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Titlebook: Risk Management in Emerging Markets; Sima Motamen-Samadian Book 2005 Palgrave Macmillan, a division of Macmillan Publishers Limited 2005 e

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发表于 2025-3-21 19:11:43 | 显示全部楼层 |阅读模式
书目名称Risk Management in Emerging Markets
编辑Sima Motamen-Samadian
视频video
丛书名称Centre for the Study of Emerging Markets Series
图书封面Titlebook: Risk Management in Emerging Markets;  Sima Motamen-Samadian Book 2005 Palgrave Macmillan, a division of Macmillan Publishers Limited 2005 e
描述This book provides a thorough analysis of risk management in emerging markets. A collection of eight studies, each chapter examines the range of risks that investors face in an emerging market, and the methods that should be used to manage these risks. It includes the latest empirical studies on the role of insider trading and the extent of information efficiency of these markets, and a comprehensive assessment of the suitability of the Value at Risk models to emerging markets.
出版日期Book 2005
关键词emerging markets; hedging; management; risk management; value-at-risk
版次1
doihttps://doi.org/10.1057/9780230596368
isbn_ebook978-0-230-59636-8Series ISSN 2947-8006 Series E-ISSN 2947-8014
issn_series 2947-8006
copyrightPalgrave Macmillan, a division of Macmillan Publishers Limited 2005
The information of publication is updating

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Sima Motamen-Samadianon of the computer with traditional physical infrastructure can improve the efficiency of such facility-based systems. However, it increases the scope of attack from physical security to a cybersecurity perspective. Thus, it becomes critical for authorities of such systems to be able to identify the
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Tomasz Piotr Wisniewskiare used in several big data applications ranging from the generation of simple reports to running deep and complex query workloads. The insights drawn by running big data analytics depend primarily on the capabilities of the underlying query engine, which is responsible for translating user queries
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Mazin A. M. Al Janabinds of contextual and disciplinary research. In recent times, while there has been considerable research in exploring the role of big data, data analytics, and textual analytics in accounting, and auditing, we still lack evidence on what kinds of best practices academics, practitioners, and organiza
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