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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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書目名稱Risk Management in Emerging Markets
編輯Sima Motamen-Samadian
視頻videohttp://file.papertrans.cn/831/830683/830683.mp4
叢書名稱Centre for the Study of Emerging Markets Series
圖書封面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
關(guān)鍵詞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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