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

Denkowska, Anna; Wanat, Stanisław

doi:10.3390/e23081022

Cited by 9 publications

(5 citation statements)

References 26 publications

(39 reference statements)

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“…Dmytrów et al [63] assess the links between the number of COVID-19 cases and the energy commodity sector. Denkowska and Wanat [64] use the DTW algorithm to group insurance institutions by the similarity of their contribution to systemic risk, as expressed by DeltaCoVaR. Stübinger [65] uses the DTW method to find optimal causal path algorithm for the minute-by-minute data of the S&P 500 constituents from 1998 to 2015.…”

Section: The Dynamic Time Warping Methodsmentioning

confidence: 99%

Assessment of the Similarity of the Situation in the EU Labour Markets and Their Changes in the Face of the COVID-19 Pandemic

Bieszk‐Stolorz

Dmytrów

2022

Sustainability

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The aim of the study is to assess the similarity of the situation in the EU labour markets and their evolution using selected indicators in the period before and during the COVID-19 pandemic. The benchmark are the countries that most closely meet the Sustainable Development Goals related to the labour market. We use quarterly data from Eurostat presenting the basic indicators of the labour market: unemployment, employment, and activity rates. We analyse all indicators for the total population, young people, and people aged 55+. We assess the similarity of the situation using the TOPSIS method and similarity of changes by means of the Dynamic Time Warping. We obtain homogeneous groups of countries due to similarity of time series using hierarchical clustering. We conduct the analysis in two periods: the years 2018 and 2019 (pre-pandemic period) and from the beginning of 2020 to the present (pandemic period). The composition of the clusters in the pre-pandemic and pandemic periods is different. The impact of the COVID-19 pandemic on the situation in the labour market can be noted. This is a result of different degree of development of labour markets, which had an impact on coping with the effects of the crisis caused by the pandemic.

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Section: The Dynamic Time Warping Methodsmentioning

confidence: 99%

Assessment of the Similarity of the Situation in the EU Labour Markets and Their Changes in the Face of the COVID-19 Pandemic

Bieszk‐Stolorz

Dmytrów

2022

Sustainability

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“…It measures the efficiency of information flow in the network and can identify a structure vulnerable to infection by negative events [58]. Since currency networks are weighted networks, the shortest path length is defined as the smallest sum of the inverse weights of the links throughout all possible paths from node u to node v; − diameter-length of the longest shortest path between any pair of nodes…”

Section: Topological Characteristics Of Networkmentioning

confidence: 99%

The Impact of the Measure Used to Calculate the Distance between Exchange Rate Time Series on the Topological Structure of the Currency Network

Andrzejak,

Chmielewski,

Landmesser-Rusek

et al. 2024

Entropy

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Structural properties of the currency market were examined with the use of topological networks. Relationships between currencies were analyzed by constructing minimal spanning trees (MSTs). The dissimilarities between time series of currency returns were measured in various ways: by applying Euclidean distance, Pearson’s linear correlation coefficient, Spearman’s rank correlation coefficient, Kendall’s coefficient, partial correlation, dynamic time warping measure, and Kullback–Leibler relative entropy. For the constructed MSTs, their topological characteristics were analyzed and conclusions were drawn regarding the influence of the dissimilarity measure used. It turned out that the strength of most types of correlations was highly dependent on the choice of the numeraire currency, while partial correlations were invariant in this respect. It can be stated that a network built on the basis of partial correlations provides a more adequate illustration of pairwise relationships in the foreign exchange market. The data for quotations of 37 of the most important world currencies and four precious metals in the period from 1 January 2019 to 31 December 2022 were used. The outbreak of the COVID-19 pandemic in 2020 and Russia’s invasion of Ukraine in 2022 triggered changes in the topology of the currency network. As a result of these crises, the average distances between tree nodes decreased and the centralization of graphs increased. Our results confirm that currencies are often pegged to other currencies due to countries’ geographic locations and economic ties. The detected structures can be useful in descriptions of the currency market, can help in constructing a stable portfolio of the foreign exchange rates, and can be a valuable tool in searching for economic factors influencing specific groups of countries.

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“…It helps distinguishing between networks that are easy to pass through and those that are complex and inefficient. It can point to a structure that is susceptible to infection by the effects of negative financial events (Denkowska and Wanat, 2021);…”

Section: Topological Characteristics Of Networkmentioning

confidence: 99%

Network analysis of the foreign exchange market using minimum spanning trees constructed from the dynamic time warping distance measure

Landmesser-Rusek

2024

Argumenta Oeconomica

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This paper evaluates the changes that occurred in the topological structure of the foreign exchange market due to the COVID-19 pandemic and after the Russian invasion of Ukraine. A network of 17 currencies was analysed from 1.01.2019 to 31.07.2022 in four sub-periods: before the pandemic, in the year of the pandemic outbreak, in the further course of the pandemic, and the year the war started. Dynamic time warping (DTW) distances between pairs of time series for individual currencies were calculated, and based on these distances, minimum spanning trees (MST) were constructed whose topological characteristics were then analysed. The results show that the topological structure of the foreign exchange market varied in the sub-periods studied. The financial crises caused by the COVID-19 pandemic and the Russian invasion of Ukraine affected the FX network in different ways. However, the two strongest monetary clusters (i.e. clusters centred on the USD and EUR, respectively) have still emerged. Comparing the MSTs for the crisis sub-periods, it can be seen that the USD took a dominant position during the pandemic outbreak, while the EUR played a more significant role during the war. This study contributes to the literature by characterising the topological structure of the FX market in specific sub-periods – during the COVID-19 pandemic and after the Russian invasion of Ukraine. Thus, it represents a particularly relevant study of the impact of crises on the global financial market.

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

Cited by 9 publications

References 26 publications

Assessment of the Similarity of the Situation in the EU Labour Markets and Their Changes in the Face of the COVID-19 Pandemic

Assessment of the Similarity of the Situation in the EU Labour Markets and Their Changes in the Face of the COVID-19 Pandemic

The Impact of the Measure Used to Calculate the Distance between Exchange Rate Time Series on the Topological Structure of the Currency Network

Network analysis of the foreign exchange market using minimum spanning trees constructed from the dynamic time warping distance measure

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