Systemic Risk and the Macroeconomy: An Empirical Evaluation

Giglio, Stefano; Kelly, Bryan T.; Pruitt, Seth

doi:10.2139/ssrn.2158347

Cited by 62 publications

(69 citation statements)

References 22 publications

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“…We do not obtain estimates of factors in a time series context. For a time series approach to 'systemic risk' we refer to Moreno and Pena (2013) and Giglio, Kelly, and Pruitt (2015).…”

Section: Introductionmentioning

confidence: 99%

“…Second, the analysis of factor models by the method of principal components is widely understood and frequently applied in the social sciences. Recent applications of principal component methods to systemic risk include Billio et al (2012), Moreno and Pena (2013), and Giglio et al (2015).…”

Section: Introductionmentioning

confidence: 99%

“…Giglio et al (2015) evaluate a sizeable collection of systemic risk measures and rank them in their ability to forecast macroeconomic downturns in the U.S. and Europe. They adopt a dynamic factor analysis to obtain systemic risk indexes in the time dimension, and then demonstrate that such a systemic risk index provides significant predictive information out-of-sample for the lower tail of macroeconomic outcomes.…”

Section: Introductionmentioning

confidence: 99%

“…Our analysis is different, however, in that we apply factor analysis across rankings in the cross-section dimension, at each point in time, to form optimal combinations of rankings. Giglio et al (2015) instead follow Moreno and Pena (2013) and apply factor analysis across firms to obtain a systemic risk index in the time dimension. Benoit et al (2015) provide a theoretical and empirical survey of systemic risk measures.…”

Section: Introductionmentioning

confidence: 99%

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The information in systemic risk rankings

Nucera

Schwaab

Koopman

et al. 2016

Journal of Empirical Finance

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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.

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“…We do not obtain estimates of factors in a time series context. For a time series approach to 'systemic risk' we refer to Moreno and Pena (2013) and Giglio, Kelly, and Pruitt (2015).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The information in systemic risk rankings

Nucera

Schwaab

Koopman

et al. 2016

Journal of Empirical Finance

View full text Add to dashboard Cite

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“…Battiston et al (2007) consider the propagation of bankruptcies in production networks while the source of system risk in Geanakoplos et al (2012) is the housing market. For an empirical investigation of the relationship between the macroeconomy and systemic risk, see Giglio et al (2016).…”

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confidence: 99%

Systemic Risk and Macroeconomic Fat Tails

Bougheas

Harvey

Kirman

2017

The Economy as a Complex Spatial System

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Abstract. We propose a mechanism for shock amplification that potentially can account for fat tails in the distribution of the growth rate of national output. We argue that extreme macroeconomic events, such as the Great Depression and the Great Recession, were preceded by significant turmoil in the banking system. We have developed a model of bank network formation and presented numerical simulations that show that, for the benchmark case, aggregate credit follows a random walk. When we introduce fire sales the model does not only produce larger variations in the growth of aggregate credit but also shows that there is an asymmetry between booms and busts that is also consistent with empirical evidence.

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Using Policy Intervention to Identify Financial Stress

2013

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This paper describes the construction of a financial stress index (FSI). Our index incorporates the level, volatility and comovement of a variety of financial series, rather than a single dimension of the data. To determine which time periods are ones of notable financial stress and thus the relevant ones for determining the role of the level, volatility and comovement of our financial series, we use actions taken by policymakers. In addition to describing the construction of our FSI, we discuss issues relevant to the general construction of stress indexes such as how an FSI differs from a financial conditions index, the challenges of combining different financial series into a single measure and the role historical experience plays in index construction.

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Systemic Risk and the Macroeconomy: An Empirical Evaluation

Cited by 62 publications

References 22 publications

The information in systemic risk rankings

The information in systemic risk rankings

Systemic Risk and Macroeconomic Fat Tails

Using Policy Intervention to Identify Financial Stress

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