Scale—Invariant Correlation Measures for Discontinuous Distributions

Hoeffding, Wassily

doi:10.1007/978-1-4612-0865-5_5

Cited by 12 publications

(6 citation statements)

References 1 publication

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“…The term copula was introduced by Sklar (1959). However, the idea of copula had previously appeared in a number of texts, most notably in Hoeffding (1940Hoeffding ( , 1941 who established best possible bounds for these functions and studied measures of dependence that are invariant under strictly increasing transformations. Relationships of copulas to other work is described in Nelsen (2006).…”

Section: Introductionmentioning

confidence: 99%

Copula Modeling: An Introduction for Practitioners

Trivedi

Zimmer

2006

FNT in Econometrics

571

493

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This article explores the copula approach for econometric modeling of joint parametric distributions. Although theoretical foundations of copulas are complex, this text demonstrates that practical implementation and estimation are relatively straightforward. An attractive feature of parametrically specified copulas is that estimation and inference are based on standard maximum likelihood procedures, and thus copulas can be estimated using desktop econometric software. This represents a substantial advantage of copulas over recently proposed simulationbased approaches to joint modeling. * The authors are grateful to the Editor Bill Greene and an anonymous reviewer for helpful comments and suggestions for improvement, but retain responsibility for the contents of the present text.

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Section: Introductionmentioning

confidence: 99%

Copula Modeling: An Introduction for Practitioners

Trivedi

Zimmer

2006

FNT in Econometrics

571

493

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“…The copula concept was not new. It is present in the work of Maurice Fréchet [15], Giorgio Dall'Aglio [6], Robert Féron [12], Wassily Hoe ding [19,20], and later Paul Deheuvels [8], Janos Galambos [17], and others. I suspect that the sequence of conferences on distributions with xed or given marginal, beginning with the one in Rome in 1990, played a role in bringing together researchers from many countries.…”

Section: Most Young Scientists Have Not Had the Chance To Meet Abe Skmentioning

confidence: 99%

My introduction to copulas

Durante

Puccetti

Scherer

et al. 2017

Dependence Modeling

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He has served on the editorial boards of two MAA journals and several of their book series.The fth interview of this series features a conversation with Roger Nelsen. His Springer book An Introduction to Copulas is eponymous for a mathematical precise and well written entrance into the fascinating eld of copulas. Moreover, it serves as a classical reference book, a large number of citations bear witness to this. He collaborated with seminal researchers in our eld, and kindly shares his view and memories with us. Roger's second strand of research -a combination of mathematical puzzles, art, and visualization -are "Proofs Without Words." He published several books, containing elegant visual justi cations of mathematical statements. In the following, our questions to Roger Nelsen are typeset in bold-face. Biographical informationWas there any particular event or person during your childhood that in uenced your choice to study mathematics?As a child I always enjoyed puzzles, especially those using arithmetic and elementary geometry. My father was an engineer, so I had a great resource for answers to my questions. But my father rarely answered my questions; he would in turn ask me questions, to enable me to more or less solve the puzzle or problem on my own. In high school I had a truly inspiring teacher. My high school o ered two levels of a one-semester calculus course (high school calculus was rare in Indiana in the 1950s), and I was in the upper level. With one semester of that high school course, I was placed into third semester calculus when I entered DePauw University. The credit belongs to that very special teacher. He passed away several years ago, and I wasn't surprised to learn that many of his students went on to earn PhDs in mathematics.

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“…The idea of copula had previously appeared in a number of papers, most notably in Hoeffding (1940Hoeffding ( , 1941 who established best possible bounds for these functions and studied measures of dependence that are invariant under strictly increasing transformations. Relationships of copulas to other work is described in Nelsen (2006).…”

Section: The Basics Copula Theorymentioning

confidence: 99%

Forecasting US real house price returns over 1831–2013: evidence from copula models

Gupta

Majumdar

2015

Applied Economics

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Given the existence of non-normality and nonlinearity in the data generating process of real house price returns over the period of 1831-2013, this paper compares the ability of various univariate copula models, relative to standard benchmarks (naive and autoregressive models) in forecasting real US house price over the annual out-of-sample period of 1859-2013, based on an in-sample of 1831-1873. Overall, our results provide overwhelming evidence in favor of the copula models (Normal, Student's t, Clayton, Frank, Gumbel, Joe and Ali-Mikhail-Huq) relative to linear benchmarks, and especially for the Student's t copula, which outperforms all other models both in terms of in-sample and out-of-sample predictability results. Our results highlight the importance of accounting for non-normality and nonlinearity in the data generating process of real house price returns for the US economy for nearly two centuries of data.

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Scale—Invariant Correlation Measures for Discontinuous Distributions

Cited by 12 publications

References 1 publication

Copula Modeling: An Introduction for Practitioners

Copula Modeling: An Introduction for Practitioners

My introduction to copulas

Forecasting US real house price returns over 1831–2013: evidence from copula models

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