Synopsis for Financial Time Series Analysis

Overview

Participants will receive an introduction to quantitative techniques for visualising, analysing, modelling and forecasting financial time series. The course will provide a multidisciplinary empirical approach to a variety of practical financial areas building on techniques from mathematics, statistics, physics and engineering. It will demonstrate how to model the dynamics of financial data, test for significant patterns and employ predictive signals to formulate a profitable strategy. Starting from the basics of describing the evolution of prices as a random walk, the course will present the shortfalls of traditional techniques when dealing with non-normal distributions and nonlinear relationships. Alternative investments for managing risk exposure to weather, climate, and energy will be employed to highlight these challenges. A variety of quantitative trading strategies will be explored, ranging from technical analysis to algorithmic trading.

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• Brockwell, P. J. and Davis, R. A., Introduction to time series and forecasting (2nd ed.), Springer, 2002
• Bouchaud, J.-P. & Potters, M.,Theory of Financial Risk and Derivative Pricing: From Statistical Physics to Risk Management, (2nd ed.), Cambridge University Press, 2004
• Wilmott, P., Quantitative Finance, John Wiley & Sons, 2001
• Diebold, F. X., Elements of forecasting (2nd ed.), South-Western, 2001
• Franses, P. H., Time Series Models for Business and Economic Forecasting, Cambridge University Press, 1998
• Kantz, H. and Schreiber, T., Nonlinear Time Series Analysis (2nd ed.), Cambridge University Press, 2003