Study on the Political Association Effects of the Crypto Assets Market

Recently, Economics Letters published an article titled “From Zero to Hero: The Spillover Effects of Meme Coins in the Crypto Assets Market.” The study analyzes the event of a political figure issuing a meme coin, revealing the heterogeneous volatility spillover effects driven by market sentiment and fundamentals, with political signals amplifying speculative dynamics, highlighting the important role of political factors in shaping the crypto assets market and investor behavior.

Introduction

The influence 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 U.S. presidential election further highlights this relationship, as a certain Republican candidate has shifted to support digital assets, claiming to turn the U.S. into the “global capital of Crypto Assets,” placing Crypto Assets at the core of his economic agenda. As a result, the market anticipates a more favorable policy stance during his potential term.

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 skyrocketed by 900%, with trading volume reaching $18 billion, surpassing the then-largest meme coin by $4 billion in market capitalization. The next day, the issuance of the meme coin related to his family further fueled market speculation. These events are not only speculative in nature but also constitute a significant exogenous shock, the effects of which extend beyond financial speculation, sending signals for broader regulatory and political agendas.

This study aims to explore how this event acts as both a political signal and a financial event impacting the Crypto Assets market. The research focuses on three key questions:

1. How does the release of new meme coins 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 in different responses from various Crypto Assets based on their technological foundation, use cases, or speculative appeal?

To answer these questions, this study 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.

The study selected the top ten cryptocurrencies by market capitalization for empirical analysis and found that after the release of new meme coins, 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. In contrast, mainstream Crypto Assets like Bitcoin and Ethereum demonstrated strong resilience, with their cumulative abnormal returns (CARs) and variances stabilizing in the later stages of the event. Conversely, other meme coins such as Dogecoin and Shiba Inu experienced depreciation, with funds likely shifting towards the newly issued meme coins.

The issuance of the new meme coin occurred in a highly politically polarized environment in the United States, where the associated political figures' brands are closely linked to strong political sentiments, thereby increasing investors' sensitivity and exacerbating market reactions. For some investors, this endorsement symbolizes a unique speculative opportunity, giving rise to a strong “herding effect”; while other investors become aware of the 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—from enthusiasm for expected political support to skepticism about reputation and political uncertainty.

This study is the first to analyze the impact of politically connected tokens on the Crypto Assets market. It expands the understanding of how political narratives influence decentralized financial markets. Furthermore, unlike previous studies that have primarily focused on negative shocks, this research focuses on the impact of positive shocks driven by political signals on the market. Notably, there is evidence that positive shocks have an even greater impact on the volatility of Crypto Assets than negative shocks. Ultimately, this study provides important references for academia, practitioners, and policymakers, revealing the heterogeneity of market responses to politically connected tokens and emphasizing how asset characteristics influence financial contagion dynamics.

Data and Methods

Data and Sample Selection

The study uses proprietary data on closing mid-prices per minute, covering the 10 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 United States, with specific data obtained from the LSEG Tick History database.

The dataset contains a total of 20,160 observations, covering the time period from January 11, 2025, to January 25, 2025. It includes a symmetric timeframe around the release of the new meme coin (January 18, 2025), facilitating comparative analysis before and after the event.

The formula for calculating the yield is as follows:

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 is the point at which the release of the new meme coin is officially announced. Calculate cumulative abnormal returns 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, to represent a relatively stable sample period. Then, subtract this benchmark from the actual returns during the sample period to obtain excess returns over the market benchmark, and accumulate these to derive CARs.

method

The study uses the BEKK-MGARCH model to analyze the impact of the launch of new meme coins on the Crypto Assets market. It is assumed that the log returns follow a normal distribution with a mean of zero and a conditional covariance matrix of Ht, and the model is 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 infection effect test is conducted. Considering the potential first-type error issues when using high-frequency data, a more stringent significance level α=0.001 is adopted.

Result

volatility spillover effect

Preliminary analysis results indicate that the interrelation among assets significantly strengthened in the phase following the event. This finding supports the hypothesis that “the event triggered a volatility spillover effect.” Meanwhile, the volatility of the stable logarithmic returns increased, reflecting a rise in market instability and a faster adjustment speed. The returns of various crypto assets experienced severe fluctuations during this event, further emphasizing the systemic impact of this incident.

The dynamic conditional covariance estimation results 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 such as ETH, SOL, and LINK, where the covariance significantly increases, showing stronger interdependence and a higher degree of market integration. In contrast, while SHIB and DOT also reached a significant level of 0.01, their impact is relatively weak. Additionally, some assets like LTC and XRP experienced a decline in covariance after the event, indicating that the spillover effects are 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.

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information cascading effect

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

In the pre-event phase, 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 next president. 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.

In the stage after the event occurs, three key dynamics are particularly prominent:

1. SOL has performed exceptionally well, surpassing all other assets, which is likely related to its direct technological connection as a new meme coin carrying blockchain.

2. LINK has also performed strongly, possibly related to its correlation with large American tech companies.

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

Meanwhile, DOGE and other meme coins like SHIB appear particularly weak, showing a clear asset substitution effect, where speculative funds are shifting from old meme coins to newly issued tokens. Although AVAX and DOT have solid technical foundations, they have not been immune to such capital transfer trends, showing signs of value loss.

The issuance of the new meme coin has disrupted the market co-movement pattern prior to the event due to this exogenous shock. Before the event, there was a high level of coordinated volatility among various assets; however, after the event, the CARs of different assets exhibited significant divergence, ranging from +20% for Solana to -20% for Dogecoin and Shiba Inu.

These results reveal that asset-specific narratives, technological relevance, and investors' subjective perceptions can significantly amplify the differences in asset returns in response to 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 benefited significantly due to its direct technical association with the new meme coin. Additionally, assets sharing the same underlying blockchain infrastructure were also boosted by riding the “coattails” of this event.

At the same time, mainstream crypto assets such as Bitcoin and Ethereum show stronger stability due to their core position in the market, playing a similar anchoring role in this incident, stabilizing the overall market structure. This indicates that investor sentiment is no longer solely dependent on fundamental technical factors, but has also begun to be significantly influenced by geopolitical and policy narratives, especially when these narratives are conveyed 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 increasingly intertwine 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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