MATLAB Computational Finance Conference 2014
With a web interface, input your portfolio specifications and let the MATLAB engine optimize the portfolio, perform asset allocation, and calculate risk measures. The example shows how MATLAB applications can be deployed and scaled conveniently to serve many customers and colleagues simultaneously.
Fraud detection is an important problem to address both from regulatory and investor perspectives. This demonstration highlights how quantitative algorithms developed in MATLAB can be used to help flag potential irregularities or fraudulent managers. Machine learning methods are used to identify candidate funds demonstrating fraudulent characteristics.
With R2014a, Optimization Toolbox includes a significant new Mixed-Integer Linear Optimization solver, applicable to problems such as cash-flow matching, portfolio rebalancing, resource allocation, index replication, and energy scheduling. This demonstration shows an index replication example, also offering thoughts on general solver performance.
This example demonstrates computing the counterparty credit risk for a portfolio of swaps using a two-factor Hull-White model to generate interest rate scenarios. Different counterparty exposures are calculated and the credit value adjustment for each instrument and the portfolio is computed using probabilities of default calibrated from market credit default swap (CDS) quotes.
This demonstration shows a series of tasks associated with a CDS contract pricing workflow, such as estimating default probability term structures and hazard rates, calculating breakeven spreads for new contracts, and value existing CDS contracts. MathWorks experts are also available to discuss new instrument functionality such as dual curve construction, functions to compute credit exposure and exposure profile, and Black’s model pricing of caps, floors, and swap options.
Examining multiple trading strategies within a single trading framework? If so, having a convenient strategy container, or object, into which you can swap in/swap out different strategies, ensuring consistent data input, analysis, and back-testing of the strategies, offers significant benefits. This example demonstrates how object-oriented programming capabilities in MATLAB can offer an easy and scalable trading strategy development capability.
Other demos will demonstrate trading, econometric analysis including state-space modelling, credit risk, cash-flow modelling, portfolio replication, and more.