How Political Trends Affect the Crypto Assets Market: A Case Study of Trump Meme Coin

A recent study 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 increasing day by day, and the crypto assets market has become an important area where politics and finance intersect. The 2024 U.S. presidential election further highlights this relationship, as a certain Republican candidate has unprecedentedly turned to support digital assets. He claims he will make the U.S. the "crypto asset capital of the world" and place crypto assets at the core of his economic agenda, leading the market to expect a more friendly policy stance during his term.

These are expected to be realized on January 18, 2025, when the candidate issues its official Meme coin on the Solana blockchain. Within 24 hours, the coin's price skyrocketed by 900%, with a trading volume reaching 18 billion USD, surpassing the market value of the largest Meme coin at that time, DOGE, by more than 4 billion USD.

The next day, the issuance of the Meme coin 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 impact of which extends 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:

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 within the crypto assets market?

3. Does this impact have heterogeneity, manifested in different Crypto Assets responding differently based on their technological foundations, uses, or speculative appeal?

To answer these questions, this article adopts 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.

This article selects the top ten crypto assets by market capitalization for empirical research and finds that after the release of this 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 ties. In contrast, mainstream crypto assets such as Bitcoin and Ethereum showed strong resilience, with their cumulative abnormal returns (CARs) and variance stabilizing in the later stages of the event. Conversely, other Meme coins like Dogecoin and Shiba Inu experienced depreciation, and funds likely shifted towards the newly issued Meme coins.

Indeed, the issuance of this Meme coin occurs in a highly politically polarized environment in the United States, and the brand associated with it is closely tied to strong political sentiments, thereby increasing investor sensitivity and exacerbating market reactions. For some investors, this endorsement symbolizes a unique speculative opportunity, giving rise to a strong "herding effect"; whereas 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 reactions—from enthusiasm for anticipated political support to skepticism regarding reputation and political uncertainty.

In recent years, the contagion effects in the Crypto Assets market have received increasing attention due to their significant implications for financial stability, risk management, and portfolio diversification. Existing research has mainly focused on the spillovers 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 have concentrated on financial or technical triggers, such as market crashes, liquidity constraints, or blockchain innovations. Political signals, especially the contagion mechanisms related to 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. Additionally, unlike previous research that has focused mainly on negative shocks, this study focuses on the impact of positive shocks driven by political signals on the market. Notably, there is evidence suggesting 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

2.1 Data and Sample Selection

This study uses proprietary data of the close mid-price ( close mid-price ) per minute, covering the most representative 10 of the top 20 market capitalization ranked Crypto Assets: Bitcoin ( Bitcoin, BTC ), Ethereum ( Ethereum, ETH ), Ripple ( Ripple, XRP ), Solana ( SOL ), Dogecoin ( Dogecoin, DOGE ), Chainlink ( LINK ), Avalanche ( AVAX ), Shiba Inu ( Shiba Inu, SHIB ), Polkadot ( DOT ), and Litecoin ( Litecoin, LTC ). The data comes from a centralized trading platform in the United States, which has been widely used in previous research, with specific data obtained from the LSEG Tick History database.

This dataset contains a total of 20,160 observations, covering the time period from January 11, 2025, to January 25, 2025. It encompasses a symmetric time span of one week before and after the release of the Meme coin on January 18, 2025, allowing for comparative analysis before and after the event.

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

Yield = ln(Pt / Pt-1)

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

The event time is defined as January 18, 2025, Coordinated Universal Time ( UTC ) at 2:44 AM, marking the official announcement of the new US president's Meme coin release. Cumulative abnormal returns are calculated to assess the information cascade effect. This article calculates the average benchmark return of each Crypto Asset from January 1, 2025, to January 10, 2025, to represent a relatively stable pre-sample. Then, the benchmark is subtracted from the actual returns during the sample period to obtain excess returns over the market benchmark, and CARs are derived through accumulation.

( 2.2 Method

Use the BEKK-MGARCH model to analyze the impact of the launch of this Meme coin 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:

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Among them,

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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, the contagion effect test is conducted. Considering the potential Type I error issue when using high-frequency data, this article adopts a stricter significance level of α = 0.001.

Result

) 3.1 Volatility Spillover Effect

This section's chart provides preliminary analysis results to reveal the interrelationships between crypto assets, which are estimated through the BEKK-MGARCH model. In the covariance structure shown in Figure 1###b###, the interconnections between assets significantly increase in the phase following the event. This finding supports the hypothesis that "the event triggered a volatility spillover effect." Similarly, Figure 1(a) shows an increase in the volatility of stationary log returns during the same period, reflecting a phenomenon of rising market instability and accelerated adjustment speed. All the right-side panels of the images display that the returns of various crypto assets experienced significant fluctuations during this event, further emphasizing the systemic impact of this event.

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Table 1 displays the dynamic conditional covariance estimated by the BEKK-MGARCH model, along with the corresponding t-test statistics to verify the presence of contagion effects. The results 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 level, particularly between assets like ETH, SOL, and LINK, where the covariance significantly increased, demonstrating stronger interconnectedness and a higher level 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 saw a decline in covariance post-event, indicating that the spillover effects are not uniformly distributed across 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 effect triggered by the issuance of the Meme coin through the analysis of cumulative abnormal returns )CARs###. The results indicate that the event has a significant structural impact on market dynamics, manifested as asset-specific response paths and increased volatility.

Figure 2 shows the CARs of the analyzed Crypto Assets during the sample period. In the pre-event phase, most coins 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 consistent with the widely documented "fear of missing out" characteristic in the Crypto Assets market.

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

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

2. LINK also performed strongly, which may be related to its association with the large American technology company Oracle.

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

At the same time, other Meme coins like DOGE and SHIB appear particularly weak, exhibiting a clear asset substitution effect, where speculative funds are shifting from old Meme coins to newly issued tokens. Despite AVAX and DOT having solid technical foundations, they have also not escaped this trend of capital transfer, showing signs of value loss.

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Figure 3 further clarifies how the issuance of this Meme coin as an exogenous shock broke the pre-event market co-movement pattern. Before the event, there was a high level of synchronous volatility among 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.

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This section reveals that asset-specific narratives, technological relevance, and investors' subjective perceptions can significantly amplify the differential responses of asset returns when major information shocks occur.

Conclusion

This study examines the impact of cryptocurrency issuance associated with political figures (, such as the President of the United States ), on the crypto assets market, focusing on the volatility spillover effect and the information cascade effect.

Research results indicate that the market's reaction to this event exhibits significant heterogeneity. For example, due to the direct technical correlation with this Meme coin, SOL has benefited from it.