Liquidity Risk Modeling using Artificial Neural Networks

Jordi Petchamé Sala

ISBN 10: 3844324976 / ISBN 13: 9783844324976

Verlag: LAP LAMBERT Academic Publishing Apr 2011, 2011

Beschreibung

Neuware -A new element of risk, the liquidity risk, has flourished along this time taking importance and playing a key role in risk management tools. This has attracted the attention of the scientific community and financial experts. Therefore, this book provides a theoretical introduction and a state of the art of the key elements needed to understand the complexity of the dealt issue. Mainly it gives a study over liquidy risk and its application in market risk (being included in VaR measure). It also explores a relatively new alternative approach to model the liquidity risk using artificial neural networks, which has been oriented in focused delay and recurrent neural networks due to their capability to work with time series. That analysis should help shed some light on this new environment and should be useful to professionals in finance.Books on Demand GmbH, Überseering 33, 22297 Hamburg 116 pp. Englisch.

Bestandsnummer des Verkäufers 9783844324976

Bibliografische Details

• Titel: Liquidity Risk Modeling using Artificial Neural Networks
• Verlag: LAP LAMBERT Academic Publishing Apr 2011
• Erscheinungsjahr: 2011
• Sprache: Englisch
• ISBN-10: 3844324976
• ISBN-13: 9783844324976
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 220B x 150 H x 7 T Millimeter Breite: 220 mm, Höhe: 150 mm, Tiefe: 7 mm
• Gewicht: 191 Gramm

Über diesen Titel

Inhaltsangabe

A new element of risk, the liquidity risk, has flourished along this time taking importance and playing a key role in risk management tools. This has attracted the attention of the scientific community and financial experts. Therefore, this book provides a theoretical introduction and a state of the art of the key elements needed to understand the complexity of the dealt issue. Mainly it gives a study over liquidy risk and its application in market risk (being included in VaR measure). It also explores a relatively new alternative approach to model the liquidity risk using artificial neural networks, which has been oriented in focused delay and recurrent neural networks due to their capability to work with time series. That analysis should help shed some light on this new environment and should be useful to professionals in finance.