{-\int_{0}^{T}q(\tau)d\tau}N(d_{1})+Se^{-\int_{0}^{T}q(\tau)d\tau}n(d_{1})\frac{\sigma^{2}(T)}{2\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\right]\]
\[
\theta_{C}=-S\left[r(T)-q(T)\right]e^{\int_{0}^{T}(r(\tau)-q(\tau))d\tau}N(d_{1})-Se^{\int_{0}^{T}(r(\tau)-q(\tau))d\tau}n(d_{1})\frac{\sigma^{2}(T)}{2\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\]
\[
\theta_{P}=-e^{\int_{0}^{T}r(\tau)d\tau}\left[r(T)\left[Xe^{-\int_{0}^{T}r(\tau)d\tau}N(-d_{2})-Se^{-\int_{0}^{T}q(\tau)d\tau}N(-d_{1})\right]+Sq(T)e^{-\int_{0}^{T}q(\tau)d\tau}N(-d_{1})-Xr(T)e^{-\int_{0}^{T}r(\tau)d\tau}N(-d_{2})+Se^{-\int_{0}^{T}q(\tau)d\tau}n(d_{1})\frac{\sigma^{2}(T)}{2\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\right]\]
\[
=-e^{\int_{0}^{T}r(\tau)d\tau}\left[Xr(T)e^{-\int_{0}^{T}r(\tau)d\tau}N(-d_{2})-Sr(T)e^{-\int_{0}^{T}q(\tau)d\tau}N(-d_{1})+Sq(T)e^{-\int_{0}^{T}q(\tau)d\tau}N(-d_{1})-Xr(T)e^{-\int_{0}^{T}r(\tau)d\tau}N(-d_{2})+Se^{-\int_{0}^{T}q(\tau)d\tau}n(d_{1})\frac{\sigma^{2}(T)}{2\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\right]\]
\[
=e^{\int_{0}^{T}r(\tau)d\tau}\left[S\left[r(T)-q(T)\right]e^{-\int_{0}^{T}q(\tau)d\tau}N(-d_{1})-Se^{-\int_{0}^{T}q(\tau)d\tau}n(d_{1})\frac{\sigma^{2}(T)}{2\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\right]\]
\[
\theta_{P}=S\left[r(T)-q(T)\right]e^{\int_{0}^{T}(r(\tau)-q(\tau))d\tau}N(d_{1})-Se^{\int_{0}^{T}(r(\tau)-q(\tau))d\tau}n(d_{1})\frac{\sigma^{2}(T)}{2\sqrt{\int_{0}^{T}\sigma^{2}(\tau)d\tau}}\]
}


\subsection{Rho}

Only defined when the riskfree rate is constant.

\begin{tabular}{|c}
\hline 
\begin{eqnarray*}
\rho_{C}=\frac{\partial C}{\partial r}=\frac{\partial}{\partial 
r}\left[e^{rT}c\right]= & 
re^{rT}\left[Se^{-qT}N(d_{1})-Xe^{-rT}N(d_{2})\right]+e^{rT}XTe^{-rT}N(d_{2})= 
& rSe^{(r-q)T}N(d_{1})+(T-r)XN(d_{2})\\
\rho_{P}=\frac{\partial P}{\partial r}=\frac{\partial}{\partial 
r}\left[e^{rT}p\right]= & 
re^{rT}\left[Xe^{-rT}N(-d_{2})-Se^{-qT}N(-d_{1})\right]-e^{rT}XTe^{-rT}N(-d_{2})=
 & (r-T)XN(-d_{2})-rSe^{(r-q)T}N(-d_{1})\end{eqnarray*}
\tabularnewline
\end{tabular}
\end{document}

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