The Information in Systemic Risk Rankings

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractWe propose to pool alternative systemic risk rankings for financial institutions using the method of principal components. The resulting overall ranking is less affected by estimation uncertainty and model risk. We apply our methodology to disentangle the common signal and the idiosyncratic components from a selection of key systemic risk rankings that are recently proposed. We use a sample of 113 listed financial sector firms in the European Union over the period 2002-2013. The implied ranking from the principal components is less volatile than most individual risk rankings and leads to less turnover among the top ranked institutions. We also find that price-based rankings and fundamentals based rankings deviated substantially and for a prolonged time in the period leading up to the financial crisis. We test the adequacy of our newly pooled systemic risk ranking by relating it to credit default swap premia.

Atheoretical Regression Trees for classifying risky financial institutions

“…For each month the original data are collected in a N × 6 matrix on which the PCA is performed. Specifically, we extract the first PC which explains between 42 and 60% of the total variance for each period, a result consistent with Nucera et al (2016). Then the first PC on average explains at least more then 50% of the information on risk collected by the 6 indicators.…”

“…Specifically, we perform the PCA on the monthly correlation matrix of the original numerical values. In doing so we depart from Nucera et al (2016). Indeed for each indicator they turn the observed data into ranks for the selected units and then they perform the PCA on the resulting transformed data.…”

“…Following the work by Nucera et al 2016 The risk measures that we consider are derived following different procedures and they take into account different dimension of the riskiness of the financial institutions involved. We provide below a short description of all the measures, we refer to Nucera et al (2016) for more details and to the vLab site for technical aspects.…”

“…In more recent contributions, Giglio et al (2016), use conditional quantile factor model to obtain systemic risk indices. The work more closely related to ours is Nucera et al (2016). They apply the factor analysis across rankings in the cross-section dimension, at each point in time, to form optimal combinations of rankings.…”

A Multi-Agent Methodology to Assess the Effectiveness of Alternative Systemic Risk Adjusted Capital Requirements

“…This may be possibly be because SRISK is a combination of market and balance sheet metrics and as such not purely a market-based measure given the inclusion of leverage. Kleinow et al (2017) empirically Nucera et al (2016) and Giglio et al (2016) both apply principal component analysis to a range of systemic risk measures in the attempt to capture the multiple aspects of systemic risk. A useful discussion on the diculty in nding a measure that can capture all aspects of systemic risk can be found in Hansen (2013).…”