\documentclass{beamer}
\usetheme[secheader]{Boadilla}

\title{Estimating Value at Risk with the Kalman Filter}
\author{Jaime Frade}
\institute[Florida State Univeristy]{
\inst{}Department of Statistics\\
Florida State Univeristy
\and
\inst{}Anuj Srivastava\\
STA5107
}
\date{\today}

\begin{document}

\frame{\titlepage}

\section[Outline]{}
\frame{\tableofcontents}


\section{Introduction}
\frame
{
\begin{center}
VaR estimation
\end{center}

\vspace{.25cm}

%\begin{figure}
%\includegraphics[scale=0.25]{example1.png} 
%\includegraphics[scale=0.25]{example2.png} 
%\caption{}
%\end{figure}
}

\subsection{Objective}
\frame
{
\begin{center}
\underline{Goal}
\end{center}
In this project, the goal is to apply one technique of risk management which measures as a percent the maximum loss which is likely to be exceeded on the portfolio, given a certain probability and time horizon within a given confidence level under assumed market conditions.  Using Value-at-Risk,  VaR, will estimate the $\beta$'s of the assets of the portfolio with a Kalman Filter.  Overall, the results showed more market sensitivity compared to historical simulation, depending on the assets of the portfolio or on the type of fund itself, for instance a hedge fund.  
\vspace{.25cm}
}



\subsection{Data of Model}
\frame
{
Time horizon: (01/02/2004- 3/12/2009).  Daily Stock last trade price for two individual companies, GE and IBM.  Also, used the S$\&$P as my market variable.  
}



\section{Simulation Results}
\frame
{
}


\section{Conclusion}
\frame
{
}

\section{Bibliography}
\frame
{
}

\end{document}

%http://www.hennesseegroup.com/releases/pitfalls.html