Linkages and systemic risk in the European insurance sector. New evidence based on Minimum Spanning Trees

Denkowska, Anna; Wanat, Stanisław

doi:10.1057/s41283-021-00087-2

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…Particularly, it reduces the amount of redundant information since it only keeps the

most important edges, i.e.,

shortest edges that are well connected. MST stems from graph theory and is applied widely to different fields [ 4 , 87 , 88 ], especially in financial markets [ 89 , 90 , 91 ]. To exploit the useability of MST, the dynamics of community structures in the stock market are observed by Huang et al [ 92 ] with the dataset split into consecutive smaller periods and a MST constructed at each of them.…”

Section: Research Methodologymentioning

confidence: 99%

The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors?

Nguyen

Tan

Bezbradica

et al. 2022

Entropy

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We analyze the correlation between different assets in the cryptocurrency market throughout different phases, specifically bearish and bullish periods. Taking advantage of a fine-grained dataset comprising 34 historical cryptocurrency price time series collected tick-by-tick on the HitBTC exchange, we observe the changes in interactions among these cryptocurrencies from two aspects: time and level of granularity. Moreover, the investment decisions of investors during turbulent times caused by the COVID-19 pandemic are assessed by looking at the cryptocurrency community structure using various community detection algorithms. We found that finer-grain time series describes clearer the correlations between cryptocurrencies. Notably, a noise and trend removal scheme is applied to the original correlations thanks to the theory of random matrices and the concept of Market Component, which has never been considered in existing studies in quantitative finance. To this end, we recognized that investment decisions of cryptocurrency traders vary between bearish and bullish markets. The results of our work can help scholars, especially investors, better understand the operation of the cryptocurrency market, thereby building up an appropriate investment strategy suitable to the prevailing certain economic situation.

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“…Particularly, it reduces the amount of redundant information since it only keeps the

most important edges, i.e.,

Section: Research Methodologymentioning

confidence: 99%

The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors?

Nguyen

Tan

Bezbradica

et al. 2022

Entropy

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“…However, before and after the crisis, the tree was chain-like and decorated by many hubs. Some research observed topological properties of the MST to extract the financial market's evolutional information [15][16][17]. An associative analysis of the market volatility and MST's network properties using APL, maximum degree, and BC was performed to show the effect of volatility on the market [15].…”

Section: Literature Reviewmentioning

confidence: 99%

“…An associative analysis of the market volatility and MST's network properties using APL, maximum degree, and BC was performed to show the effect of volatility on the market [15]. MST has been applied to model many other complex market structures [16,17]. The PMFG was proposed as an alternative tree representation tool of the MST, and as an application, it was tested on 100 stocks of the NYSE [18].…”

Section: Literature Reviewmentioning

confidence: 99%

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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Volatility and returns connectedness in cryptocurrency markets: Insights from graph-based methods

Nguyen,

Mai,

Bezbradica

et al. 2023

Physica A: Statistical Mechanics and its Applications

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Linkages and systemic risk in the European insurance sector. New evidence based on Minimum Spanning Trees

Cited by 3 publications

References 19 publications

The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors?

The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors?

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

Volatility and returns connectedness in cryptocurrency markets: Insights from graph-based methods

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