[2] G. Rigatos,
State-Space Approaches for Modelling and Control in Financial Engineering:
Systems Theory and Machine Learning Methods, Springer, 2016 (monograph) download
Ref No 5610 State-space approaches for modelling and control in financial engineering: systems
theory and machine learning approaches, Duration: 2016- 2017 . The project has solved in a conclusive manner 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. Moreover, it has solved in a conclusive manner
problems associated with the control and estimation of financial systems governed by partial differential equations
(e.g. the Black-Scholes PDE and its variants). Finally, the project has solved in a conclusive and optimal manner
the problem of statistical validation of computational models and tools used to support decision taking by agents
involved in electricity. The application of state-space models in financial systems, including also electricity
markers, allows to eliminate heuristics and empirical methods currently in use for such a purpose. On the other
side it permits to establish methods of fault-free performance and optimality in the management of assets and
capitals and methods assuring stability in the functioning of these financial systems.
