Book review: quantitative risk management: concepts, techniques and tools, revised edition, by A.F. McNeil, R. Frey and P. Embrechts. Princeton University Press, 2015, ISBN 978-0-691-16627-8, xix +…

By Chen Zhou. Extremes, 2017

973 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/59d368cf8fdf7be506973933cce28f6a79802850

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Citations in the corpus

Bivariate simulation using copula and its application to probabilistic pile settlement analysis (2013, methodology): “, [6]) f y1; y2 ð Þ 1⁄4 @ (2)CðF1 y1 ð Þ; F2ðy2ÞÞ @F1 y1 ð Þ@F2ðy2Þ f1 y1 ð Þf2 y2 ð Þ 1⁄4 c u1; u2Þf1 y1 ð Þf2 y2 ð Þ ð (8) in which c(u1, u2) is the copula density associated with the copula function C(u1, u2).”
Total loss estimation using copula-based regression models (2012, methodology): “Since then, copulae have been used, e.g., for the modeling of bivariate loss distributions by Klugman and Parsa (1999) and of dependencies between loss triangles by De Jong (2009), as well as for risk management (see McNeil et al. (2005)).”
Impact of sample size on geotechnical probabilistic model identification (2017, methodology): “Similarly, the copula parameters h for the four copulas can be estimated using the method of moments and MLE.”
Tail Risk of Multivariate Regular Variation (2011, background): “, [28] for details), and risk %(X) for loss X corresponds to the amount of extra capital requirement that has to be invested in some secure instrument so that the resulting position %(X)−X is acceptable to regulators/supervisors.”
Maximum likelihood estimation of skew-t copulas with its applications to stock returns (2018, background): “ARTICLE HISTORY Received 28 April 2017 Accepted 23 April 2018 KEYWORDS Skew-t distribution; copula; maximum likelihood estimation; tail asymmetry; tail dependence; generalized hyperbolic distribution AMS SUBJECT CLASSIFICATIONS 62E17; 62H10; 62H20; 65C60”
Uncertainty and Crude Oil Returns (2016, methodology): “As documented, for example, by Jondeau and Rockinger (2006a), Junker et al. (2006), Luciano and Marena (2003), and McNeil et al. (2005), the widely used measure of dependence, the Pearson correlation coeffi cient, may not appropriately describe the type of dependence between returns and, as a…”
Bayesian identification of random field model using indirect test data (2016, methodology): “This section applies copula theory to re-construct PðXjξ̂;MkÞ based on MCMCS samples of X. Copula theory provides a rational tool to construct multivariate distributions according to multivariate data (e.g., McNeil et al., 2005; Nelsen, 2006; Au and Wang, 2014; Li et al., 2015b; Tang et al., 2015).”
A critical review on supply chain risk – Definition, measure and modeling ☆ (2015, background): “In recent decades, we have observed this term being applied to areas such as decision theory [75,119], finance [63,64,92], actuarial science [82], health care [53,77], marketing [34,120], management [90], emergency planning [33,57,60] and psychology [2,20,38].”
Sequential Bayesian Model Selection of Regular Vine Copulas (2015, methodology): “This property makes copulas a very convenient tool to be used by statistical modelers (Nelsen (2006); McNeil et al. (2005); Kurowicka and Cooke (2006); Kurowicka and Joe (2010)).”
Bivariate distribution models using copulas for reliability analysis (2013, methodology): “When the copula parameters u are known, the probabilities of failure can be evaluated efficiently using equation (16).”
Multinomial VaR Backtests: A simple implicit approach to backtesting expected shortfall (2016, methodology): “The different forecasting methods considered are listed below; for more details of the methodology, see McNeil et al. (2015), Chapter 9.”
Model-Based Clustering (2016, background): “McNeil, Frey, and Embrechts (2005) note that a random variableX following the generalized hyperbolic distribution can be represented via the relationship in (13) withW following a generalized inverse Gaussian distribution.”
Using wavelets for time series forecasting: Does it pay off? (2010, methodology): “For determining the lag-order (p, q) of the process, methods like overfitting or measures like the Bayesian information criterion, that punishes a high number of variables, can be used (see McNeil et al., 2005).”