Within and between systemic country risk. Theory and evidence from the sovereign crisis in Europe

Baglioni, Angelo; Cherubini, Umberto

doi:10.1016/j.jedc.2013.02.005

Cited by 31 publications

(30 citation statements)

References 22 publications

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“…Although this type of estimation well reflects the default dependence among institutions, it can only capture one type of risk, which is credit risk. On the other hand, it is very useful in studying the nature of systemic sovereign credit risk (see Lucas et al, ; Ang and Longstaff, ), an analysis not feasible by using stock market data, and it has been used to analyse co‐movements between the banking system and risk in the public sector (Baglioni and Cherubini, ).…”

Section: Introductionmentioning

confidence: 99%

Systemic Risk in the Italian Banking Industry

et al. 2014

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Systemic risk is the risk of a collapse of the entire financial system, typically triggered by the default of one, or more, interconnected financial institutions. In this paper, we estimate the systemic risk contribution of Italian-listed banks for the period 2000–2011. We follow a methodology first proposed by Adrian and Brunnermeier and measure banks' contribution to systemic risk by ΔCoVaR, which measures the contribution of bank i to the financial system VaR when bank i is in a state of distress. We define ‘the system’ as the set of Italian-listed banks in the sample. First, we find that the information contained in ΔCoVaR is different from that contained in the VaR. Therefore, regulators should take it into account in order to monitor the systemic risk posed by banks. Second, recent policy debate has focused on the danger posed by large banks and on the need to curb their size. We find that size is indeed the main predictor of a bank contribution to systemic risk. However, in the post-Lehman period, leverage is also an important predictor of systemic risk. Consequently, any financial regulation designed only to curb banks' size could not completely eliminate systemic risk because it is exactly in crisis times that leverage becomes relevant. Hence, we conclude that ΔCoVaR is a very useful policy tool for regulators that can estimate which factors are more relevant in terms of contribution to systemic risk

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Section: Introductionmentioning

confidence: 99%

Systemic Risk in the Italian Banking Industry

et al. 2014

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“…After the crisis, the financial contagion in the stock market is revealed and comovements between the analyzed stock markets have become more noticeable. Baglioni and Cherubini (2013) and Metiu (2012) prove that the financially cross-border contagion in Europe increases on and after the sovereign debt crisis.…”

Section: Empirical Analysis Resultsmentioning

confidence: 96%

A case study for intercontinental comparison of herd behavior in global stock markets

Lee

Choi

Kim

et al. 2018

CSAM

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Measuring market fear is an important way of understanding fundamental economic phenomena related to financial crises. There have been several approaches to measure market fear or panic level in a financial market. Recently, herd behavior has gained its popularity as important economic phenomena explaining the fear in the financial market. In this paper, we investigate herd behavior in global stock markets with a focus on intercontinental comparison. While various risk measures are available for the detection of herd behavior in the market, we use the standardized herd behavior index in Dhaene et al.

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“…On the other side, ignoring these relationships may induce a substantial bias in the estimates of the common factor itself. This problem is recognized by [1], who propose an estimator based on a model with standard bivariate Marshall-Olkin margins.…”

Section: Discussionmentioning

confidence: 99%

Extended Marshall–Olkin Model and Its Dual Version

Pinto

Kolev

2015

Springer Proceedings in Mathematics &Amp; Statistics

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We propose an extension of the generalized bivariate Marshall-Olkin model assuming dependence between the random variables involved. Probabilistic, aging properties, and survival copula representation of the extended model are obtained and illustrated by examples. Bayesian analysis is performed and possible applications are discussed. A dual version of extended Marshall-Olkin model is introduced and related stochastic order comparisons are presented. IntroductionA variety of bivariate (multivariate) extensions of the univariate exponential distribution have been considered in the literature. These include the distributions of [6,10,11,24], see a full review in [2].The vector (X 1 , X 2 ) meets the Marshall-Olkin model (MO hereafter) whenever it admits the stochastic representationwhere the random variables T i are independent and exponentially distributed with parameters λ i > 0, respectively, i = 1, 2, 3. The random variables T 1 and T 2 in (6.1) can be interpreted as the arrival time of individual shocks for two different components, while T 3 represents the time of arrival of a shock common to both components. Several applications of (6.1) can be mentioned. In reliability theory it can describe the lifetime of a system of two components operating in a random environment and subject to three independent

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Within and between systemic country risk. Theory and evidence from the sovereign crisis in Europe

Cited by 31 publications

References 22 publications

Systemic Risk in the Italian Banking Industry

Systemic Risk in the Italian Banking Industry

A case study for intercontinental comparison of herd behavior in global stock markets

Extended Marshall–Olkin Model and Its Dual Version

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