In this paper, we are interested in evaluating the resilience of financial portfolios under extreme economic conditions. Therefore, we use empirical measures to characterize the transmission process of macroeconomic shocks to risk parameters. We propose the use of an extensive family of models, called General Transfer Function Models, which condense well the characteristics of the transmission described by the impact measures. The procedure for estimating the parameters of these models is described employing the Bayesian approach and using the prior information provided by the impact measures. In addition, we illustrate the use of the estimated models from the credit risk data of a portfolio.The complex relationship between series, the restricted availability of observations (usually between 20 and 30 quarters of observation), the diffuse behavior of all series (Random Walk) and the need to maintain a simple economic narrative are relevant issues to take into account when proposing models for stress testing. Case Study: Credit Risk DataCredit risk has great potential to generate losses on its assets and, therefore, has significant effects on capital adequacy. In addition, the credit risk is, possibly, the dimension of risk with the biggest bank regulation regarding stress tests. The most relevant credit risk parameters to assess resilience are: Probability of Default (PD) and Loss Given Default (LGD). Other risk parameters can be considered, but these have a definition superimposed with the parameters already mentioned. Furthermore, PD and LGD are used explicitly in the calculation of capital for a financial institution.
Bacuri (Platonia insignis Mart) is a species from the Clusiaceae genus. Its fruit pulp is commonly used in South America in several food products, such as beverages, ice cream and candies. Only the pulp of the fruit is used, and the peels and seeds are considered waste from these industries. As a trioxygenated xanthone source, this species is of high interest for bioproduct development. This work evaluated the mesocarp and epicarp of bacuri fruits through different extraction methods and experimental conditions (pH, temperature and solvent) in order to determine the most effective method for converting this agro-industrial waste in a value-added bioproduct. Open-column procedures and HPLC and NMR experiments were performed to evaluate the chemical composition of the extracts, along with total phenols, total flavonoids and antioxidant activities (sequestration of the DPPH and ABTS radicals). A factorial design and response surface methodology were used. The best extraction conditions of substances with antioxidant properties were maceration at 50 °C with 100% ethanol as solvent for mesocarp extracts, and acidic sonication in 100% ethanol for epicarp extracts, with an excellent phenolic profile and antioxidant capacities. The main compounds isolated were the prenylated benzophenones garcinielliptone FC (epicarp) and 30-epi-cambogin (mesocarp). This is the first study analysing the performance of extraction methods within bacuri agro-industrial waste. Results demonstrated that shells and seeds of bacuri can be used as phenolic-rich bioproducts obtained by a simple extraction method, increasing the value chain of this fruit.
An optimal evaluation of the resilience in financial portfolios implies having initial hypotheses about the causal influence between the macroeconomic variables and the risk parameters. In this paper, we propose a graphical model for to infer the causal structure that links the multiple macroeconomic variables and the assessed risk parameters, Stress Testing Network, in which the relationships between the macroeconomic variables and the risk parameter define a "relational graph" among their time-series, where related time-series are connected by an edge. Our proposal is based on the temporal causal models, but unlike, we incorporate specific conditions in the structure which correspond to intrinsic characteristics to this type of networks. Following the proposed model and given the high-dimensional nature of the problem, we used regularization methods to efficiently detect causality in the time-series and reconstruct the underlying causal structure. In addition, we illustrate the use of model in credit risk data of a portfolio.
An resilience optimal evaluation of financial portfolios implies having plausible hypotheses about the multiple interconnections between the macroeconomic variables and the risk parameters. In this article, we propose a graphical model for the reconstruction of the causal structure that links the multiple macroeconomic variables and the assessed risk parameters, it is this structure that we call stress testing network. In this model, the relationships between the macroeconomic variables and the risk parameter define a “relational graph” among their time‐series, where related time‐series are connected by an edge. Our proposal is based on the temporal causal models, but unlike, we incorporate specific conditions in the structure which correspond to intrinsic characteristics this type of networks. Using the proposed model and given the high‐dimensional nature of the problem, we used regularization methods to efficiently detect causality in the time‐series and reconstruct the underlying causal structure. In addition, we illustrate the use of model in credit risk data of a portfolio. Finally, we discuss its uses and practical benefits in stress testing.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
334 Leonard St
Brooklyn, NY 11211
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.