Konstantinos N. Pantazopoulos

Secure outsourcing of scientific computations

We investigate the outsourcing of numerical and scientific computations using the following framework: A customer who needs computations done but lacks the computational resources (computing power, appropriate software, or programming expertise) to do these locally would like to use an external agent to perform these computations. This currently arises in many practical situations, including the financial services and petroleum services industries. The outsourcing is secure if it is done without revealing to the external agent either the actual data or the actual answer to the computations. The general idea is for the customer to do some carefully designed local preprocessing (disguising) of the problem and/or data before sending it to the agent, and also some local postprocessing of the answer returned to extract the true answer. The disguise process should be as lightweight as possible, e.g., take time proportional to the size of the input and answer. The disguise preprocessing that the customer performs locally to “hide” the real computation can change the numerical properties of the computation so that numerical stability must be considered as well as security and computational performance. We present a framework for disguising scientific computations and discuss their costs, numerical properties, and levels of security. We show that no single disguise technique is suitable for a broad range of scientific computations but there is an array of disguise techniques available so that almost any scientific computation could be disguised at a reasonable cost and with very high levels of security. These disguise techniques can be embedded in a very high level, easy-to-use system (problem solving environment) that hides their complexity.

Financial prediction and trading strategies using neurofuzzy approaches

Neurofuzzy approaches for predicting financial time series are investigated and shown to perform well in the context of various trading strategies involving stocks and options. The horizon of prediction is typically a few days and trading strategies are examined using historical data. Two methodologies are presented wherein neural predictors are used to anticipate the general behavior of financial indexes (moving up, down, or staying constant) in the context of stocks and options trading. The methodologies are tested with actual financial data and show considerable promise as a decision making and planning tool.

A knowledge based system for evaluation of option pricing algorithms

Presents the design and prototype implementation of a system built around the FINANZIA system that aims in the automated analysis and classification of option pricing algorithms based on experimental data. The main objective is to assist in the generation, storage and evaluation of large amounts of experimental option pricing data and to facilitate the identification of performance properties of the pricing algorithms with respect to the various problems. The analysis of the data is achieved using statistical and inductive logic techniques and the identified properties are used to expand the knowledge base. We demonstrate the use of the system in the context of a case study covering the pricing of American vanilla options in a Black & Scholes (1973) modeling framework.

Front-Tracking Finite Difference Methods for the Valuation of American Options

This paper is concerned with the numerical solution of the American option valuation problem formulated as a parabolic free boundary/initial value model. We introduce and analyze a front-tracking finite difference method and compare it with other commonly used techniques. The numerical experiments performed indicate that the front-tracking method considered is an efficient alternative for approximating simultaneously the option value and free boundary functions associated with the valuation problem.

Neurofuzzy characterization of financial time series in an anticipatory framework

Neurofuzzy characterization of financial time series refers to the judicious application of neural and fuzzy tools to the problem of time series prediction. A methodology is presented where fuzzy if/then rules and neural predictors are used to anticipate the predictability a time series over various time horizons. The methodology is tested with actual financial time series data (S&S 500 daily closes) and shows considerable promise as a decision making and planning tool. Results in the context of option trading strategies are presented and discussed.

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 operations 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.