Automating the analysis of option pricing algorithms through intelligent knowledge acquisition approaches

The traditional approach for estimating the performance of numerical methods is to combine an operation's count with an asymptotic error analysis. This analytic approach gives a general feel of the comparative efficiency of methods, but it rarely leads to very precise results. It is now recognized that accurate performance evaluation can be made only with actual measurements on working software. Given that such an approach requires an enormous amount of performance data related to actual measurements, the development of novel approaches and systems that intelligently and efficiently analyze these data is of great importance to scientists and engineers. The paper presents intelligent knowledge acquisition approaches and an integrated prototype system, which enables the automatic and systematic analysis of performance data. The system analyzes the performance data which is usually stored in a database with statistical, and inductive learning techniques and generates knowledge which can be incorporated in a knowledge base incrementally. We demonstrate the use of the system in the context of a case study, covering the analysis of numerical algorithms for the pricing of American vanilla options in a Black and Scholes modeling framework. We also present a qualitative and quantitative comparison of two techniques used for the automated knowledge acquisition phase.