SRISK: A Conditional Capital Shortfall Measure of Systemic Risk
TL;DR: Bekaert et al. as mentioned in this paper introduced SRISK, a measure to measure the systemic risk contribution of a financial firm, which is a function of its size, leverage and risk.
Abstract: We introduce SRISK to measure the systemic risk contribution of a financial firm. SRISK measures the capital shortfall of a firm conditional on a severe market decline, and is a function of its size, leverage and risk. We use the measure to study top financial institutions in the recent financial crisis. SRISK delivers useful rankings of systemic institutions at various stages of the crisis and identifies Fannie Mae, Freddie Mac, Morgan Stanley, Bear Stearns, and Lehman Brothers as top contributors as early as 2005-Q1. Moreover, aggregate SRISK provides early warning signals of distress in indicators of real activity.Received June 7, 2011; accepted April 18, 2016 by Editor Geert Bekaert.
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TL;DR: In this article, the authors use LASSO methods to shrink, select, and estimate the high-dimensional network linking the publicly traded subset of the world's top 150 banks, 2003-2014.
Abstract: Summary
We use LASSO methods to shrink, select, and estimate the high-dimensional network linking the publicly traded subset of the world's top 150 banks, 2003–2014. We characterize static network connectedness using full-sample estimation and dynamic network connectedness using rolling-window estimation. Statically, we find that global bank equity connectedness has a strong geographic component, whereas country sovereign bond connectedness does not. Dynamically, we find that equity connectedness increases during crises, with clear peaks during the Great Financial Crisis and each wave of the subsequent European Debt Crisis, and with movements coming mostly from changes in cross-country as opposed to within-country bank linkages.
267 citations
TL;DR: In this article, the authors used C o V a R to estimate the conditional tail-risk in the markets for bitcoin, ether, ripple and litecoin and found that these cryptocurrencies are highly exposed to tail risk within cryptomarkets.
202 citations
TL;DR: This paper surveys existing researches and methodologies on assessment and measurement of financial systemic risk combined with machine learning technologies, including big data analysis, network analysis and sentiment analysis, etc.
Abstract: This research has been partially supported by grants from the National Natural Science Foundation
of China (#U1811462, #71874023, #71771037, #71725001, and #71433001).
198 citations
TL;DR: In this paper, the authors proposed an extreme risk spillover network for analysing the intimate value at risk (VaR) and the Granger causality risk test (GRLT) to quantify the risk of spillovers.
Abstract: Using the CAViaR tool to estimate the value-at-risk (VaR) and the Granger causality risk test to quantify extreme risk spillovers, we propose an extreme risk spillover network for analysing the int...
163 citations
TL;DR: In this article, the authors proposed a new class of copula-based dynamic models for high-dimensional conditional distributions, facilitating the estimation of a wide variety of measures of systemic risk.
Abstract: This article proposes a new class of copula-based dynamic models for high-dimensional conditional distributions, facilitating the estimation of a wide variety of measures of systemic risk. Our proposed models draw on successful ideas from the literature on modeling high-dimensional covariance matrices and on recent work on models for general time-varying distributions. Our use of copula-based models enables the estimation of the joint model in stages, greatly reducing the computational burden. We use the proposed new models to study a collection of daily credit default swap (CDS) spreads on 100 U.S. firms over the period 2006 to 2012. We find that while the probability of distress for individual firms has greatly reduced since the financial crisis of 2008–2009, the joint probability of distress (a measure of systemic risk) is substantially higher now than in the precrisis period. Supplementary materials for this article are available online.
144 citations
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7,837 citations
TL;DR: In this article, a new class of multivariate models called dynamic conditional correlation models is proposed, which have the flexibility of univariate generalized autoregressive conditional heteroskedasticity (GARCH) models coupled with parsimonious parametric models for the correlations.
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TL;DR: The authors developed a simple neoclassical model of the business cycle in which the condition of borrowers' balance sheets is a source of output dynamics, and the mechanism is that higher borrower net worth reduces the agency costs of financing real capital investments.
Abstract: This paper develops a simple neoclassical model of the business cycle in which the condition of borrowers' balance sheets is a source of output dynamics. The mechanism is that higher borrower net worth reduces the agency costs of financing real capital investments. Business upturns improve net worth, lower agency costs, and increase investment, which amplifies the upturn; vice versa, for downturns. Shocks that affect net worth (as in a debt-deflation) can initiate fluctuations. Copyright 1989 by American Economic Association.
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TL;DR: In this paper, the authors propose several connectedness measures built from pieces of variance decomposition positions, and argue that they provide natural and insightful measures of connectedness among nancial asset returns and volatilities.
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