Page 251 - Special Topic Session (STS) - Volume 3
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STS 543 Luís T. D. et al.
                              Figure 2 – The new CCR data attributes



















            3.  Financial Stability and Macroprudential Policy — What’s in it for the
                New CCR?
                As discussed in the previous section, the Bank has been leveraging the
            Portuguese CCR to meet its tasks in several domains. One of the most relevant
            task entrusted to the Bank in its Organic Law is ensuring “the stability of the
            national  financial  system,  performing  for  this  purpose,  in  particular,  the
            functions of lender of last resort and national macro-prudential authority” and
            participating “in the European system for the prevention and mitigation of
            risks to financial stability and in other bodies pursuing the same goal”. To meet
            this challenge, the Bank resorts to a number of different inputs and techniques
            that allow for a systemic view of the financial system and of the build-up of
            systemic risks.
                In this context, data from the CCR are an instrumental and extensively used
            input, analysing the various dimensions and characteristics attached to the
            loans, debtors and/or creditors. Indeed, in light of its intrinsic homogeneity
            and of the possibility to compare its data with other databases, the CCR data
            allows for a complementary analysis to the “traditional” aggregate data by
            providing  the  underlying  distribution  measures  and  by  enabling  the
            enhancement of the testing and monitoring (e.g., stress testing) of the banks’
            results in ever-changing and increasingly complex scenarios.
                Indeed, Lima & Drumond (2015) discussed the insufficiencies attached to
            aggregate data when assessing financial stability and showed how microdata
            databases,  such  as  the  CCR,  enable  an  evaluation  of  the  causes  of  the
            movements behind the aggregates and thus uncover the potential build¬up
            of imbalances. Moreover, they also recognize that some macroprudential tools
            require specifically the use of characteristics that are only available in granular
            datasets – such as the collateral amount of real estate and debt instalments.



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