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portada State-Space Approaches for Modelling and Control in Financial Engineering: Systems Theory and Machine Learning Methods (Intelligent Systems Reference Library)
Type
Physical Book
Publisher
Author
Year
2017
Language
English
Pages
310
Format
Hardcover
ISBN13
9783319528656
Edition No.
1
Categories

State-Space Approaches for Modelling and Control in Financial Engineering: Systems Theory and Machine Learning Methods (Intelligent Systems Reference Library)

Gerasimos G. Rigatos (Author) · Springer · Hardcover

State-Space Approaches for Modelling and Control in Financial Engineering: Systems Theory and Machine Learning Methods (Intelligent Systems Reference Library) - Gerasimos G. Rigatos

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Synopsis "State-Space Approaches for Modelling and Control in Financial Engineering: Systems Theory and Machine Learning Methods (Intelligent Systems Reference Library) "

The book conclusively solves problems associated with the control and estimation of nonlinear and chaotic dynamics in ?nancial systems when these are described in the form of nonlinear ordinary di?erential equations. It then addresses problems associated with the control and estimation of ?nancial systems governed by partial di?erential 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 ?nancial engineers in decision making.The application of state-space models in ?nancial engineering means that the heuristics and empirical methods currently in use in decision-making procedures for ?nance 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 ?nancial systems to be established.The estimation, forecasting and control methods can be applied not only to ?nancial systems described by nonlinear ordinary di?erential equations, but also to ?nancial systems exhibiting spatiotemporal dynamics. Further, they make a valuable contribution to the statistical validation of decision-making tools used in ?nancial engineering.Covering the following key areas of ?nancial engineering: (i) control and stabilization of ?nancial 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 ?nancial engineering. It is also a useful resource for the engineering and computer science community

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