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Matlab Finance Code

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GARCH,EGARCH,NAGARCH,GJR models and implicit VIX

Estimate GARCH/EGARCH/NAGARCH/GJR parameters from a time series of prices , rates and VIX  value.

FBD – “Find the Best Distribution” tool

With this GUI you can find the best distribution that fits your data.

Modeling Variable Annuities with MATLAB

This demo shows how to price variable annuity product (Guaranteed Minimum Withdrawal Benefit)

Modeling Variable Annuities with MATLAB

Pricing Guaranteed Minimum Withdrawal Benefit

Cointegration and Pairs Trading with Econometrics Toolbox

Files used in the April 14, 2011 webinar titled Cointegration and Pairs Trading with Econometrics Toolbox.

It is recomended that you watch the recording of the webinar: http://www.mathworks.com/wbnr55450

Approaches to implementing Monte Carlo methods in MATLAB

Monte Carlo methods have long been used in computational finance to solve problems where analytical solutions are not feasible or are difficult to formulate. However, these methods are computationally intensive making it challenging to implement and adopt. In the last decade, advances in hardware, increasing processor speeds and decreasing costs have made it easier to adopt Monte Carlo methods to solve numerically intensive problems. With growing access to data and demand for quicker results, researchers are constantly looking for better ways to implement algorithms using Monte Carlo methods.

In the Wilmott Magazine September 2011 article(http://www.wilmott.com/magazine.cfm), we will share some of our observations and demonstrate various ways MATLAB could be used to implement Monte Carlo methods. We take a case study of pricing Asian options and show various approaches to implementing them in MATLAB.

A draft version of the article is included in this submission.

GARCH Tool

User Interface for fitting and evaluating a generic GARCH model using the Econometrics Toolbox.

SCOPE: interactively tabulate SEER excel variables

This program asks the user for the SEER variable stored in excel, it tabultes the elements, then write it back to the excel. This facilates creation of excel tables for reporting and publication purposes. Otherwise, manually copying the tabulated results will be very tedious.

Matrix Decomposition

Matrix decomposition using, e.g. the Cholesky decomposition requires the correlation matrix to be positive definite. That is, the eigenvalues must all be positive. In finance, this is rarely the case, and one often observes negative eigenvalues, or zero eigenvalues. These two functions do essentially the same thing. One adjusts only the <= 0 eigenvalues, while the other adjusts those eigenvalues, but then also increases the other non-negative eigenvalues to compensate for the higher ‘weight’ given to the smaller eigenvalues.

Mining Economics with MATLAB

View the “Mining Economics with MATLAB” webinar at:

http://www.mathworks.com.au/company/events/webinars/wbnr73971.html

Committing to the development of a new mine involves huge amounts of capital expenditure and long time frames. In order to make optimal decisions, it is crucial to understand the future economic potential of a mine, and associated risks. Traditional spread sheet solutions, that use “average values” or “simplistic distributions”, often mean inaccurate models of the risk/return profile.

In this example we will showcase how MATLAB® can be used to develop and formalise a process for more detailed modelling of the future economic cash flow of a mine, based on historical data and producing distributions of a range of possible economic outcomes.

This example will highlight how MATLAB can be used to perform:

Net Present Value distribution analysis of a mine

Modelling and simulation of future prices and interest rate paths

Risk Assessments based on distribution of economic outcomes.

TO RUN:

Excel Version: demo_mining.xlsm (View README for steps to setup)

MATLAB Version: NPV_Analysis.m

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