About Burns Statistics       Burns Statistics is the consulting and software vehicle for Patrick Burns. Specialties are programming in the S language, quantitative finance, risk management, and solving difficult optimization problems in any field via genetic algorithms and simulated annealing. Another strength is creating automatic trading programs using Apama MonitorScript. Upcoming events involving Burns Statistics History of news from Burns Statistics Experience Consulting Patrick has years experience as a consultant in the financial and medical industries. Assignments have included building GARCH modules in S, tutoring S-PLUS and statistics, automated trading, risk management, and evaluating the efficacy of medical devices. Finance In addition to his consulting experience, Patrick was an employee of Schroder Salomon Smith Barney (Citigroup) in London for four years. While there, he developed statistical models for equities -- both client-facing and proprietary. He also wrote in-house software for numerous applications including portfolio optimization and risk management. Software Development Patrick was a lead developer of S-PLUS in its early years. His tasks included adding functionality (such as principal components and robust estimation in several settings), finding and fixing bugs, and documentation. A sizable portion of the S-PLUS help files are largely attributable to him. Education In 1988 Patrick received a PhD in statistics from the University of Washington in Seattle. His thesis topic was the robust analysis of designed experiments -- that is, developing techniques that perform well even when outliers exist in the data. Some Presentations Sep 2006. “Assessing the Performance of Asset Managers” University of Bologna Summer School. Sep 2006. “Portfolio Analysis with Random Portfolios”. UKSIP, London. Jun 2006. “A Sensitivity Analysis of Non-uniformity in Random Portfolios” 12th International Conference on Computing in Economics and Finance, Cyprus. Oct 2005. “Data analysis techniques for evaluating trading strategies (or How to beat the stock market)” and “Random portfolios: some answers and questions”. CSDA Conference, Cyprus. Mar 2004. “An introduction to GARCH modelling” and “Using GARCH for Value at Risk”. Unicom seminar. London. Sep 2003. “Quantitative tools for market prediction”. Workshop associated with Quant '03. IIR. London. Nov 2002. “The modelling process for multivariate BEKK GARCH”. Quantitative Finance 2002. Risk Waters Group. London. Nov 2002. “Fundamentals and applications of univariate and multivariate GARCH”. The GARP Asset Management Forum. London. Feb 2001. “Getting Value at Risk simply and effectively with GARCH”. 2nd Annual Conference Global Association of Risk Professionals. New York. Nov 2000. “Managing the risks of using volatility forecasting and estimation techniques” and “Model selection workshop”. Applying Volatility Forecasting and Modelling Techniques. Risk Training. London. Sep 2000. “Factor models of large variance matrices”, “Choosing an effective optimization algorithm”, and “Evaluating the quality of models”. Mastering and applying financial econometrics. Risk Training, London. Aug 2000. “Multivariate GARCH: Solving hard maximum likelihood problems using a genetic algorithm”. Computational Methods in Decision-Making and Finance. University of Neuchatel, Neuchatel, Switzerland. Jun 2000. “On the right track”. Stockfacts Pro Conference. Schroder Salomon Smith Barney. London. May 2000. “Building macroeconomic factor models for equity returns.” S-PLUS Conference. MathSoft Inc. London. Mar 2000. “Constructing a macroeconomic factor model for Europe”. Integrating Risk Management into the Investment Process. IIR. London. Publications See also Working Papers. Burns, P. (2007). Bullseye. Professional Investor March issue. A very similar version is available as Dart to the Heart Burns, P. (2007). Random Portfolios for Performance Measurement. Appears in Optimisation, Econometric and Financial Analysis E. Kontoghiorghes and C. Gatu, eds. Springer. This is based on the working paper Performance Measurement via Random Portfolios but has some additional material. Burns, P. (2000). Constructing Multinational Macroeconomic Factor Models: Experience from Europe. Journal of Asset Management. 1 #2 p121-131. Burns, P. (1998). S Poetry. Burns, P., R. Engle and J. Mezrich. (1998). Correlations and Volatilities of Asynchronous Data. The Journal of Derivatives. 5 number 4. (abstract) This is derived from Discussion Paper 97-30 at the University of California, San Diego Department of Economics: 1997 Discussion Papers. Fraley, C. and P. Burns. (1995). Large-scale estimation of variance and covariance components. SIAM Journal on Scientific and Statistical Computing. 16 , number 1. Burns, P. (1991). A graphical display for choosing a transformation. Proceedings of the 23rd Symposium of the Interface: Computing Science and Statistics. p42-45. Burns, P. (1990). The L1 solution set in two-way tables. Utilitas Mathematica 37 p233-250. Go to Burns Statistics Home.