State-Space Approaches for Modelling and Control in Financial Engineering: Systems theory and machine learning methods (Intelligent Systems Reference Library, 125) by Rigatos, Gerasimos G.: As New Hardcover (2017) 1. Auflage

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Bibliographic Details

Title: State-Space Approaches for Modelling and Control in Financial Engineering: Systems theory and machine learning methods (Intelligent Systems Reference Library, 125)
Publisher: Springer
Publication Date: 2017
Language: English
ISBN 10: 3319528653
ISBN 13: 9783319528656
Binding: Hardcover
Edition: 1. Auflage
Condition: As New

About this title

Synopsis

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.

Coveringthe 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

From the Back Cover

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

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