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Titlebook: Energy Risk Modeling; Applied Modeling Met Nigel Costa Lewis Book 2005 Palgrave Macmillan, a division of Macmillan Publishers Limited 2005

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發(fā)表于 2025-3-21 20:03:03 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Energy Risk Modeling
副標(biāo)題Applied Modeling Met
編輯Nigel Costa Lewis
視頻videohttp://file.papertrans.cn/311/310395/310395.mp4
叢書名稱Finance and Capital Markets Series
圖書封面Titlebook: Energy Risk Modeling; Applied Modeling Met Nigel Costa Lewis Book 2005 Palgrave Macmillan, a division of Macmillan Publishers Limited 2005
描述Energy Risk Modeling is a primer on statistical methods for managers, students and anybody interested in the field. Illustrated through elementary and more advanced statistical Methods, it is primarily aimed at those individuals who need a gentle introduction in how to go about using statistical methods for modeling energy price risk. Statistical ideas are presented by outlining the necessary concepts and illustrating how these ideas can be implemented. This is the first energy risk book on the market to focus specifically on the role of statistical methods. Its practical approach makes the book a very useful reference and an interesting read.
出版日期Book 2005
關(guān)鍵詞Rating; Risk Management; Volatility; investments and securities
版次1
doihttps://doi.org/10.1057/9780230523784
isbn_ebook978-0-230-52378-4Series ISSN 2946-2010 Series E-ISSN 2946-2029
issn_series 2946-2010
copyrightPalgrave Macmillan, a division of Macmillan Publishers Limited 2005
The information of publication is updating

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https://doi.org/10.1007/b117251spersion. The third class known as shape statistics summarizes important elements of the shape of the underlying probability distribution implied by the sample. Our objective in using descriptive statistics is to describe as compactly as possible the key properties of empirical data. This informatio
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https://doi.org/10.1007/978-1-4471-1669-1arge. For a specific product such as the forward price of Brent Crude, or price return of an Electricity index, which of the dozens of distributions should we use? This chapter outlines the process by which the practicing risk manager can begin to answer this question. It starts by assessing the val
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Statical and Geomechanical Modelser hand, the closeness of the two indices is defined in terms of a correlation coefficient, then the manager at least has some rudimentary way of assessing whether or not the relationship exists and its strength.
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Introduction to Applied Probability for Energy Risk Managementviduals purchase lottery tickets even though the likelihood of wining a very large pay-out is extremely small. If we say that the probability of snow today is one-half, but tomorrow it is only one quarter, we know that snow is more likely today than tomorrow.
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Modeling and Fitting Price Distributionsarge. For a specific product such as the forward price of Brent Crude, or price return of an Electricity index, which of the dozens of distributions should we use? This chapter outlines the process by which the practicing risk manager can begin to answer this question. It starts by assessing the val
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Nonparametric Density Estimation for Energy Price Returns
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Stiffness Changes and Reanalysis,ct more than one independent variable to influence the dependent variable. It allows us to explore the relationship between several independent and a single dependent variable. We also discuss multivariate regression which arises when we have several dependent variables dependent on the same (or some subset) independent variables.
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