MATLAB Computational Finance Conference 2014

Abstracts

These sessions provide a deep dive into workflows around a specific topic. Master classes, which are not hands-on, are most useful for people with who have experience with the products.

Event-Driven and Object-Oriented Programming with MATLAB

11:20 a.m.–12:40 p.m.
Ameya Deoras, Senior Application Engineer, MathWorks

Are you ready to take your MATLAB programming skills to the next level? Object-oriented programming in MATLAB enables you to simplify the design of complex algorithms and applications by giving you the ability to define relationships between entities, incorporate automatic error checking, greatly reduce memory requirements, and more. Furthermore, event-driven programming enables you to build applications that respond to real-time events, such as market quotes and streaming data. In this master class you will learn how to harness the power of object-oriented and event-driven programming techniques through real-world examples in complex contract pricing and real-time trading. Techniques covered include:

  • Creating classes, properties, and methods to represent entities
  • Enabling pass-by-reference semantics to improve data efficiency
  • Automating error checking and documentation generation
  • Customizing behavior of operators
  • Writing event handlers for processing streaming data

About the Speaker

Ameya Deoras

Ameya is a senior application engineer at MathWorks with a focus on computational finance. Prior to joining MathWorks in 2008, he undertook graduate research in computational gene prediction and robust speech recognition, both involving building statistical models for pattern recognition on large data sets using MATLAB. Ameya holds a B.S. in electrical engineering from the University of Illinois and an M.S. in electrical engineering from the Massachusetts Institute of Technology.

Time-Series Modeling with MATLAB

2:00–3:20 p.m.
Abhishek Gupta, Application Engineer, MathWorks

Time-series modeling techniques based on computational statistics are often used for financial analysis tasks such as forecasting, pricing complex instruments, and gaining economic insights. However, implementing and comparing modeling techniques to choose the best approach can be challenging.

In this session, you will learn about econometric modeling techniques available in MATLAB, and how to perform data preprocessing and cleanup, select models and evaluate their performance, compare results, and apply the best techniques for your problem.

Highlights include:

  • Multivariate linear regression techniques in time-series analysis
  • Automated predictor selection and cross-validation
  • Residual analysis – diagnostics for autocorrelation and heteroscedasticity
  • Dynamic model construction using autoregressive and moving average lag variables

About the Speaker

Abhishek Gupta

Abhishek is an application engineer at MathWorks focusing on machine learning and computational finance. In this role, he ensures the success of customers by building their understanding of the benefits of using different technologies for technical computing and data analytics, leveraging parallel computing paradigms, automating the workflow, and integrating multiple applications. Prior to joining MathWorks in 2007, Abhishek graduated with an M.S. in mechanical engineering from Texas A&M University, where his research constituted development of mathematical models of heat exchangers using MATLAB and Simulink.

Optimization in MATLAB

3:40–5:00 p.m.
Seth DeLand, Product Marketing Manager, MathWorks

In this session you will learn about the different tools available for optimization in MATLAB. Seth demonstrates how you can use Optimization Toolbox™ and Global Optimization Toolbox to solve a wide variety of optimization problems. You will learn best practices for setting up and solving optimization problems, as well as how to speed up optimizations with parallel computing.

Highlights include:

  • Solving linear, nonlinear, and mixed-integer optimization problems
  • Tuning solver options to increase performance
  • Using Symbolic Math Toolbox™ to automatically calculate gradients
  • Speeding up an optimization with parallel computing

About the Speaker

Seth DeLand

Seth DeLand is the product marketing manager for MATLAB optimization products. Prior to joining MathWorks, Seth earned his B.S. and M.S. in mechanical engineering from Michigan Technological University.

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