1© 2012 The MathWorks, Inc.

Computational Finance with MATLAB

MathWorks Computational Finance Team

2

Challenges in Financial Analysis

Volatile markets

– Ever-changing needs

Lack of computing power

– Large data, large models

Increased transparency

– More auditing and regulation

– More sharing with colleagues

ManagementTraders

Quant Group

Financial

Engineer

Regulators Clients Partners

Other groups

3

Who uses MATLAB in Financial Services?

The top 15 asset-

management companies

The top 10 U.S. commercial

banks

11 of the top 15 hedge funds

The reserve banks of all

OECD member countries

The top 3 credit rating

agencies

4

MATLAB Application Areas in Finance

Investment Management

Risk Analysis

Insurance

Economics/Econometrics

Trading

Pricing & Valuation

More details & user stories at mathworks.com/financial-services

5

Credit Risk Analysis

Assess the Credit Value-at-

Risk of a bond portfolio

– 1311 different obligors

– 12 different industries

– Maturities in 1 to 4 years

Compute credit ratings

Model credit transitions

Simulate credit events &

analyze portfolio losses

93.68%

2.44%

0.14%

0.03%

0.03%

0.00%

0.00%

0.00%

5.55%

92.60%

4.18%

0.23%

0.12%

0.00%

0.00%

0.00%

0.59%

4.03%

91.02%

7.49%

0.73%

0.11%

0.00%

0.00%

0.18%

0.73%

3.90%

87.86%

8.27%

0.82%

0.37%

0.00%

0.00%

0.15%

0.60%

3.78%

86.74%

9.64%

1.84%

0.00%

0.00%

0.00%

0.08%

0.39%

3.28%

85.37%

6.24%

0.00%

0.00%

0.00%

0.00%

0.06%

0.18%

2.41%

81.88%

0.00%

0.00%

0.06%

0.08%

0.16%

0.64%

1.64%

9.67%

100.00%

AAA AA A BBB BB B CCC D

AAA

AA

A

BBB

BB

B

CCC

D

6

Traditional Approach Challenge

Challenges Faced During Model Development

Off-the-shelf software Inability to customize

Third-party consulting Lack of transparency

Spreadsheets, ExcelSubpar computational

speed, limited data size

In-house development with

traditional languagesLong development time

Combination of the aboveInefficiencies in

Integration & Automation

7

Computational Finance Workflow

Research and Quantify

Data Analysis

& Visualization

Financial

Modeling

Application

Development

Reporting

Applications

Production

Share

Automate

Files

Databases

Datafeeds

Access

8

Excel spreadsheets and flat files

ODBC or JDBC compliant databases

Data feeds including Bloomberg, Reuters, Factset®, eSignal® and others

Web services (SOAP)

Connect to Data from Various Sources

9

Mathematics Regression (Linear, Non-Linear)

Curve Fitting

Probability Distributions, RNG

Clustering

Multivariate & Factor Analysis

Predictive Modeling, AI

Optimization, Parameter Estimation

Leverage Built-in Functions to Save Time

Financial Modeling Portfolio Optimization & Analysis

Derivative Pricing & Hedging

Yield Curve Modeling

Monte Carlo Simulation

Risk Quantification

ARMA/GARCH Analysis

10

CalPERS Analyzes Currency

Market Dynamics to Identify

Intraday Trading Opportunities

ChallengeDevelop an intraday trading model for currency

markets

SolutionUse MATLAB and companion toolboxes to quantify

market dynamics by analyzing millions of rows of

historical market data

Results Development time reduced by months

Profitable market dynamic identified

Market insight gained

“My expertise is in finance, not

programming. To perform sophisticated

analysis on vast amounts of data, I

needed software that was easy to use

and included many of the functions I

needed. With MATLAB I can do

everything in one environment, and that

is a real benefit.”

Omid Rezania

CalPERS

CalPERS intraday currency volatility analysis.

Link to user story

11

Traditional Approach Challenge

Off-the-shelf software Inability to customize

Third-party consulting Lack of transparency

Spreadsheets, ExcelSubpar computational

speed, limited data size

In-house development with

traditional languagesLong development time

Combination of the aboveInefficiencies in

Integration & Automation

Challenges Faced During Model Development

12

Traditional Approach Challenge

Off-the-shelf software Inability to customize

Third-party consulting Lack of transparency

Spreadsheets, ExcelSubpar computational

speed, limited data size

In-house development with

traditional languagesLong development time

Combination of the aboveInefficiencies in

Integration & Automation

Challenges Faced During Model Development

13

Challenge Solution

Inability to customizeFlexible modeling

Complete development environment

Lack of transparencyWhite-box modeling

Viewable-source functions

Subpar computational

speedsPowerful computation engine

Run fast Monte-Carlo simulations

Long development timeQuick prototyping

Focus on modeling not programming

Inefficiencies in

IntegrationEasy to Integrate & Deploy

Point-and-click workflow

MATLAB Solutions

14

Finance Application Examples

Economic Modeling &

Forecasting

Cash-flow Hedging &

Scenario Analysis

Credit Value-at-Risk

More focused webinars available at mathworks.com/recordedwebinars

15

Computational Finance Workflow

Financial

Statistics Optimization

Fixed Income Financial Derivatives Econometrics

MATLAB

Parallel Computing MATLAB Distributed Computing Server

Files

Databases

Datafeeds

Access

Reporting

Applications

Production

Share

Data Analysis and Visualization

Financial Modeling

Application Development

Research and Quantify

Datafeed

Database

Spreadsheet Link EX

Builder NE

Builder EX

Builder JA

MATLAB Compiler

Re

po

rt Ge

ne

rato

r

16

Support and Community

17

Training

Three ways to get training

– Public training

Offered throughout the world

Schedule and course information at www.mathworks.in/training

– Onsite training

Bring training to your site, with course customization available

– Web-based training

Instructor-led e-learning

Train at work or at home, with flexible dates and times

Example course topics

– Introductory and intermediate training on MATLAB, Simulink, Stateflow,

and Real-Time Workshop

– Specialized courses in control design, signal processing, parallel computing,

code generation, communications, financial analysis, and other areas

www.mathworks.in/training

18

Technical Support

Resources

Over 100 support engineers

– All with MS degrees (EE, ME, CS)

– Local support in North America,

Europe, and Asia

Comprehensive, product-specific Web

support resources

High customer satisfaction

95% of calls answered

within three minutes

70% of issues resolved

within 24 hours

80% of customers surveyed

rate satisfaction at 80-100%

www.mathworks.in/support