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

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1022
Author(s):  
Anna Denkowska ◽  
Stanisław Wanat
Keyword(s):  
Systemic Risk ◽  
Dynamic Time Warping ◽  
Spanning Trees ◽  
Tail Dependence ◽  
Risk Identification ◽  
Insurance Companies ◽  
Time Warping ◽  
Insurance Sector ◽  
Dynamic Time ◽  
European Insurance

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.

scholarly journals A Tail Dependence-Based MST and Their Topological Indicators in Modeling Systemic Risk in the European Insurance Sector

Risks ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 39 ◽  
Author(s):  
Anna Denkowska ◽  
Stanisław Wanat
Keyword(s):  
Time Series ◽  
Systemic Risk ◽  
Spanning Trees ◽  
Hybrid Approach ◽  
Market Price ◽  
Garch Model ◽  
Tail Dependence ◽  
Insurance Companies ◽  
Insurance Sector ◽  
European Insurance

In the present work, we analyze the dynamics of indirect connections between insurance companies that result from market price channels. In our analysis, we assume that the stock quotations of insurance companies reflect market sentiments, which constitute a very important systemic risk factor. Interlinkages between insurers and their dynamics have a direct impact on systemic risk contagion in the insurance sector. Herein, we propose a new hybrid approach to the analysis of interlinkages dynamics based on combining the copula-DCC-GARCH model and minimum spanning trees (MST). Using the copula-DCC-GARCH model, we determine the tail dependence coefficients. Then, for each analyzed period we construct MST based on these coefficients. The dynamics are analyzed by means of the time series of selected topological indicators of the MSTs in the years 2005–2019. The contribution to systemic risk of each institution is determined by analyzing the deltaCoVaR time series using the copula-DCC-GARCH model. Our empirical results show the usefulness of the proposed approach to the analysis of systemic risk (SR) in the insurance sector. The times series obtained from the proposed hybrid approach reflect the phenomena occurring in the market. We check whether the analyzed MST topological indicators can be considered as systemic risk predictors.

scholarly journals Capital regulation and systemic risk in the insurance sector

2018 ◽  
Vol 10 (2) ◽  
pp. 237-263 ◽  
Author(s):  
Thomas Gehrig ◽  
Maria Chiara Iannino
Keyword(s):  
Systemic Risk ◽  
Banking Sector ◽  
Financial System ◽  
Solvency Ii ◽  
Insurance Companies ◽  
Capital Regulation ◽  
Content Type ◽  
Insurance Sector ◽  
Main Challenge ◽  
European Insurance

Purpose This paper aims to analyze systemic risk in and the effect of capital regulation on the European insurance sector. In particular, the evolution of an exposure measure (SRISK) and a contribution measure (Delta CoVaR) are analyzed from 1985 to 2016. Design/methodology/approach With the help of multivariate regressions, the main drivers of systemic risk are identified. Findings The paper finds an increasing degree of interconnectedness between banks and insurance that correlates with systemic risk exposure. Interconnectedness peaks during periods of crisis but has a long-term influence also during normal times. Moreover, the paper finds that the insurance sector was greatly affected by spillovers from the process of capital regulation in banking. While European insurance companies initially at the start of the Basel process of capital regulation were well capitalized according to the SRISK measure, they started to become capital deficient after the implementation of the model-based approach in banking with increasing speed thereafter. Practical implications These findings are highly relevant for the ongoing global process of capital regulation in the insurance sector and potential reforms of Solvency II. Systemic risk is a leading threat to the stability of the global financial system and keeping it under control is a main challenge for policymakers and supervisors. Originality/value This paper provides novel tools for supervisors to monitor risk exposures in the insurance sector while taking into account systemic feedback from the financial system and the banking sector in particular. These tools also allow an evidence-based policy evaluation of regulatory measures such as Solvency II.

scholarly journals Understanding the Impact of COVID–19 on Global Financial Network Using Graph Based Algorithm: Minimum Spanning Tree Approach

2021 ◽  
Vol 46 (1) ◽  
pp. 111-123
Author(s):  
Veysel Fuat Hatipoğlu
Keyword(s):  
Stock Markets ◽  
Dynamic Time Warping ◽  
Spanning Trees ◽  
Minimum Spanning Tree ◽  
Minimum Spanning Trees ◽  
Time Warping ◽  
Time Period ◽  
Dynamic Time ◽  
The Impact ◽  
Tree Approach

AbstractIn this paper effects of COVID–19 pandemic on stock market network are analyzed by an application of operational research with a mathematical approach. For this purpose two minimum spanning trees for each time period namely before and during COVID–19 pandemic are constructed. Dynamic time warping algorithm is used to measure the similarity between each time series of the investigated stock markets. Then, clusters of investigated stock markets are constructed. Numerical values of the topology evaluation for each cluster and time period is computed.

Multichannel sparse spike deconvolution based on dynamic time warping

2021 ◽  
Author(s):  
Xiaowei Zhao ◽  
Shangxu Wang ◽  
Sanyi Yuan ◽  
Liang Cheng ◽  
Youjun Cai
Keyword(s):  
Dynamic Time Warping ◽  
Time Warping ◽  
Dynamic Time
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