Financial crisis dynamics: attempt to define a market instability indicator

Choi, Youngna; Douady, Raphaël

doi:10.1080/14697688.2011.627880

Cited by 27 publications

(11 citation statements)

References 18 publications

(10 reference statements)

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“…2019: 3-12; DOI: 10.15196/RS090108 Similar indices of economic distress have been developed, but at country level (e.g. Angelopoulou et al 2014, Bhattacharya-Sinha Roy 2009, Choi-Douady 2012, Louzis-Vouldis 2013. This paper shows that our WD indices satisfy the properties of non-triviality (i.e.…”

Section: Introductionsupporting

confidence: 59%

Quantifying worldwide economic distress

Delbianco¹,

Tohmé²,

Fioriti³

2019

Reg. Stat.

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This paper proposes a new measure for worldwide economic distress, which can be described as the proportion of the gross domestic product (GDP) affected by countrylevel financial crises. It proves that this measure has the desirable properties of fair and representative indices. It also adequately justifies the beliefs of some economists that financial crises have negative effects on economic performance.

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Section: Introductionsupporting

confidence: 59%

Quantifying worldwide economic distress

Delbianco¹,

Tohmé²,

Fioriti³

2019

Reg. Stat.

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“…The last model we consider here is obtained by endowing a bivariate Gumbel copula (cf. (10)) with t margins. Thus it has the same heavy-tailed margins as the previous example, but a different dependence structure.…”

Section: Gumbel Copula With T Marginsmentioning

confidence: 99%

“…In addition to systemic risk measures [cf. 2,4,14,15,1,29,16], related topics include the structure of interbank networks, e.g., [8,11], models explaining how systemic risk is created, e.g., [10,17], and attribution of systemic risk charges within a financial system, as discussed in [26,25].…”

Section: Introductionmentioning

confidence: 99%

On dependence consistency of CoVaRand some other systemic risk measures

Mainik

Schaanning

2014

Statistics &Amp; Risk Modeling

153

115

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This paper is dedicated to the consistency of systemic risk measures with respect to stochastic dependence. It compares two alternative notions of Conditional Value-at-Risk (CoVaR) available in the current literature. These notions are both based on the conditional distribution of a random variable given a stress event for a random variable , but they use different types of stress events. We derive representations of these alternative CoVaR notions in terms of copulas, study their general dependence consistency and compare their performance in several stochastic models. Our central finding is that conditioning on ≥ VaR ( ) gives a much better response to dependence between and than conditioning on = VaR ( ). We prove general results that relate the dependence consistency of CoVaR using conditioning on ≥ VaR ( ) to well established results on concordance ordering of multivariate distributions or their copulas. These results also apply to some other systemic risk measures, such as the Marginal Expected Shortfall (MES) and the Systemic Impact Index (SII). We provide counterexamples showing that CoVaR based on the stress event = VaR ( ) is not dependence consistent. In particular, if ( , ) is bivariate normal, then CoVaR based on = VaR ( ) is not an increasing function of the correlation parameter. Similar issues arise in the bivariate model and in the model with margins and a Gumbel copula. In all these cases, CoVaR based on ≥ VaR ( ) is an increasing function of the dependence parameter.

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“…1 See Choi and Douady (2012) or Choi and Douady (2013) for a detailed nature of these cash flows, although this article follows Castellacci and Choi (2014) that uses different methods in selecting F ij .…”

Section: Basic Setupmentioning

confidence: 99%

“…We use the market instability indicator, an early warning system proposed by Choi and Douady (2012), to quantify the level of financial instability of the United States households and nonprofit institutions serving households (HNISH) and its subsectors. HNISH is chosen because its macroeconomic time series are available on Integrated Macroeconomic Accounts for the United States (2018) and the Survey of Consumer Finances (2017) confirms an extreme wealth inequality within the sector, and hence serves our purpose well.…”

Section: Introductionmentioning

confidence: 99%

Masked Instability: Within-Sector Financial Risk in the Presence of Wealth Inequality

Choi

2018

Risks

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Abstract:We investigate masked financial instability caused by wealth inequality. When an economic sector is decomposed into two subsectors that possess a severe wealth inequality, the sector in entirety can look financially stable while the two subsectors possess extreme financially instabilities of opposite nature, one from excessive equity, the other from lack thereof. The unstable subsector can result in further financial distress and even trigger a financial crisis. The market instability indicator, an early warning system derived from dynamical systems applied to agent-based models, is used to analyze the subsectoral financial instabilities. Detailed mathematical analysis is provided to explain what financial instabilities can arise amid seemingly stable economy and positive market data. The theoretical conjecture is verified by historical macroeconomic time series of the United States households among whom a substantial wealth inequality has been officially confirmed.

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Financial crisis dynamics: attempt to define a market instability indicator

Cited by 27 publications

References 18 publications

Quantifying worldwide economic distress

Quantifying worldwide economic distress

On dependence consistency of CoVaRand some other systemic risk measures

Masked Instability: Within-Sector Financial Risk in the Presence of Wealth Inequality

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