arXiv 3 Jun 2019 · Statistics — Applications · publishedApplied Stochastic Models in Business and Industry (2019)
arXiv:1906.01468 · PDF · DOI · OpenAlex · Extracted main text
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 paper, 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 (STN). 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Rojas H, D. D (2019) Transmission of macroeconomic shocks to risk parameters: Their uses in stress testing | 0.843 | 3 | 3 | 100% |
| 2 | Henry, J., Kok, C., Amzallag, A., Baudino, P., Cabral, I., Grodzicki… (2013) A macro stress testing framework for assessing systemic risks in the banking sector | 0.644 | 2 | 2 | 100% |
| 3 | Basu, S., Michailidis, G., et al (2015) Regularized estimation in sparse high-dimensional time series models | 0.405 | 1 | 1 | 100% |
| 4 | Chan-Lau, M. J. A (2017) Lasso regressions and forecasting models in applied stress testing | 0.405 | 1 | 1 | 100% |
| 5 | Charbonnier, C., Chiquet, J., and Ambroise, C (2010) Weighted-lasso for structured network inference from time course data | 0.405 | 1 | 1 | 100% |
| 6 | Chiquet, J (2015) Contributions to Sparse Methods for Complex Data Analysis | 0.405 | 1 | 1 | 100% |
| 7 | Davis, R. A., Zang, P., and Zheng, T (2016) Sparse vector autoregressive modeling | 0.405 | 1 | 1 | 100% |
| 8 | Dent, K., Westwood, B., and Segoviano Basurto, M (2016) Stress testing of banks: an introduction | 0.405 | 1 | 1 | 100% |
| 9 | Eichler, M (2012) Graphical modelling of multivariate time series | 0.405 | 1 | 1 | 100% |
| 10 | Granger, C. W (1980) Testing for causality: a personal viewpoint | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 18 scored citations.