A dynamic MST-deltaCoVaR model of systemic risk in the European insurance sector

Denkowska, Anna; Wanat, Stanisław

doi:10.21307/stattrans-2021-022

Cited by 3 publications

(8 citation statements)

References 12 publications

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“…APL is smallest, Max.Degree is largest, RCE for k = 2 is largest, and the assortativity value is smallest. The alpha parameter values are not in (2,3). Thus, it confirms the visual image of MST, on which we observe the lack of the so-called hubs.…”

Section: Mst Based On Dtwsupporting

confidence: 84%

“…By examining the contribution to systemic risk of all the analyzed insurance institutions, we established a standard DeltaCoVaR measure for each of them based on the estimation CoVaR j|i β,t described in the paper [3]. Measure CoVaR j|i β,t is the value at risk (VaR) of an institution j under the condition that another institution i is at risk of crisis in a given period t, i.e., its rate of return is less than its value at risk:…”

Section: Deltacovar Measurementioning

confidence: 99%

“…Although the average in the SMC period is similar, the dynamics of the change in this indicator is different than in the periods I and FIC, just as was the dynamics of the development of crises. Due to the fact that during the entire period 2005-2019, MSTs are scale-free, as alpha has values in the range (2,3), the similarity of the shape of this indicator series does not provide new information. Note that the period N3 lags behind, the periods FIC and I again are similar, and SMC is in the group with N4.…”

Section: Analysis Of the Similarity Of Time Series Of Topological Indicators Using The Dtw Algorithmmentioning

confidence: 99%

“…The CoVaR measure is the basis for measuring SR. Formally, in [3], CoVaR j|i β,t is defined as the value at risk at the β significance level (VaR) of an institution j under the condition that another institution i is at risk of crisis in a given period t. Then, for the second time in this paper, we used the DTW algorithm in a different context. This time, we established DTW (i, j)the similarity of the DeltaCoVaR time series between pairs (i, j) of insurers in periods of different crises and in the normal period.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Time Warping Algorithm in Modeling Systemic Risk in the European Insurance Sector

Denkowska

Wanat

2021

Entropy

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We are looking for tools to identify, model, and measure systemic risk in the insurance sector. To this aim, we investigated the possibilities of using the Dynamic Time Warping (DTW) algorithm in two ways. The first way of using DTW is to assess the suitability of the Minimum Spanning Trees’ (MST) topological indicators, which were constructed based on the tail dependence coefficients determined by the copula-DCC-GARCH model in order to establish the links between insurance companies in the context of potential shock contagion. The second way consists of using the DTW algorithm to group institutions by the similarity of their contribution to systemic risk, as expressed by DeltaCoVaR, in the periods distinguished. For the crises and the normal states identified during the period 2005–2019 in Europe, we analyzed the similarity of the time series of the topological indicators of MST, constructed for 38 European insurance institutions. The results obtained confirm the effectiveness of MST topological indicators for systemic risk identification and the evaluation of indirect links between insurance institutions.

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Section: Mst Based On Dtwsupporting

confidence: 84%

Section: Deltacovar Measurementioning

confidence: 99%

Section: Analysis Of the Similarity Of Time Series Of Topological Indicators Using The Dtw Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic Time Warping Algorithm in Modeling Systemic Risk in the European Insurance Sector

Denkowska

Wanat

2021

Entropy

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“…For each country, sectoral clusters and strong observable links prevailed during the period crisis. Denkowska et al (2021) used the topological indices of MST and the delta CoVaR measure to analyse the systemic risk dynamics from 2005 to 2019, covering the Great Recession and the European public debt crises. They identified an evident shrinkage of the network during crisis time.…”

Section: Literature On Financial Networkmentioning

confidence: 99%

Electoral influences on the Brazilian B3 data correlation network

Cardoso

Junior²

2022

Int. J Fin Econ

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In any society, the relationship between economy and politics is characterized by controversy, enigmas and mysteries. Economic performance affects political results, and the political environment and its process produce financial results. Brazil was hit by economic and political crises from 2012 to 2016. Such crises made it possible to redesign the correlation network between the assets negotiated on the B3 (Brazil Stock Exchange and Over‐the‐Counter Market). This paper aimed to analyse the main aspects related to the topological structure of the assets negotiated on the B3 and its volatility between the pre‐and post‐2014 electoral periods. Results showed the hierarchical clusters and the evolution of the systemic risk, with the banks leading the concentration in the post‐electoral period. The closeness centrality indices for the minimum variance portfolios were modified by approximately 80% between the pre‐electoral and post‐electoral periods. It was concluded that the political events significantly changed the structure, the risk and the possibility of selecting assets for portfolios in the Brazilian market.

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Change in hierarchy of the financial networks: A study on firms of an emerging market in Bangladesh

Rakib,

Alam,

Akter

et al. 2024

PLoS ONE

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We investigate the hierarchical structure of Dhaka stocks’ financial networks, known as an emerging market, from 2008 to 2020. To do so, we determine correlations from the returns of the firms over a one-year time window. Then, we construct a minimum spanning tree (MST) from correlations and calculate the hierarchy of the tree using the hierarchical path. We find that during the unprecedented crisis in 2010–11, the hierarchy of this emerging market did not sharply increase like in developed markets, implying the absence of a compact cluster in the center of the tree. Noticeably, the hierarchy fell before the big crashes in the Bangladeshi local market, and the lowest value was found in 2010, just before the 2011 Bangladesh market scam. We also observe a lower hierarchical MST during COVID-19, which implies that the network is fragile and vulnerable to financial crises not seen in developed markets. Moreover, the volatility in the topological indicators of the MST indicates that the network is adequately responding to crises and that the firms that play an important role in the market during our analysis periods are financial, particularly the insurance companies. We notice that the largest degrees are minimal compared to the total number of nodes in the tree, implying that the network nodes are somewhat locally compact rather than globally centrally coupled. For this random structure of the emerging market, the network properties do not properly reflect the hierarchy, especially during crises. Identifying hierarchies, topological indicators, and significant firms will be useful for understanding the movement of an emerging market like Dhaka Stock exchange (DSE), which will be useful for policymakers to develop the market.

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A dynamic MST-deltaCoVaR model of systemic risk in the European insurance sector

Cited by 3 publications

References 12 publications

Dynamic Time Warping Algorithm in Modeling Systemic Risk in the European Insurance Sector

Dynamic Time Warping Algorithm in Modeling Systemic Risk in the European Insurance Sector

Electoral influences on the Brazilian B3 data correlation network

Change in hierarchy of the financial networks: A study on firms of an emerging market in Bangladesh

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