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|2 23
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|a UAMI
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100 |
1 |
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|a Wong, Max C. Y.
|q (Max Chan Yue),
|e author.
|1 https://id.oclc.org/worldcat/entity/E39PCjHw7kWxT3DhhT6Tgxkwbq
|
245 |
1 |
0 |
|a Bubble value at risk :
|b a countercyclical risk management approach /
|c Max C.Y. Wong.
|
250 |
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|a Revised edition.
|
264 |
|
1 |
|a Singapore :
|b John Wiley & Sons Singapore Pte. Ltd.,
|c [2013]
|
300 |
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|a 1 online resource (xxi, 346 pages) :
|b illustrations (black and white)
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336 |
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|a text
|b txt
|2 rdacontent
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337 |
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|a computer
|b c
|2 rdamedia
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338 |
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|a online resource
|b cr
|2 rdacarrier
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490 |
0 |
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|a Wiley Finance Series
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504 |
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|a Includes bibliographical references and index.
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505 |
0 |
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|a Bubble Value at Risk: A Countercyclical Risk Management Approach; Copyright; Contents; About the Author; Foreword; Preface; Audience; Overview of the Contents; Additional Materials; Acknowledgments; Part One: Background; Chapter 1: Introduction; 1.1: The Evolution of Riskometer; 1.2: Taleb's Extremistan; 1.3: The Turner Procyclicality; 1.4: The Common Sense of Bubble Value-at-Risk (BuVaR); Notes; Chapter 2: Essential Mathematics; 2.1: Frequentist Statistics; 2.2: Just Assumptions; i.i.d. and Stationarity; Law of Large Numbers; The Quest for Invariance; PDF and CDF; Normal Distribution.
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505 |
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|a Central Limit Theorem2.3: Quantiles, VaR, and Tails; 2.4: Correlation and Autocorrelation; Correlation; Autocorrelation; 2.5: Regression Models and Residual Errors; 2.6: Significance Tests; How to Compute t-Ratio for Regression; Hypothesis Testing; Stationarity Tests; 2.7: Measuring Volatility; 2.8: Markowitz Portfolio Theory; 2.9: Maximum Likelihood Method; 2.10: Cointegration; 2.11: Monte Carlo Method; 2.12: The Classical Decomposition; 2.13: Quantile Regression Model; 2.14: Spreadsheet Exercises; Notes; Part Two: Value at Risk Methodology; Chapter 3: Preprocessing; 3.1: System Architecture.
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505 |
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|a 3.2: Risk Factor MappingRationale for Risk Factor Mapping; Market Risks and Nonmarket Risks; Risk Dimensions; Risk Factor Universe; 3.3: Risk Factor Proxies; 3.4: Scenario Generation; Different Returns; Negative Rates; 3.5: Basic VaR Specification; The Case for Mean Adjustment; Notes; Chapter 4: Conventional VaR Methods; 4.1: Parametric VaR; Weakness of pVaR; 4.2: Monte Carlo VaR; Weakness of mcVaR; 4.3: Historical Simulation VaR; Weaknesses of hsVaR; 4.4: Issue: Convexity, Optionality, and Fat Tails; Convexity; Optionality; Fat Tails; 4.5: Issue: Hidden Correlation.
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505 |
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|a 4.6: Issue: Missing Basis and Beta ApproachBasis Risks; Beta Approach; 4.7: Issue: The Real Risk of Premiums; 4.8: Spreadsheet Exercises; Notes; Chapter 5: Advanced VaR Methods; 5.1: Hybrid Historical Simulation VaR; 5.2: Hull-White Volatility Updating VaR; 5.3: Conditional Autoregressive VaR (CAViaR); 5.4: Extreme Value Theory VaR; Classical EVT; Peaks-over-Thresholds (POT) Method; 5.5: Spreadsheet Exercises; Notes; Chapter 6: VaR Reporting; 6.1: VaR Aggregation and Limits; 6.2: Diversification; 6.3: VaR Analytical Tools; The Tail Profile; Component VaR; Incremental VaR.
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505 |
8 |
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|a 6.4: Scaling and Basel RulesBasel Rules; Time Scaling; Quantile Scaling; 6.5: Spreadsheet Exercises; Notes; Chapter 7: The Physics of Risk and Pseudoscience; 7.1: Entropy, Leverage Effect, and Skewness; 7.2: Volatility Clustering and the Folly of i.i.d.; 7.3: ""Volatility of Volatility"" and Fat Tails; 7.4: Extremistan and the Fourth Quadrant; 7.5: Regime Change, Lagging Riskometer, and Procyclicality; The Lagging Nature of VaR; Hardwired Procyclicality; 7.6: Coherence and Expected Shortfall; 7.7: Spreadsheet Exercises; Notes; Chapter 8: Model Testing; 8.1: The Precision Test.
|
520 |
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|a Introduces a powerful new approach to financial risk modeling with proven strategies for its real-world applications The 2008 credit crisis did much to debunk the much touted powers of Value at Risk (VaR) as a risk metric. Unlike most authors on VaR who focus on what it can do, in this book the author looks at what it cannot. In clear, accessible prose, finance practitioners, Max Wong, describes the VaR measure and what it was meant to do, then explores its various failures in the real world of crisis risk management. More importantly, he lays out a revolutionary new method of measuring risks, Bubble Value at Risk, that is countercyclical and offers a well-tested buffer against market crashes. Describes Bubble VaR, a more macro-prudential risk measure proven to avoid the limitations of VaR and by providing a more accurate risk exposure estimation over market cycles Makes a strong case that analysts and risk managers need to unlearn our existing "science" of risk measurement and discover more robust approaches to calculating risk capital. Illustrates every key concept or formula with an abundance of practical, numerical examples, most of them provided in interactive Excel spreadsheets. Features numerous real-world applications, throughout, based on the author's firsthand experience as a veteran financial risk analyst.
|
590 |
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|b Ebook Central Academic Complete
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650 |
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0 |
|a Risk management
|x Mathematical models.
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650 |
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|a Risk assessment
|x Mathematical models.
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650 |
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0 |
|a Financial risk management
|x Mathematical models.
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650 |
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|a Asset-liability management
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650 |
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6 |
|a Gestion du risque
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650 |
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6 |
|a Évaluation du risque
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650 |
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6 |
|a Finances
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650 |
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6 |
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650 |
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7 |
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650 |
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7 |
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650 |
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7 |
|a Risk assessment
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650 |
|
7 |
|a Risk management
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|2 fast
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758 |
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|i has work:
|a Bubble value at risk (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGQFQDfD4fWYWbHVWQfb7d
|4 https://id.oclc.org/worldcat/ontology/hasWork
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