Study on Political Signals and Contagion Effects in the Crypto Assets Market

Recently, the journal Economics Letters published a research paper titled "From Zero to Hero: The Spillover Effects of Meme Coins in the Crypto Assets Market." The paper analyzes the event of a well-known political figure issuing a Meme coin, revealing the heterogeneous volatility spillover effects driven by both 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 U.S. presidential election further highlights this relationship, as a Republican candidate has unprecedentedly shifted to support digital assets. He claims he will make the U.S. the "global capital of Crypto Assets" and place Crypto Assets at the core of his economic agenda, leading to market expectations of more favorable policies during his term.

These are expected to be realized on January 18, 2025, when the politician issued their official Meme coin on the Solana blockchain. Within 24 hours, the price of this token surged by 900%, with a trading volume reaching $18 billion, and its market capitalization exceeding that of the largest Meme coin at the time, DOGE, by $4 billion.

The next day, the issuance of another 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, with impacts that extend beyond the realm of 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. It primarily 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 in the Crypto Assets market?

3. Does this impact exhibit heterogeneity, manifesting as 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.

The study selected the top ten cryptocurrencies by market capitalization for empirical analysis and found that after the release of the Meme coin, there was 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 cryptocurrencies such as Bitcoin and Ethereum demonstrated strong resilience, with their cumulative abnormal returns (CARs) and variance tending to stabilize in the later stages of the event. In contrast, other Meme coins like Dogecoin and Shiba Inu experienced depreciation, suggesting that funds likely shifted towards the newly issued Meme coins.

Indeed, the issuance of this Meme coin occurred in an environment of heightened political polarization in the United States, and the brand associated with it is closely linked 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"; while others, due to its controversial image, become aware of the political and regulatory risks, adopting a more cautious stance. This polarization explains the observed high volatility and differentiated market reactions—from enthusiasm for expected political support to skepticism regarding reputation and political uncertainty.

In recent years, the contagion effects in the cryptocurrency market have increasingly attracted attention due to their significant implications for financial stability, risk management, and portfolio diversification. Existing research primarily focuses on spillovers between cryptocurrencies themselves or between cryptocurrencies 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. Political signals, especially the contagion mechanisms related to politically connected tokens, remain a research gap.

This study is the first paper 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. Additionally, unlike previous studies that focused mainly on negative shocks such as the Bitcoin price crash, the Terra-Luna collapse, or the bankruptcy of certain trading platforms or Silicon Valley Bank, 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 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 crypto assets by market capitalization: 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 certain exchange, which is a US centralized trading platform widely used in previous studies, 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, encompassing a symmetric time frame of one week before and after the release of the Meme coin on January 18, 2025, (, to facilitate comparative analysis before and after the event.

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

Yield = ln ) P t ∕ P t − 1(

Among them, P t 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. This point marks the official release of the new U.S. president's meme coin. Cumulative abnormal returns are calculated to assess the information cascade effect. This article calculates the average benchmark return for each crypto asset from January 1, 2025, to January 10, 2025, to represent a relatively stable preliminary sample. Then, the benchmark is subtracted from the actual returns during the sample period to derive excess returns over the market benchmark, and CARs are obtained 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 log 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 model's stability and positive definiteness. Subsequently, the contagion effect test is conducted. Considering the potential Type I error issues that may arise when using high-frequency data, this paper adopts a stricter significance level α = 0.001.

Result

( 3.1 Volatility Spillover Effect

The chart in this section presents preliminary analysis results to reveal the interrelationships among Crypto Assets, which are estimated through the BEKK-MGARCH model. In the covariance structure illustrated in figure 1)b###, the interconnections between the assets significantly strengthened in the post-event phase. 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 rise in market instability and a faster adjustment speed. All right-side panels of the images indicate that the returns of each Crypto Asset experienced severe fluctuations during this event, further emphasizing the systemic impact of this occurrence.

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Table 1 presents the dynamic conditional covariance estimated through the BEKK-MGARCH model, along with the corresponding t-test statistics to verify the existence of contagion effects. The results indicate that the event did indeed trigger financial contagion and volatility spillover effects in the Crypto Assets market. Most covariance coefficients in the later stages after the event are significant at the 0.001 significance level, particularly among assets like ETH, SOL, and LINK, where the covariance significantly increased, demonstrating 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 evenly 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 cascading effects triggered by the issuance of this 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 the potential election of this political figure as the 47th President of the United States. This indicates that even in the absence of concrete information, investors have exhibited 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 relationship as the underlying blockchain for that Meme coin.

2. LINK has also performed strong, possibly 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 experiencing a moderate rise, reflecting their market resilience and relative insulation from the effects of cascading speculation.

At the same time, other Meme coins like DOGE and SHIB appear particularly vulnerable, showing a clear asset substitution effect, where speculative funds have shifted from old Meme coins to newly issued tokens. Despite AVAX and DOT having a solid technical foundation, 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 disrupts the market co-movement pattern prior to the event due to this exogenous shock. Before the event, there was a high degree of synchronous volatility among various assets; however, after the event occurred, 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 response of returns between assets during major informational shocks.

Conclusion

This study examines the issuance of Crypto Assets related to political figures ### such as the President of the United States ( and its impact on the crypto market.