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Titlebook: Decision Technologies for Computational Finance; Proceedings of the f Apostolos-Paul N. Refenes,Andrew N. Burgess,John E Conference proceed

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發(fā)表于 2025-3-21 17:20:58 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Decision Technologies for Computational Finance
副標(biāo)題Proceedings of the f
編輯Apostolos-Paul N. Refenes,Andrew N. Burgess,John E
視頻videohttp://file.papertrans.cn/265/264307/264307.mp4
叢書名稱Advances in Computational Management Science
圖書封面Titlebook: Decision Technologies for Computational Finance; Proceedings of the f Apostolos-Paul N. Refenes,Andrew N. Burgess,John E Conference proceed
描述This volume contains selected papers that were presented at the International Conference COMPUTATIONAL FINANCE 1997 held at London Business School on December 15-17 1997. Formerly known as Neural Networks in the Capital Markets (NNCM), this series of meetings has emerged as a truly multi-disciplinary international conference and provided an international focus for innovative research on the application of a multiplicity of advanced decision technologies to many areas of financial engineering. It has drawn upon theoretical advances in financial economics and robust methodological developments in the statistical, econometric and computer sciences. To reflect its multi-disciplinary nature, the NNCM conference has adopted the new title COMPUTATIONAL FINANCE. The papers in this volume are organised in six parts. Market Dynamics and Risk, Trading and Arbitrage strategies, Volatility and Options, Term-Structure and Factor models, Corporate Distress Models and Advances on Methodology. This years‘ acceptance rate (38%) reflects both the increasing interest in the conference and the Programme Committee‘s efforts to improve the quality of the meeting year-on-year. I would like to thank the me
出版日期Conference proceedings 1998
關(guān)鍵詞Arbitrage; capital market; controlling; dynamics; econometrics; finance; investment; learning; modeling; netw
版次1
doihttps://doi.org/10.1007/978-1-4615-5625-1
isbn_softcover978-0-7923-8309-3
isbn_ebook978-1-4615-5625-1Series ISSN 1388-4301
issn_series 1388-4301
copyrightSpringer Science+Business Media Dordrecht 1998
The information of publication is updating

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https://doi.org/10.1007/978-3-319-57363-2, and that at any rate much of the potential of EVT remains latent. We substantiate this claim by sketching a number of pitfalls associated with use of EVT techniques. More constructively, we show how certain of the pitfalls can be avoided, and we sketch a number of explicit research directions that will help the potential of EVT to be realized
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Patrick Oloko,Michaela Ott,Clarissa Vierkest traders, the dynamics looks similar to noisy low-dimensional chaos. By introducing traders memory and/or feedback between global and individual wealth fluctuations, one obtains auto-correlations in the time evolution of the “volatility” as well as market booms and crashes.
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發(fā)表于 2025-3-22 13:40:35 | 只看該作者
https://doi.org/10.1057/978-1-349-93358-7s empirical illustrations we consider the term structure of interest rates and the relationship between common and preferred stock prices. It turns out that for this applications there is only weak evidence for a nonlinear long run relationship.
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發(fā)表于 2025-3-22 19:40:46 | 只看該作者
Stability Analysis and Forecasting Implicationse relationship is shown. There are gains in the forecast performance when considering the empirical model for the rolling estimators, jointly with the initial structural model. .: Recursive Estimators, Rolling Estimators, Recursive Sequential Test, Monte Carlo Methods, Campbell-Grossman-Wang model.
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Stochastic Lotka-Volterra Systems of Competing Auto-Catalytic Agents Lead Generically to Truncated Pst traders, the dynamics looks similar to noisy low-dimensional chaos. By introducing traders memory and/or feedback between global and individual wealth fluctuations, one obtains auto-correlations in the time evolution of the “volatility” as well as market booms and crashes.
9#
發(fā)表于 2025-3-23 01:54:04 | 只看該作者
Nonparametric Tests for Nonlinear Cointegrations empirical illustrations we consider the term structure of interest rates and the relationship between common and preferred stock prices. It turns out that for this applications there is only weak evidence for a nonlinear long run relationship.
10#
發(fā)表于 2025-3-23 08:32:42 | 只看該作者
Conference proceedings 1998December 15-17 1997. Formerly known as Neural Networks in the Capital Markets (NNCM), this series of meetings has emerged as a truly multi-disciplinary international conference and provided an international focus for innovative research on the application of a multiplicity of advanced decision techn
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