Contagion and interdependence in Eurozone bank and sovereign credit markets

This paper proposes a new systemic risk measure based on a multi‐way analysis. The systemic risk is composed of two different components: the time and the cross‐dimension. The first refers to the accumulation of banking risk and its interaction with the business cycle, while the second concern, the high‐level concentration of the specific risk on relevant financial institutions. Thanks to the three‐way analysis, we can, on the one hand, estimate a new measure of systemic risk based on these both components, and, on the other hand, we can transparently identify the SIBs (systemically important banks). To this end, we have empirically evaluated and compared the marginal expected shortfall (MES), the SRISK measure and the conditional value at risk (CoVaR) based on a representative sample of Eurozone institutions listed on the stock exchange for the period from June 2005 to May 2018. Our results show how these systemic risk measures produce different systemic risk classifications for the same bank. The findings, therefore, highlight the fragility and structural dependence of these measures, which may not be used for the estimation of a stable rank. Applying three‐way factorial analysis, we show how our measure gives a more stable score. Moreover, our index is the first one to be composed of both the cross‐section and the temporal components, essential elements for a proper assessment of systemic risk.

Section: An Overview Of Systemic Risk In Eurolandmentioning

confidence: 99%

The triple (T3) dimension of systemic risk: Identifying systemically important banks

Foglia

Angelini

2020

“…For example, Segoviano and Goodhart used CDSs to introduce an indicator for the stability of the banking system based on interbank relationships in extreme events (Segoviano & Goodhart, 2009). Moreno and Peña (2013) and Bratis, Laopodis, and Kouretas (2018) also found that the CDS spread could be used as an appropriate estimator of systemic risk. In another paper, Morrison, Michalis, Mungo, and Zikes (2017) used the network of CDS to show the transmission of shocks through financial networks.…”

Section: Introductionmentioning

confidence: 97%

Network‐based early warning system to predict financial crisis

Dastkhan

2019

In recent years, early warning systems have been developed as a significant tool to prevent financial and economic crises. In this paper, we applied the forward‐looking conditional value at risk (COVaR) as a market‐based systemic risk measure to analyse the systemic risk in an emerging market. A network representation of asset exposures is presented based on the values of ΔCoVaR. According to the exposure networks, two new network‐based indices are introduced. Furthermore, regarding the dependence of the new indices on the values of forward‐looking CoVaR, they are applied as the indicators for the proposed early warning system to predict downturns and crises in the level of firms and the whole market. The results of the early warning system for the market return show that the indicators based on both proposed models have a good ability to predict the crises in the market. In other words, the indicators issued a warning signal up to seven periods before the real crisis in the market. Similarly, the results of the early warning system to predict downturns at the level of firms also indicate the ability of indicators to issue the true warning signals. The results of the proposed system can help policymakers and authorities to determine the appropriate microprudential and macroprudential policies to avoid systemic risks.

“…This family of models are often heavily parameterized and the problem gets more pronounced when the size of the assets is large. Thus, some M‐GARCH models (e.g., DCC‐GARCH) constrain the parameters and simplify the parameters estimation to avoid “dimension disaster,” which have been adopted particularly for market practitioners (Bratis, Laopodis, & Kouretas, 2018; Miralles‐Quiros & Miralles‐Quiros, 2017; Tam & Yu, 2008).…”

Section: Introductionmentioning

confidence: 99%

A large constrained time‐varying portfolio selection model with DCC‐MIDAS: Evidence from Chinese stock market

Zuo

Jiang

et al. 2020

To solve a large portfolio selection, we propose a novel norm constrained time‐varying minimum variance model with DCC‐MIDAS, labelled as NC‐MVP‐DCC‐MIDAS. It applies the DCC‐MIDAS model to improve the estimation of dynamic correlations among financial assets by exploiting rich information contained in mixed frequency data. Additionally, it imposes norm constraints on the minimum variance portfolio with the elastic‐net penalty to pick a reasonable number of assets and prevent extreme positions in the resulting portfolio. Its superiority is illustrated via empirical studies on the construction of optimal sparse and stable portfolios with the constituent stocks in the Shanghai Stock Exchange (SSE) 50 Index of China.