Browsing by Subject "correlation"

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    Time-Varying Systemic Risk: Evidence from a Dynamic Copula Model of CDS Spreads
    (Economic Research Initiatives at Duke (ERID) Working Paper, 2013-05-23) Oh, DH; Patton, AJ
    This paper proposes a new class of copula-based dynamic models for high dimension conditional distributions, facilitating the estimation of a wide variety of measures of systemic risk. Our proposed models draw on successful ideas from the literature on modeling high dimension covariance matrices and on recent work on models for general time-varying distributions. Our use of copula-based models enable the estimation of the joint model in stages, greatly reducing the computational burden. We use the proposed new models to study a collection of daily credit default swap (CDS) spreads on 100 U.S. firms over the period 2006 to 2012. We find that while the probability of distress for individual firms has greatly reduced since the financial crisis of 2008-09, the joint probability of distress (a measure of systemic risk) is substantially higher now than in the pre-crisis period.
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    Using Model Analysis to Unveil Hidden Patterns in Tropical Forest Structures
    (Frontiers in Ecology and Evolution, 2021-11-30) Picard, N; Mortier, F; Ploton, P; Liang, J; Derroire, G; Bastin, JF; Ayyappan, N; Bénédet, F; Boyemba Bosela, F; Clark, CJ; Crowther, TW; Engone Obiang, NL; Forni, É; Harris, D; Ngomanda, A; Poulsen, JR; Sonké, B; Couteron, P; Gourlet-Fleury, S
    When ordinating plots of tropical rain forests using stand-level structural attributes such as biomass, basal area and the number of trees in different size classes, two patterns often emerge: a gradient from poorly to highly stocked plots and high positive correlations between biomass, basal area and the number of large trees. These patterns are inherited from the demographics (growth, mortality and recruitment) and size allometry of trees and tend to obscure other patterns, such as site differences among plots, that would be more informative for inferring ecological processes. Using data from 133 rain forest plots at nine sites for which site differences are known, we aimed to filter out these patterns in forest structural attributes to unveil a hidden pattern. Using a null model framework, we generated the anticipated pattern inherited from individual allometric patterns. We then evaluated deviations between the data (observations) and predictions of the null model. Ordination of the deviations revealed site differences that were not evident in the ordination of observations. These sites differences could be related to different histories of large-scale forest disturbance. By filtering out patterns inherited from individuals, our model analysis provides more information on ecological processes.