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Keynote Presentation

Driving Innovation with MATLAB

Roy Lurie, MathWorks

In this keynote talk, Roy Lurie presents his perspectives on key technologies and trends that are creating both challenges and opportunities in computational finance. He highlights trends in computational resources, quantitative analysis, system integration, and production deployment, including MathWorks technologies that are helping to drive innovation in these areas.

Customer Presentations

Inside the Tornado with MATLAB

Martyn Dorey, CAMRADATA

This presentation looks at the role of MATLAB in CAMRADATA product delivery. We also discuss our use of MATLAB for visualisation and for communicating ideas. One example is the use of MATLAB to illustrate that the biggest threat to modern-day quantitative finance is the Pythagorean cult.


How Fennia Life Models Insurance Risk for Solvency II, Using MATLAB and the mSII Toolbox

Timo Salminen, Model IT

Insurance market regulation will take a giant leap at the beginning of 2013 when the European Union begins to enforce the Solvency II regulation. Insurance companies are required to model and price insurance contracts to the most granular detail, a major challenge for all insurers. This is particularly challenging for life insurance, where policies include many possible capital market relations and optionalities. To simplify this problem, Model IT developed mSII toolbox for MATLAB, providing companies with a simple and efficient modeling platform to calculate insurance risks. In this presentation, we demonstrate mSII and share our experiences working with Fennia Life, a Finnish insurance company that successfully ran an authority reporting test (QIS5) in October 2010 using mSII.


Basel 2 Advanced Internal Rating-Based (AIRB) Credit Risk Modeling Using MATLAB

Bart Hamers, Dexia

In this presentation, we explain why and how Dexia uses MATLAB in day-to-day modeling and data manipulation tasks. The focus is on the use of MATLAB for modeling of probability of default (PD), loss given default (LGD), and earnings at default (EaD), and how Dexia uses MATLAB for automating yearly model backtesting and Pillar 1 stress testing.


Building Models for High-Frequency Algorithmic Trading Strategies Using MATLAB

Christian Hesse, Deutsche Bank

This presentation provides an overview of how MATLAB is used at Deutsche Bank for research, development, calibration, and monitoring of quantitative models that inform high-frequency trading strategies used in the latest suite of Autobahn Equity algorithms.


MATLAB: A Corporate Development Tool at Banc Sabadell

Joan Puig, Banc Sabadell

In this presentation, we show how Banc Sabadell uses MathWorks products in the day‐to‐day operations of the trading desk. We focus on Banc Sabadell's deployment environment and how MATLAB CompilerTM enables us to reach, and cater to the specific needs of, more than a thousand users. In particular, we examine the notion of "parallel deployment" with MATLAB, and how we have sped up some of our performance-critical functions by running them in parallel. Finally, we discuss some of the ongoing projects that will allow users to define their own payoff structures "on the fly" and price them using Monte Carlo methods.


"The Prayer": A 10-Step Checklist for Advanced Risk and Portfolio Management

Attilo Meucci, Kepos Capital, LP

"The Prayer" is a recipe of 10 sequential steps for portfolio managers, risk managers, and algorithmic traders across all asset classes and all investment horizons to model and manage the P&L distribution of their positions. For each of the 10 steps of the Prayer, we introduce all the key concepts with precise notation; illustrate the key concepts by means of a simple case study; discuss multiple advanced approaches to address the nontrivial practical problems of real-life risk modeling; and highlight a nonexhaustive list of common pitfalls.


Design of Modern Forecasting and Policy Analysis Systems at Central Banks

Jaromir Benes, International Monetary Fund

In the last 15 years, a growing number of central banks have adopted a new type of monetary policy regime: inflation targeting. Unlike exchange rate management, money targeting, or other regimes, inflation targeting requires deeper understanding of economic forces that determine inflation, output, and other macroeconomic variables, and (no less importantly) new attitudes to public communication.

To facilitate economic analysis and make monetary policy decisions more transparent and accountable, many central banks have built new model-based forecasting and policy analysis systems. These involve computationally intensive tasks and require powerful software, such as MATLAB, and the domain-specific community-developed IRIS Toolbox.

This presentation describes key components of such a forecasting and policy analysis system, including the core macroeconomic model; explains why forecasters and policy-makers need to distinguish between short-term conjunctural forecasts and medium-term structural projections; and demonstrates live computer-aided experiments to illustrate typical modeling and forecasting tasks. These experiments examine, for example, the properties of a stochastic economic model, using the model to analyze recent data outturns, build consistent projections and policy scenarios, and condition model simulations upon judgmental adjustments.


Using MATLAB to Undertake Financial System Risk Analysis

Nicholas Labelle, Bank of Canada

In this presentation, we examine challenges associated with analysis of financial system risk, particularly financial market infrastructures. The presentation focuses on how Bank of Canada uses MATLAB® to examine how specific events impact intraday payment flows, what credit exposures exist in a payment system, whether central counterparties have sufficient liquidity, and how operational events can impact the financial system.

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