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.

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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.