How Political-Linked Tokens Affect the Crypto Assets Market: Taking Trump Meme Coin as an Example

A recent study analyzed the impact of Trump issuing Meme coins on the crypto assets market. The research revealed the heterogeneous volatility spillover effects driven by market sentiment and fundamentals, with political signals amplifying speculative dynamics, highlighting the increasing role of political factors in shaping the crypto assets market and investor behavior.

Introduction

Political dynamics are increasingly influencing financial markets, and the cryptocurrency market has become a significant arena where politics and finance intersect. The 2024 U.S. presidential election further highlights this relationship, as Republican candidate Trump has unprecedentedly shifted to support digital assets. He claims to make the U.S. the "crypto capital of the world" and has placed cryptocurrency at the core of his economic agenda, leading the market to anticipate a more friendly policy stance during his term.

These are expected to be realized on January 18, 2025. Trump issued his official Meme coin ($TRUMP) on the Solana blockchain. Within 24 hours, the price of $TRUMP skyrocketed by 900%, trading volume reached 18 billion USD, and its market capitalization surpassed that of the then largest Meme coin DOGE by 4 billion USD.

The next day, the issuance of the Meme coin $MELANIA, associated with the First Lady, further boosted 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 of broader regulatory and political agendas.

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

• How does the release of $TRUMP affect the returns and volatility of major Crypto Assets?
• Did this event trigger a financial contagion effect in the Crypto Assets market?
• Does this impact exhibit heterogeneity, manifested in different responses from various crypto assets based on their technological foundations, use cases, or speculative appeal?

To answer these questions, the research employs the BEKK-MGARCH model to analyze the dynamic relationship between volatility and correlation over time.

Research has found that after the release of Trump Meme Token, 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 showed strong resilience, with their cumulative abnormal returns (CARs) and variance stabilizing in the later stages of the event. In contrast, other Meme Tokens such as Dogecoin and Shiba Inu experienced depreciation, with funds likely shifting towards $TRUMP.

The issuance of $TRUMP took place in an environment of high political polarization in the United States, and the Trump brand itself is closely related to strong political sentiments, which increases investor sensitivity and exacerbates market reactions. For some investors, Trump's endorsement symbolizes a unique speculative opportunity, giving rise to a strong "herding effect"; while other investors, aware of the political and regulatory risks due to his controversial image, adopt a more cautious stance. This polarization explains the observed high volatility and differentiated market reactions—from enthusiasm for expected political support to skepticism about reputation and political uncertainty.

In recent years, the contagion effect in the Crypto Assets market has garnered increasing attention due to its significant implications for financial stability, risk management, and portfolio diversification. Existing studies primarily focus on the spillover between Crypto Assets themselves or between Crypto Assets and traditional financial assets, revealing patterns of connectivity, contagion risk, and volatility transmission. However, most of these studies concentrate on financial or technical triggers, such as market crashes, liquidity constraints, or blockchain innovations. The political signals, especially those related to the contagion mechanisms of politically connected Tokens, remain a research gap.

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 affect decentralized financial markets. Moreover, unlike previous research that has often focused on negative shocks, this study 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 the dynamics of financial contagion.

Data and Methods

2.1 Data and Sample Selection

This study uses proprietary data of closing mid-prices per minute, covering the most representative 10 of the top 20 crypto assets 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 comes from a certain exchange, which is a widely used centralized trading platform in the US in previous studies.

The dataset contains a total of 20,160 observations, covering the time period from January 11, 2025, to January 25, 2025. It includes a symmetrical time frame around the release of Trump's official Meme coin on January 18, 2025, (, facilitating comparative analysis before and after the event.

According to the practices in existing literature, this study uses the following formula to calculate Crypto Assets returns:

Yield = ln)Pt ∕Pt−1(

Where Pt represents the price of the digital asset at time t.

The event time is defined as January 18, 2025, Coordinated Universal Time ) UTC ( at 2:44 AM, which marks the official release of the new U.S. President's official Meme Token. Cumulative abnormal returns are calculated to assess the information cascading effect. This article calculates the average benchmark return for each Crypto Asset from January 1 to January 10, 2025, to represent a relatively stable sample period. Then, the benchmark is subtracted from the actual returns during the sample period to obtain the excess returns over 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 Trump Meme Token on the Crypto Assets market. Assume that the logarithmic returns follow a normal distribution with a mean of zero and a conditional covariance matrix of Ht, the model is set as follows:

yt|Ωt−1 ∼ N###0,Ht(

Ht = C′C + A′ε t−1ε′t−1A + B′Ht−1B

Among them,

C =

c11 0 ⋯ 0 c21 c22 ⋯ 0 ⋮ ⋮ ⋱ ⋮ cn1 cn2 ⋯ cnn

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 Type I error issue when using high-frequency data, this paper adopts a stricter significance level of α=0.001.

Result

) 3.1 Volatility Spillover Effect

The preliminary analysis results reveal the interrelationships between crypto assets, which are estimated through the BEKK-MGARCH model. In the covariance structure, the interconnection between assets significantly enhances in the phase after the event occurs. This finding supports the hypothesis that "the event triggered volatility spillover effects." Similarly, the increase in the volatility of stable logarithmic returns reflects a phenomenon of rising market instability and accelerating adjustment speed. All images show that the returns of each crypto asset experienced severe fluctuations during the event, further emphasizing the systemic impact of this event.

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The dynamic conditional covariance results estimated by the BEKK-MGARCH model indicate that the event indeed triggered financial contagion and volatility spillover effects in the Crypto Assets market. Most of the covariance coefficients in the later stages of the events are significant at the 0.001 significance level, especially among assets like ETH, SOL, and LINK, where the covariance significantly increased, showing stronger interdependence and a higher degree of market integration. In contrast, although SHIB and DOT also reached a significance level of 0.01, their impact was weaker. 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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3.2 Information Cascading Effect

Based on the confirmed heterogeneity effects among crypto assets, this section further reveals the information cascade effects triggered by the issuance of Trump Meme coin through the analysis of cumulative abnormal returns ###CARs(. The results indicate that this event has a significant structural impact on market dynamics, manifested as asset-specific response paths and increased volatility.

In the pre-event phase, most crypto assets experienced positive gains, possibly driven by speculative expectations or the market's optimistic attitude towards Trump 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:

• SOL performed excellently, surpassing all other assets, which is likely related to its direct technological connection as the blockchain that carries Trump Meme coin.

• LINK also performed strongly, possibly related to its correlation with large American technology companies.

• Mature crypto assets such as Bitcoin, Ethereum, Ripple, and Litecoin have gradually stabilized after experiencing moderate increases, reflecting their market resilience and relative insulation from cascading speculative impacts.

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Meanwhile, DOGE and other Meme coins like SHIB appear particularly weak, exhibiting a clear asset substitution effect, where speculative funds have shifted from the old Meme coins to the newly issued Trump Token. Despite AVAX and DOT having solid technical foundations, they have also not been spared from this trend of capital transfer, showing signs of value loss.

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The issuance of Trump Meme coin has disrupted the market's co-movement pattern before the event due to this exogenous shock. Prior to the event, there was a high level of co-volatility among various assets; however, after the event, the CARs of different assets showed significant divergence, ranging from +20% for Solana to -20% for Dogecoin and Shiba Inu.

These results reveal that asset-specific narratives, technical 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 related to political figures on the crypto market, focusing on the volatility spillover effect and the information cascade effect.

Research results show that the market's reaction to this event exhibits significant heterogeneity. For example, due to the direct technical association with Trump Meme coin, SOL benefited significantly. Additionally, assets sharing the same underlying blockchain infrastructure also received a boost by hitching a "free ride" on this event.

At the same time, mainstream crypto assets such as Bitcoin and Ethereum,