Research on the Impact of Politically Related Meme Coins on the Crypto Assets Market

Recently, a study published in Economics Letters analyzed the event of a well-known political figure issuing a Meme coin, revealing the heterogeneous volatility spillover effects driven by market sentiment and fundamentals. This event highlights the increasingly important role of political factors in shaping the Crypto Assets market and investor behavior.

Introduction

The impact of political dynamics on financial markets is becoming increasingly significant, and the Crypto Assets market has emerged as an important area where politics and finance intersect. The 2024 United States presidential election further highlights this relationship, as a certain candidate has shifted to support digital assets, claiming to make the U.S. the "Crypto Assets capital of the world" and placing Crypto Assets at the core of their economic agenda.

These are expected to be realized on January 18, 2025, when the candidate issued its official Meme coin on the Solana blockchain. Within 24 hours, the coin's price surged by 900%, with a trading volume of $18 billion, surpassing the market capitalization of the largest Meme coin at the time, which was $4 billion. The next day, the issuance of Meme coins related to its family members further fueled market speculation. These events not only had a speculative nature but also constituted a significant exogenous shock, the impact of which extended beyond the realm of financial speculation, sending signals of broader regulatory and political agendas.

This study aims to examine how this event acts simultaneously as a political signal and a financial event affecting the Crypto Assets market, focusing on three key issues:

1. How does the release of this Meme coin affect the returns and volatility of major Crypto Assets?
2. Did this event trigger a financial contagion effect in the Crypto Assets market?
3. Does this impact exhibit heterogeneity, manifesting as different responses from various Crypto Assets based on their technological foundations, uses, or speculative appeal?

To address these questions, this paper employs the Baba-Engle-Kraft-Kroner (BEKK) multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model, which is particularly suitable for analyzing the dynamic relationship between volatility and correlation over time.

Research has found that after the release of the Meme coin, there is a significant volatility spillover effect among Crypto Assets, indicating the presence of financial contagion in the market. The event triggered a major shift in market dynamics, with Solana and Chainlink recording the largest gains due to their infrastructure and strategic connections. Meanwhile, mainstream Crypto Assets like Bitcoin and Ethereum exhibited strong resilience, with their cumulative abnormal returns (CARs) and variance stabilizing in the later stages of the event. In contrast, other Meme coins such as Dogecoin and Shiba Inu depreciated, with funds likely shifting to the newly issued Meme coins.

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This event occurred in an environment of high political polarization in the United States, closely related to strong political emotions, thereby increasing investor sensitivity and exacerbating market reactions. For some investors, this symbolizes a unique speculative opportunity, giving rise to a strong "herding effect"; while other investors become aware of political and regulatory risks due to its controversial image and adopt a more cautious stance. This polarization explains the observed high volatility and differentiated market responses.

This study is the first paper analyzing the impact of politically associated tokens on the Crypto Assets market. It expands the understanding of how political narratives influence decentralized financial markets. Furthermore, unlike previous research that mostly focused on negative shocks, this study focuses on the impact of positive shocks driven by political signals on the market. The research findings provide important references for academia, practitioners, and policymakers, revealing the heterogeneity of market responses to politically associated tokens and emphasizing how asset characteristics influence financial contagion dynamics.

Data and Methods

2.1 Data and Sample Selection

This study uses proprietary data of closing mid-prices per minute, covering the ten most representative Crypto Assets among the top 20 by market capitalization: Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Solana (SOL), Dogecoin (DOGE), Chainlink (LINK), Avalanche (AVAX), Shiba Inu (SHIB), Polkadot (DOT), and Litecoin (LTC). The data is sourced from a centralized trading platform in the US, obtained from the LSEG Tick History database.

The dataset contains 20,160 observations, with a time interval from January 11, 2025, to January 25, 2025, covering a symmetric time period one week before and after the release of the Meme coin (January 18, 2025).

The formula for calculating yield is:

Yield = ln(Pt / Pt-1)

Among them, Pt represents the price of the digital asset at time t.

The event time is defined as 2:44 AM Coordinated Universal Time (UTC) on January 18, 2025, which marks the first official announcement of the new Meme coin release. Cumulative abnormal returns are calculated to assess the information cascade effect. The average benchmark return for each Crypto Asset is calculated from the returns between January 1, 2025, and January 10, 2025, serving as a relatively stable preliminary sample. The actual returns within the sample period are subtracted from this benchmark to obtain excess returns relative to the market benchmark, which are then accumulated to derive CARs.

2.2 Method

Using the BEKK-MGARCH model to analyze the impact of the launch of this Meme coin on the Crypto Assets market. It is assumed that the logarithmic returns follow a normal distribution with a mean of zero and a conditional covariance matrix of Ht, with the model set up as follows:

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H represents the unconditional covariance matrix. The parameter matrix satisfies a, b > 0, and a + b < 1, to ensure the stability and positive definiteness of the model. Subsequently, an infectious effect test is conducted. Considering the potential Type I error issues when using high-frequency data, this paper adopts a more stringent significance level of α = 0.001.

Result

3.1 Volatility Overflow Effect

Preliminary analysis results reveal the interrelationships between Crypto Assets. In the covariance structure, the interconnection between assets significantly strengthens in the post-event phase, supporting the hypothesis that "events triggered volatility spillover effects." The volatility of the stationary log returns increases, reflecting a rise in market instability and a faster adjustment speed. The returns of each Crypto Asset experienced sharp fluctuations during this event, further emphasizing the systemic impact of this incident.

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The estimation results of the dynamic conditional covariance indicate that the event indeed triggered financial contagion and volatility spillover effects in the Crypto Assets market. The covariance coefficients in the later stages of most events are significant at the 0.001 significance level, especially among assets like ETH, SOL, and LINK, where the covariance significantly increased, showing stronger connectivity and a higher degree of market integration. Although SHIB and DOT also reached a significance level of 0.01, their impact is weaker. The covariance of LTC and XRP actually decreased after the event, indicating that the spillover effect is not uniformly distributed among all assets. Overall, the results highlight the structural impact of this Meme coin issuance event on the entire Crypto Assets market.

3.2 information cascading effect

The analysis of Cumulative Abnormal Returns (CARs) further reveals the information cascade effects triggered by the issuance of this Meme coin. The results indicate that the event has a significant structural impact on market dynamics, manifested as asset-specific reaction paths and increased volatility.

In the pre-event stage, most Crypto Assets experienced positive returns, possibly driven by speculative expectations or the market's optimistic attitude towards a certain candidate potentially being elected as the 47th President of the United States. This indicates that even in the absence of concrete information, investors have shown significant speculative buying behavior, a phenomenon that aligns with the widely documented "fear of missing out" characteristic in the Crypto Assets market.

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In the phase following the event, three key dynamics are particularly prominent:

1. SOL has performed excellently, surpassing all other assets, which is likely related to its direct technical relationship as the blockchain supporting the Meme coin.

2. LINK has also performed strongly, possibly due to its correlation with major US tech companies.

3. Bitcoin, Ethereum, Ripple, Litecoin and other mature Crypto Assets have gradually stabilized after a moderate rise, reflecting their market resilience and relative insulation from the impact of cascading speculation.

At the same time, DOGE and other Meme coins like SHIB appear particularly weak, exhibiting a clear asset substitution effect, where speculative funds have shifted from old Meme coins to newly issued tokens. Although AVAX and DOT have solid technological foundations, they have also not been spared from this trend of capital transfer, showing signs of value erosion.

The research findings reveal that asset-specific narratives, technological relevance, and investors' subjective perceptions can significantly amplify the differential responses of asset returns during major information shocks.

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Conclusion

This study examines the impact of cryptocurrency issuance associated with political figures on the crypto market, focusing on the analysis of volatility spillover effects and information cascade effects.

Research results indicate that the market's reaction to this event exhibits significant heterogeneity. For example, SOL has benefited significantly due to its direct technical association with the Meme coin. Additionally, assets sharing the same underlying blockchain infrastructure have also been boosted by riding the "coattails" of this event.

At the same time, mainstream Crypto Assets such as Bitcoin and Ethereum demonstrate stronger stability due to their core position in the market, playing a stabilizing role similar to an anchor during this event, thus stabilizing the overall market structure. This indicates that investor sentiment is no longer solely dependent on fundamental technical factors, but is also significantly influenced by geopolitical and policy narratives, especially when these narratives are issued by highly symbolic leaders.

In summary, this article reveals the high sensitivity of the Crypto Assets market to external events, as well as its tendency to be driven by speculative behavior. As digital assets become increasingly intertwined with political and economic issues, it is particularly important to continuously monitor this interaction to understand its impact on market stability.

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