Combining permutation tests to rank systemically important banks
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Combining permutation tests to rank systemically important banks Lorenzo Frattarolo1 · Francesca Parpinel1
· Claudio Pizzi1
Accepted: 12 October 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract In this work we propose the use of a nonparametric procedure to investigate the relationship between the Regulator’s Global Systemically Important Banks (G-SIBs) classification and the equity-based systemic risk measures. The proposed procedure combines several permutation tests to investigate the equality of the multivariate distribution of two groups and assumes only the hypothesis of exchangeability of variables. In our novel approach, the weights used in the combination of tests are obtained using the Particle Swarm Optimization heuristic and quantify the informativeness about the selection. Finally, the p value of the combined test measures the reliability of the result. Empirical results about the selection of G-SIBs show how considering the systematic (β), stress (ΔCoVaR) and connectedness components (in–out connection) of systemic risk cover more than 70% of weight in all the considered years. Keywords Systemic risk · Global Systemically Important Bank · Particle Swarm Optimization · Permutation tests
1 Introduction The framework of Systemically Important Financial Institutions (SIFIs) was introduced by the Financial Stability Board (FSB) in October of 2010; these institutions were defined as those “whose disorderly failure, because of their size, complexity and systemic interconnectedness would cause significant disruption to the wider financial
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Claudio Pizzi [email protected] Lorenzo Frattarolo [email protected] Francesca Parpinel [email protected]
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Department of Economics, Ca’ Foscari University of Venice, San Giobbe Cannaregio 873, 30121 Venice, Italy
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L. Frattarolo et al.
system and economic activity” FSB (2010). The methodology to select which Globally Systemically Important Banks (G-SIBs) should have additional capital requirements according to the Basel III agreement is outlined by the Basel Committee on Banking Supervision (BCBS), BIS (2013). The Regulator builds the selection process on annual data disclosed by banks of member countries. The collection of the dataset is complex and time-consuming, furthermore the selection is usually published one year later. The use of slow varying balance sheet annual data, on the one hand, increases the stability of the ranking, but on the other hand, it cannot promptly detect sudden changes. Such a delay could result in sub-optimal monitoring of institutions and inefficient policies to mitigate the systemic risk. In this respect, several works have proposed systemic risk measures based on market data; for a comprehensive review see Silva et al. (2017) and Benoit et al. (2017), which show what scholars and practitioners use to rank the systemic importance of each institution in a timely manner. However, according to Silva et al. (2017), many published articles are focused on specific measures and consider different samp
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