State-Space Approaches for Modelling and Control in Financial Engineering: Systems theory and machine learning methods (Intelligent Systems Reference Library, 125, Band 125) - Hardcover

Über die Autorin bzw. den Autor

Dr. G. Rigatos, obtained a diploma (1995) and a Ph.D. (2000) both from the Department of Electrical and Computer Engineering, of the National Technical University of Athens (NTUA), Greece. Currently he holds a Researcher position (Grade B) at the Industrial Systems Institute (Greek Secretariat for Research and Technology), on the topic of "Modelling and Control of Industrial Systems". In 2001 he was a post-doctoral researcher at the Institut de Recherche en Informatique et Systèmes Aléatoires IRISA, in Rennes France. In 2007 he was an invited professor (maître des conférences) at Université Paris XI (Institut d' Electronique Fondamentale). In 2012 he held a Lecturer Position at the Department of Engineering, of Harper-Adams University College, in Shropshire, UK on the topic of "Mechatronics and Artificial Intelligence". He has also been an adjunct professor in Greek Universities where he has taught courses on systems and control theory. His research interests include the areas of control and robotics, optimization and fault diagnosis, adaptive systems and computational intelligence. He is a member of the IEEE, IET and IMACS.

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The book conclusively solves problems associated with the control and estimation of nonlinear and chaotic dynamics in financial systems when these are described in the form of nonlinear ordinary differential equations. It then addresses problems associated with the control and estimation of financial systems governed by partial differential equations (e.g. the Black–Scholes partial differential equation (PDE) and its variants). Lastly it an offers optimal solution to the problem of statistical validation of computational models and tools used to support financial engineers in decision making.

The application of state-space models in financial engineering means that the heuristics and empirical methods currently in use in decision-making procedures for finance can be eliminated. It also allows methods of fault-free performance and optimality in the management of assets and capitals and methods assuring stability in the functioning of financial systems to be established.

Covering the following key areas of financial engineering: (i) control and stabilization of financial systems dynamics, (ii) state estimation and forecasting, and (iii) statistical validation of decision-making tools, the book can be used for teaching undergraduate or postgraduate courses in financial engineering. It is also a useful resource for the engineering and computer science community