Network and Herding Analysis Reveal Structural Shifts During Terra Collapse

Table of Links

Abstract and 1. Introduction

2. Data and quantitative nature of the events

2.1. Hourly data analysis

2.2. Transaction data analysis

2.3. Anchor protocol

3. Methodology

3.1. Network analysis: Triangulated Maximally Filtered Graph (TMFG)

3.2. Herding analysis

4. Results

4.1. Correlations and network analysis

4.2. Herding analysis: CSAD approach

5. Robustness analysis

6. Implications and future research

6.1. Relevance for stakeholders

6.2. Future lines of research

7. Conclusion, Acknowledgements, and References

Supplementary Material

3. Methodology

3.1. Network analysis: Triangulated Maximally Filtered Graph (TMFG)

To get more insights into Terra project’s failure, we describe the evolution of dependency structures among cryptocurrencies during the crash. In order to do this, we use instruments provided by network science. Networks have been extensively used to study financial systems (Mantegna, 1999; Aste et al., 2010; Briola and Aste, 2022) modeling dependencies among assets through correlations. Different correlation measures capture different relationships among assets. We decide to use the Pearson correlation coefficient to model linear relations among cryptocurrencies. It is worth noting that, in a condition of stress of the underlying system, pure correlations might be affected by an excessive sensitiveness. In order to partially mitigate such an effect, we assign a structure of weights to observational events, giving more relevance to the last observations of a given window. Weighted correlations are found to be smoother and recovering faster from market’s turbulence than their unweighted counterparts, helping also to discriminate more effectively genuine from spurious correlations. Following the definition in Pozzi et al. (2012), we define the Pearson correlation coefficient weighted with exponential smoothing as follows:

Such a definition can be used to build a range of networks representing dependency structures among cryptocurrencies (Briola and Aste, 2022). Here we use the state-of-the-art methodology, namely, Triangulated Maximally Filtered Graph (TMFG) (Massara et al., 2017). Such a filtering network comes with several advantages compared to its alternatives. It is able to capture meaningful interactions among multiple assets and it is characterised by topological constraints which help to regularise for probabilistic modeling (Aste, 2022). As a measure of network centrality, we compute the eigenvector centrality, which allows us to measure the influence of a crypto-asset in the system. Intuitively, a cryptocurrency has an higher eigenvector centrality as long as it is connected to other relevant cryptocurrencies, which are also characterised by an high eigenvector centrality. In order to highlight relevant events and reduce the impact of secondary ones, also in this case, we compute an exponentially smoothed rolling average of the signal with a smoothing factor equals to 0.3.[11]

3.2. Herding analysis

In this study, we also use the approach proposed by Chang et al. (2000) to study the possible emergence of herd behaviour during Terra collapse. This method is based on the notion of herding towards market consensus in which “herds are characterised by individuals who suppress their own beliefs and base their investment decisions solely on the collective actions of the market, even when they disagree with its predictions” (Christie and Huang, 1995). More specifically, Chang et al. (2000) analysed the existence of herding in a system of N assets through the cross-sectional absolute deviation of returns (CSAD) as a measure of return dispersion,

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[11] Results are consistent to different values of θ.