Does Financial Connectedness Predict Crises?

Minoiu, Camelia; Kang, Chanhyun; Subrahmanian, V. S.; Berea, Anamaria

doi:10.5089/9781475554250.001

Cited by 18 publications

(7 citation statements)

References 43 publications

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“…Chinazzi, Fagiolo, Reyes, and Schiavo (2013) relate the 2008-2009 crisis to a global banking network built with data on cross-border portfolio investment holdings. In a similar vein, Minoiu, Kang, Subrahmanian, and Berea (2015) show the usefulness of network measures, computed over the web of international banking exposures (the BIS bilateral locational statistics), for crisis prediction. Differently, in our analysis, the banking sector is considered as one of the sectors in the broad architecture of the financial system.…”

Section: Introductionmentioning

confidence: 87%

Interconnectedness of the banking sector as a vulnerability to crises

2018

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This paper uses macro‐network to measure the interconnectedness of the banking sector and relates it to banking crises in Europe. Beyond cross‐border financial linkages of the banking sector, the macronetwork also accounts for financial linkages to the other main financial and nonfinancial sectors within the economy. We find that a more central position of the banking sector in the macronetwork significantly increases the probability of a banking crisis. By analysing the different types of risk exposures, our evidence shows that credit is an important source of vulnerability. Finally, our early‐warning models augmented with interconnectedness measures outperform traditional models in terms of out‐of‐sample predictions.

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Section: Introductionmentioning

confidence: 87%

Interconnectedness of the banking sector as a vulnerability to crises

2018

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“…By constructing two investment networks based on the cross-border equity and a long-term debt securities portfolio, Joseph et al (2014) identify two network-based indicators (algebraic connectivity and edge density) that could have predicted the 2008 global financial crisis. Minoiu et al (2015) show that the interconnectedness in the global network of financial linkages could have predicted the financial crises that occurred during the 1978-2010 period.…”

Section: Early Warning Model Of Financial Crisismentioning

confidence: 99%

Short term prediction of extreme returns based on the recurrence interval analysis

Jiang

Wang

Canabarro

et al. 2017

Quantitative Finance

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Being able to predict the occurrence of extreme returns is important in financial risk management. Using the distribution of recurrence intervals-the waiting time between consecutive extremes-we show that these extreme returns are predictable on the short term. Examining a range of different types of returns and thresholds we find that recurrence intervals follow a q-exponential distribution, which we then use to theoretically derive the hazard probability W(∆t|t). Maximizing the usefulness of extreme forecasts to define an optimized hazard threshold, we indicates a financial extreme occurring within the next day when the hazard probability is greater than the optimized threshold. Both in-sample tests and out-of-sample predictions indicate that these forecasts are more accurate than a benchmark that ignores the predictive signals. This recurrence interval finding deepens our understanding of reoccurring extreme returns and can be applied to forecast extremes in risk management.

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“…It has been shown that the topology of financial networks can be associated with probabilities for systemic collapse [21,22]. In particular, network centrality measures have been identified as appropriate measures to quantify SR by various groups [8,[23][24][25][26][27][28]. A disadvantage of centrality measures is that the SR value for a particular node has no clear interpretation as a measure for expected losses in the case of a cascading failure event.…”

Section: Introductionmentioning

confidence: 99%

The multi-layer network nature of systemic risk and its implications for the costs of financial crises

Poledna

Molina-Borboa

Martínez-Jaramillo

et al. 2015

Journal of Financial Stability

259

166

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The inability to see and quantify systemic financial risk comes at an immense social cost. Systemic risk in the financial system arises to a large extent as a consequence of the interconnectedness of its institutions, which are linked through networks of different types of financial contracts, such as credit, derivatives, foreign exchange and securities. The interplay of the various exposure networks can be represented as layers in a financial multi-layer network. In this work we quantify the daily contributions to systemic risk from four layers of the Mexican banking system from 2007-2013. We show that focusing on a single layer underestimates the total systemic risk by up to 90%. By assigning systemic risk levels to individual banks we study the systemic risk profile of the Mexican banking system on all market layers. This profile can be used to quantify systemic risk on a national level in terms of nation-wide expected systemic losses. We show that market-based systemic risk indicators systematically underestimate expected systemic losses. We find that expected systemic losses are up to a factor four higher now than before the financial crisis of 2007-2008. We find that systemic risk contributions of individual transactions can be up to a factor of thousand higher than the corresponding credit risk, which creates huge risks for the public. We find an intriguing non-linear effect whereby the sum of systemic risk of all layers underestimates the total risk. The method presented here is the first objective data driven quantification of systemic risk on national scales that reveal its true levels.

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Does Financial Connectedness Predict Crises?

Cited by 18 publications

References 43 publications

Interconnectedness of the banking sector as a vulnerability to crises

Interconnectedness of the banking sector as a vulnerability to crises

Short term prediction of extreme returns based on the recurrence interval analysis

The multi-layer network nature of systemic risk and its implications for the costs of financial crises

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