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State-space approaches for modelling...

Rigatos, Gerasimos G.

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State-space approaches for modelling and control in financial engineering = systems theory and machine learning methods /

Record Type:	Electronic resources : Monograph/item
Title/Author:	State-space approaches for modelling and control in financial engineering/ by Gerasimos G. Rigatos.
Reminder of title:	systems theory and machine learning methods /
Author:	Rigatos, Gerasimos G.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xxviii, 310 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Financial engineering - Mathematics. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-52866-3
ISBN:	9783319528663

State-space approaches for modelling and control in financial engineering = systems theory and machine learning methods /
Rigatos, Gerasimos G.

State-space approaches for modelling and control in financial engineeringsystems theory and machine learning methods /[electronic resource] :by Gerasimos G. Rigatos. - Cham :Springer International Publishing :2017. - xxviii, 310 p. :ill. (some col.), digital ;24 cm. - Intelligent systems reference library,v.1251868-4394 ;. - Intelligent systems reference library ;v.125..

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.

ISBN: 9783319528663

Standard No.: 10.1007/978-3-319-52866-3doiSubjects--Topical Terms:

888441
Financial engineering
--Mathematics.

LC Class. No.: HG176.7

Dewey Class. No.: 658.15

State-space approaches for modelling and control in financial engineering = systems theory and machine learning methods /
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520 $a 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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