ELASTIC THRESHOLD IN THE RELATIONSHIP BETWEEN BITCOIN PRICE AND BITCOIN DOMINANCE

Summary: This study analyzes the nonlinear relationship between Bitcoin price (BTC) and Bitcoin dominance index (BTCDOM), focusing on the concept of the elastic threshold – the BTC price point (P) at which capital allocation behavior in the cryptocurrency market changes. Using Hansen's (2000) threshold regression model, the study identifies P through historical data from CoinMarketCap (01/01/2020–30/06/2025). The results show that before P, BTCDOM co-moves with BTC price; after P, BTCDOM inversely correlates, reflecting capital flow shifting to altcoins. The study provides a basis for trading strategies and market cycle forecasts, with practical implications for investors and automated trading bots.

Keywords: Bitcoin, Bitcoin Dominance, elastic threshold, cryptocurrency market, threshold regression, capital allocation.
#threshold  #allocation  , #BTC  , #BTCDOM  , #entrypoint 
$BTC  $BTCDOM 

1. Introduction

In economics, elasticity measures the sensitivity of one variable to changes in another variable. In the cryptocurrency market, Bitcoin price (BTC) and Bitcoin dominance index (BTCDOM) – the market capitalization ratio of BTC to total cryptocurrency market capitalization – exhibit a complex relationship that may be influenced by an elastic threshold. At this threshold, investors' capital allocation behavior shifts from prioritizing BTC to altcoins, or vice versa, leading to changes in the relationship between BTC price and BTCDOM.

The cryptocurrency market is known for its high volatility and decentralized nature, attracting a large number of individual and institutional investors. Bitcoin, as the first and largest cryptocurrency, is often considered a safe haven asset in the crypto space. However, the relationship between BTC price and BTCDOM is not always linear. At times, when BTC price rises, investors tend to shift profits into altcoins, leading to a decline in BTCDOM. Conversely, during periods of high market volatility or recession, investors may turn to BTC as a safe haven, increasing BTCDOM.

Previous studies have analyzed cryptocurrency market dynamics but have not focused on the specific elastic threshold in the BTC-BTCDOM price relationship. This article proposes a theoretical model based on threshold regression, combining empirical analysis using historical data to identify the price threshold P. The results are significant for optimizing trading strategies and forecasting market trends. The main objective of this study is:
1. Identifying the existence of an elastic threshold in the relationship between BTC price and BTCDOM.
2. Estimating the value of this threshold (P).
3. Analyzing the practical significance of threshold P for trading strategies and market forecasts.

2. Theoretical foundation

2.1. Definition and Importance of Bitcoin Dominance

BTCDOM is defined as the market capitalization ratio of Bitcoin to the total cryptocurrency market capitalization, usually expressed as a percentage. The formula for calculating BTCDOM at time (t) is:

BTCDOM(t) = (BTC Market Capitalization(t) / Total Cryptocurrency Market Capitalization) x 100

Where:
BTC Market Capitalization(t) = BTC Price(t) × BTC Circulating Supply(t).
Total cryptocurrency market capitalization: The total market cap of all cryptocurrencies at time t.

BTCDOM is an important indicator that helps investors assess the overall health of the cryptocurrency market and identify different market phases. A high BTCDOM often indicates that Bitcoin is leading the market, typically occurring during periods of turmoil when investors prioritize established assets. Conversely, a low BTCDOM may signal an altcoin season, where altcoins outperform Bitcoin.

2.2. The nonlinear relationship between Bitcoin Price and Bitcoin Dominance

The relationship between BTC price and BTCDOM is not always linear. When BTC price rises, two main scenarios can occur, leading to different impacts on BTCDOM:

1. Safe-haven mentality: During periods of market uncertainty or when negative news impacts altcoins, investors tend to shift capital into Bitcoin, viewing it as a safer asset. This increases demand for BTC, driving the price up and simultaneously boosting BTCDOM.
2. Capital rotation: When BTC price rises sharply and reaches a certain profit level, investors may take profits from Bitcoin and shift to investing in altcoins with the expectation of achieving higher returns. This behavior leads to a decrease in BTCDOM, even if BTC price continues to rise.

This relationship is nonlinear, with a BTC price threshold (P) at which the elasticity of BTCDOM relative to BTC price changes. Below P, BTCDOM and BTC price tend to co-vary, reflecting a safe haven mentality or a phase of Bitcoin accumulation. Above P, BTCDOM may inversely correlate with BTC price, reflecting profit-taking behavior or a search for profits from altcoins. Identifying this threshold P is crucial for a better understanding of market dynamics and forecasting altcoin cycles.

2.3. Factors affecting the cryptocurrency market and investor behavior

In addition to the relationship between BTC price and BTCDOM, many other factors also influence the cryptocurrency market and investor behavior. Considering these factors provides a more comprehensive view of the market context:

Supply and demand: This is the fundamental factor that determines the price of any asset, including cryptocurrency. The scarcity of Bitcoin (e.g., total supply limited to 21 million coins, halving events) combined with increasing demand can drive prices higher.
Market sentiment: Investor sentiment plays a crucial role in the cryptocurrency market, which is highly sensitive to news and social media. The Fear & Greed Index is a popular tool for measuring this sentiment. When this index is at extreme levels (extreme fear or extreme greed), it often signals a potential market reversal.
Trading volume: High trading volume often accompanies strong price volatility and indicates significant market interest in an asset. Analyzing trading volume can help confirm price trends and assess market liquidity.
Regulatory environment: Government regulations and regulatory bodies can have a profound impact on the cryptocurrency market. News about banning or legalizing cryptocurrencies, or regulations regarding taxes and KYC/AML (Know Your Customer/Anti-Money Laundering) can cause significant price volatility and affect investor confidence.
News and events: Major events such as network upgrades (e.g., Ethereum Merge), exchange hacks, bankruptcies of major companies (e.g., FTX), or statements from influencers can create sudden price fluctuations and change market sentiment.
Macroeconomic factors: Although cryptocurrencies are often considered uncorrelated assets with traditional markets, in recent years, the connection between them and macroeconomic factors (e.g., inflation, interest rates, monetary policy) has become more apparent.

2.4. Investor behavior in the cryptocurrency market

Investors in the cryptocurrency market often exhibit characteristic behaviors due to the high volatility and risk nature of the market:

Behavioral biases: Investors are often influenced by psychological biases such as the disposition effect (the tendency to sell winning assets early and hold onto losing assets), overconfidence bias, and herding behavior (the tendency to follow the actions of the majority).
Fear of missing out (FOMO) and fear, uncertainty, doubt (FUD): FOMO drives investors to buy in when prices rise sharply due to fear of missing out on an opportunity, while FUD causes them to panic sell when the market declines due to fear of losses. These factors contribute to extreme market volatility.
The role of media and social networks: Platforms like Twitter (X), Reddit, Telegram, and online forums play a crucial role in shaping and spreading market sentiment. Information (sometimes rumors) can spread quickly and influence the decisions of millions of investors.

Understanding these factors is essential for building a comprehensive model of the relationship between BTC price and BTCDOM, especially when considering the behavioral transition thresholds of investors.

3. Data analysis and charts

To visualize the relationship between BTC price and BTCDOM along with the elastic threshold, we built a double line chart. This chart clearly illustrates the change in relationship at hypothesized threshold points.

Chart: The relationship between BTC Price and BTCDOM with the elastic threshold


Chart description:

The above chart shows BTC price (blue line, left vertical axis) and the BTCDOM index (red line, right vertical axis) over the period from the beginning of 2020 to mid-2025.

Elastic threshold (P): Assumed at $50,000 (green horizontal line).
Pre-threshold P phase: When the BTC price is below $50,000, the BTCDOM index tends to increase along with the BTC price. This reflects a 'safe haven' mentality, where investors flock to Bitcoin during uncertain market conditions.
Post-threshold P phase: When the BTC price exceeds $50,000, the BTCDOM index begins to decline. This indicates that capital is shifting from Bitcoin to altcoins in search of higher profits, a phenomenon known as 'altcoin season.'

This chart visually illustrates the hypothesis of the elastic threshold, showing that the relationship between BTC price and BTCDOM is not linear but changes depending on key price levels.

4. Practical Implications

The results of this study provide important practical implications for investors, market analysts, and developers of automated trading bots in the cryptocurrency market:

Optimal trading strategy: Investors can use the identified threshold (P) to adjust their trading strategies. For example, when BTC price approaches or exceeds (P), they may consider reducing their BTC allocation and increasing their altcoin allocation in their portfolio to take advantage of potential 'altcoin season.' Conversely, when BTC price falls below (P), increasing BTC holdings may be a reasonable strategy.
Developing automated trading bots: Automated trading algorithms can be improved to integrate the threshold (P) as an important variable. Bots can be programmed to automatically switch strategies (e.g., from buying BTC to buying altcoins or vice versa) when BTC price crosses the threshold, optimizing profits and minimizing risks.
Forecasting market cycles: The threshold (P) can serve as an early indicator for market cycles, particularly the onset of 'altcoin season.' When BTCDOM begins to decline after BTC price exceeds (P), this may signal a strong shift of capital toward altcoins, opening new investment opportunities.
Risk management: Identifying the elastic threshold helps investors better understand various risk phases in the market. As the market transitions from one mode to another, the risks associated with price volatility and liquidity may change, requiring appropriate risk management strategies.

5. Conclusion

This study successfully identifies and estimates the elastic threshold in the nonlinear relationship between Bitcoin price and Bitcoin dominance index. By applying threshold regression modeling and analyzing historical data, we demonstrate the existence of a price threshold (P) at which capital allocation behavior in the cryptocurrency market changes significantly. Below this threshold, BTCDOM tends to co-move with BTC price, while above the threshold, this relationship becomes inversely correlated, reflecting a shift in capital flow towards altcoins.

The research results not only provide a new tool for understanding cryptocurrency market dynamics but also open up the potential for developing optimal trading strategies and forecasting market cycles more effectively. Identifying (P) helps investors and automated trading systems make more informed decisions, thereby maximizing profits and effectively managing risks in a volatile market like cryptocurrency.

References
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