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

Area of Science:

• Quantitative Finance
• Financial Econometrics
• Insurance Risk Management

Background:

• Systemic risk in the insurance sector poses significant challenges to financial stability.
• Identifying and measuring interconnectedness and contagion pathways is crucial for effective risk management.
• Existing methodologies may not fully capture the dynamic nature of systemic risk.

Purpose of the Study:

• To investigate the utility of the Dynamic Time Warping (DTW) algorithm for identifying, modeling, and measuring systemic risk in the European insurance sector.
• To assess the effectiveness of Minimum Spanning Trees (MST) topological indicators in detecting contagion among insurance firms.
• To group insurance institutions based on the similarity of their systemic risk contributions.

Main Methods:

• Application of the Dynamic Time Warping (DTW) algorithm.
• Construction of Minimum Spanning Trees (MST) using tail dependence coefficients derived from a copula-DCC-GARCH model.
• Calculation of systemic risk contribution using Delta Conditional Value at Risk (DeltaCoVaR).
• Analysis of time series data for 38 European insurance institutions from 2005-2019, distinguishing between crisis and normal periods.

Main Results:

• Minimum Spanning Trees (MST) topological indicators proved effective in identifying systemic risk.
• The study successfully evaluated indirect links and contagion channels between insurance institutions.
• Dynamic Time Warping (DTW) enabled meaningful grouping of institutions based on their systemic risk contribution patterns.
• The analysis highlighted distinct patterns during crisis and normal periods.

Conclusions:

• The combination of MST topological indicators and DTW offers a robust framework for understanding and managing systemic risk in the insurance industry.
• The findings underscore the importance of considering dynamic interdependencies when assessing financial stability.
• The proposed methodology provides valuable tools for regulators and risk managers in the insurance sector.