Shusheng Ding, Xiangling Wu, Tianxiang Cui, John W. Goodell · 5 authors
Climate change is a highly controversial topic within the socioeconomic context. Climate Policy Uncertainty (CPU) arises from the process of climate policies formulation and implementation. This uncertainty impacts financial market volatilities, including cryptocurrency markets . In this paper, we demonstrate the substantial role of CPU in forecasting volatilities in cryptocurrency markets using Genetic Programming (GP). Our study shows that different cryptocurrency markets respond differently to CPU across time scales. Our paper contributes to the literature by illustrating the impact of CPU on cryptocurrency market volatilities and analyzes it across different time horizons. Second, we build three volatility forecasting models for different cryptocurrency markets by incorporating CPU, which outperform traditional models. Our models can thereby illuminate portfolio construction and hedging strategies, providing valuable insights for investors and policymakers.
In the context of escalating climate change and mounting environmental challenges, green finance has emerged as a crucial mechanism for fostering sustainable development. This paper presents an experimental analysis that illustrates how the integration of blockchain technology into financial technology (fintech) strategies can significantly enhance the efficacy of green investments. Our proposed framework facilitates the optimization of these strategies by improving transparency and fund traceability in environmentally focused projects. Through rigorous testing and data-driven insights, we demonstrate the potential of blockchain to streamline financing processes, mitigate risks associated with fraudulent practices, and promote accountability among stakeholders. By establishing a synergistic relationship between fintech and ecological responsibility, this research provides a novel approach that contributes to both academic discourse and practical applications in green finance. The proposed approach showcases experimental originality by integrating blockchain technology with green finance, setting a precedent for future research in this interdisciplinary field. Our findings reveal that blockchain can significantly enhance the efficiency of financing processes, reducing transactional delays and fostering transparency that mitigates risks related to fraud. Moreover, this study highlights the potential of this synergistic model to cultivate a robust framework for accountability among stakeholders, ultimately guiding investment toward environmentally sustainable initiatives and bolstering the integrity of green financial practices.
Tarihin başlangıcından itibaren sürekli evrim geçiren para, insanlık tarafından geliştirilen en önemli araçlardan biridir. Para, insanların gelecekteki ve anlık ihtiyaçlarını karşılamak için belirlenen bir değeri temsil eder. Para kavramı, dönemin koşullarına ve imkânlarına göre farklı şekillerde ortaya çıkar. Kripto paraların temelleri 1980'lerde atılmış olup, 2008 yılında Satoshi Nakamoto tarafından Bitcoin'in tanıtılmasıyla hayatımıza girmiştir. Geleneksel paralara alternatif olarak ortaya çıkan kripto paralar, teknolojik bir yenilik olup her geçen gün daha da popüler hale gelmektedir. Bitcoin, merkezi bir otorite tarafından yönetilmeyen ilk kripto paradır ve popülerliği ve başarısı diğer alternatif kripto paraların oluşmasına yol açmıştır. Julong Deng tarafından 1982 yılında geliştirilen “Gri Sistem Teorisi”, belirsiz sistemlerin davranışlarını tahmin etmek için kullanılan bir yöntem olup GM (1,1) modeli en sıklıkla kullanılan gri modeldir. Bu çalışma, Bitcoin'in fiyatlarını GM (1,1) modeli kullanarak tahmin etmeyi amaçlamaktadır. Araştırma sonucunda, modelin gelecek dönem tahminleri için uygun olduğu ve başarılı tahminler yaptığı belirlenmiştir
This study explores the feasibility of Bitcoin as a legal currency and a store of value in comparison to traditional fiat currencies. Through a comprehensive literature review and discussion, the study examines Bitcoin’s core characteristics such as circulation limitations, scarcity, price stability, intrinsic value, and associated security risks. The analysis highlights key challenges, including Bitcoin’s limited acceptance in global commerce, high volatility, and the potential risks posed by its decentralized nature. While Bitcoin’s scarcity and technological innovation position it as a unique asset, its viability as a mainstream currency remains uncertain due to its lack of regulatory support and price stability. The paper concludes that although Bitcoin holds promise as a digital asset, it faces significant obstacles in replacing fiat currencies as a stable medium of exchange or a reliable store of value. Recommendations are provided for governments and institutions on regulatory approaches and the integration of cryptocurrencies into the existing financial system.
The rapid growth of decentralized finance (DeFi) has revolutionized the global financial landscape, providing decentralized alternatives to traditional financial services. This study investigates the asymmetric multifractal behavior of nine DeFi markets—AAVE, Pancake Swap (CAKE), Compound (COMP), Curve Finance (CRV), Maker DAO (MKR), Synthetix (SNX), Sushi Swap (SUSHI), UniSwap (UNis), and Yearn Finance (YFI)—using Asymmetrical Multifractal Detrended Fluctuation Analysis (A-MFDA). The use of generalized Hurst exponents, Rényi exponents, and singularity spectrum functions revealed that DeFi markets exhibit multifractal behaviors. The analysis uncovered clear differences between uptrend and downtrend fluctuation functions, highlighting asymmetric multifractal behavior. The asymmetry intensity was analyzed through excess differences in uptrend and downtrend generalized Hurst exponents. AAVE, COMP, SNX, UNis, SUSHI, and MKR exhibit negative asymmetry, with stronger correlations during negative trends. CAKE shifts from positive to negative asymmetry, showing sensitivity to both trends. CRV is more volatile in negative trends, while YFI consistently displays positive asymmetry across market fluctuations. The results also reveal that long-term correlations and heavy-tailed distributions contribute to the multifractality of DeFi assets. This study highlights the need for dynamic risk management in DeFi markets, urging investors to adopt adaptive strategies for volatile assets and prepare for sudden price fluctuations to safeguard investments.
Whilst previous studies have primarily focused on the hedge effects and co-movements between cryptos and traditional assets, cryptos’ features that are associated with hedge effects and co-movements have often been neglected in extant studies. This research aims to investigate how specific cryptocurrency features influence their dynamic volatility and co-movements with stock markets. Using cointegration analysis and Granger causality tests, we explore the hedge effects and co-movement between the top 100 cryptos and eight leading stock markets. Additionally, we use logistic regression models to assess the role of crypto-specific features in driving these dynamics. We find that consensus mechanisms and having limited supply are key features influencing co-movements during and after the Covid-19 pandemic, while acting as a means of payment predominantly affects co-movement after the pandemic. We highlight cryptos underlying characteristics and functionalities that could significantly affect their demand and people’s attitudes toward them. Based on finance theory, these differing characteristics could affect cryptos’ versatility thereby impacting their demand, pricing, hedge effects and co-movement in their returns compared to stock returns. This paper makes significant theoretical contributions by addressing the role of crypto features in their co-movements and hedge effects on representative stock markets.
• Matching trading periods and investment horizons between equities and cryptocurrencies are fundamentally challenging. • Monday returns and intermarket connectedness of cryptocurrencies notably differ when alternative benchmark (closing) prices are used. • Using inconsistent return estimation methods from different sources delivers spurious intermarket connectedness results. • THETA, GNO, GLM, ENJ, WAXP, KCS, and WAVES are most vulnerable to the return estimation method. • Seemingly inconsequential choices critically affect the main conclusions drawn by the existing studies on market interconnectedness. Cryptocurrencies trade continuously, unlike traditional assets limited to weekdays, creating challenges in calculating Monday returns. This paper investigates the impact of four benchmark closing prices—Friday, Saturday, Sunday, and a weekend average—on intermarket connectedness. Analyzing 72 cryptocurrencies (2018–2024) and their relation to the S&P500 using the TVP-VAR model, we find significant variations in economic and statistical outcomes, influencing both the magnitude and direction of spillovers. Mixed log- and non-log-based return methods yield inconsistent results for specific cryptocurrencies like THETA, GNO, GLM, and WAVES. These findings highlight the critical importance of consistent return methodologies in cryptocurrency market analysis.
Especially new generation investors may prefer to use stocks of popular companies that use advanced technologies and cryptocurrencies as investment instruments. Gold, one of the classical investment instruments, still maintains its place among the commodity assets in the portfolios of investors around the world. These asset groups were evaluated in this study. As the first group investment tool, decacorn and hectocorn technology companies called the new generation the magnificent five; Company stock returns of Apple, Microsoft, Amazon, Alphabet, Nvidia Corporation and Tesla were analyzed. In addition, as the second financial asset, cryptocurrencies, which are used as investment instruments as well as being used in daily life with the evolution of technology, and Bitcoin (BTC), which remains popular among these cryptocurrencies, were the subject of the study. Finally, the study evaluated gold mines, one of the world's oldest valuable investment instruments, compared with other financial assets. The study examined the magnificent five stocks, BTC and gold ounce prices between the periods of 2020:01 and 2023:12, using mutual cointegration, vector error correction (VEC) and Granger causality analyses. Findings of the study; Short-term shocks caused by variables in BTC stabilise after about a month. In this process, as NVDA shares increase, BTC value decreases, and as gold value increases, BTC value increases.
Yang Zhou, Chi Xie, Gang‐Jin Wang, Jue Gong · 5 authors
Abstract Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways. Its increasingly complex interactions with the conventional financial market make precisely forecasting its volatility increasingly challenging. To this end, we propose a novel framework based on the evolving multiscale graph neural network (EMGNN). Specifically, we embed a graph that depicts the interactions between the cryptocurrency and conventional financial markets into the predictive process. Furthermore, we employ hierarchical evolving graph structure learners to model the dynamic and scale-specific interactions. We also evaluate our framework’s robustness and discuss its interpretability by extracting the learned graph structure. The empirical results show that (i) cryptocurrency volatility is not isolated from the conventional market, and the embedded graph can provide effective information for prediction; (ii) the EMGNN-based forecasting framework generally yields outstanding and robust performance in terms of multiple volatility estimators, cryptocurrency samples, forecasting horizons, and evaluation criteria; and (iii) the graph structure in the predictive process varies over time and scales and is well captured by our framework. Overall, our work provides new insights into risk management for market participants and into policy formulation for authorities.
Gabriel A. Giménez Roche, Antoine Noël, Loïc Sauce
We analyze the determinants of Bitcoin (BTC) trade volume in decentralized exchanges (DEXs) and test the claim that BTC trades on these platforms are censorship-resistant. The study finds that overall economic freedom, particularly monetary freedom, correlates indirectly with BTC trade volumes, while capital restrictions on residents' transactions abroad correlate in two different directions. Purchase transactions inversely correlate with BTC volume in DEXs, while sales transactions correlate directly. These results suggest that BTC can be used to hedge against poor institutional frameworks, particularly against poor monetary governance, and as a vehicle for institutional hedging against repressive capital controls and institutional failures. The study's originality lies in its use of on-chain panel data on the volume of BTC transactions, which are country-specific and allow for comparing the impact of country-specific socio-institutional variables on BTC volumes. • Decentralized exchanges leverage blockchain for innovative financial services. • BTC provides an institutional hedging option against poor governance frameworks. • On-chain data reveal BTC country dynamics and institutional hedging potential.
Within the framework of high-frequency volatility modeling, this study investigates the realized volatility spillover dynamics across major cryptocurrencies over an extended period of time. Using a Time-Varying Parameter Vector Autoregression (TVP-VAR) model of the realized volatility (RV), this work constructs the Total Connectedness Index (TCI) and Pairwise Connectedness Index (PCI) to measure the intensity and direction of realized volatility transmission within this digital asset network. Our findings reveal a consistently high level of spillovers among these leading cryptocurrencies, with notable peaks during periods of global market turbulence. Notably, Ethereum emerges as the most influential volatility transmitter, challenging the traditional view of Bitcoin as a primary driver of volatility spillovers. This reflects Ethereum’s pivotal role in decentralized finance (DeFi), decentralized applications (dApps), and its growing trading activity, suggesting a shifting influence in the increasingly diversified cryptocurrency ecosystem.
The paper investigates how attention to the Russia-Ukraine war affects cryptocurrency returns by creating a Google search volume index (GSVI) using Google trends keywords. It finds that crypto returns react positively to attention to war and negatively to the volatility index (VIX), demonstrating that investor fear during times of crisis may increase interest in cryptocurrencies. The research provides specific insights into crypto markets that can aid portfolio managers and regulators. It also adds to the limited studies on the impact of war on cryptocurrency returns.
Ahmed Bouteska, Taimur Sharif, Layal Isskandarani, Mohammad Zoynul Abedin
This research investigates how market-wide conditions (macro aspects) and individual cryptocurrency-specific characteristics (micro aspects) influence the efficiency of cryptocurrency markets. Macro aspects encompass the impacts of overall market liquidity, volatility, and global uncertainty events (e.g., the COVID-19 pandemic and geopolitical conflicts) on market efficiency. Micro aspects focus on cryptocurrency-specific attributes, such as liquidity and volatility levels, and their effects on price delays. Our findings reveal that rising liquidity and declining volatility enhance market efficiency at both macro and micro levels. Furthermore, we observe that during the periods of uncertainty, inefficiencies are exacerbated among less liquid and more volatile cryptocurrencies. We propose that the perceived uncertainties and substantial transaction costs associated with cryptocurrencies that lack liquidity and exhibit high volatility act as deterrents, diminishing the eagerness of active traders to participate in arbitrage trading. Consequently, this leads to inefficiencies in the market. The results of this study offer valuable insights for financial market regulators and authorities as well as investors associated with the crypto market, particularly during the times of financial turmoils.
As a theoretical foundation and overview, the paper explains how blockchain technology influences energy trade and finance through decentralized, safe, and transparent peer-to-peer transactions. It examines the current energy crisis that arises with a steep, rising curve of rather unorthodox consumption of energy and calls for cleaner, more reliable sources of energy. It also discusses how blockchain-based platforms could help eliminate persistent challenges in centralized energy systems. By combining the previous literature on distributed ledgers, smart contracts, and decentralized market mechanisms, we find that blockchain provides faster settlements, lower overheads, and enhanced resilience against single points of failure. This study will review how blockchain-enabled energy finance solutions speed transactions, build trust, and allow for innovative funding approaches, such as green bonds and energy banking. All in all, the findings support blockchain as a viable way of achieving a more flexible, customer-oriented, and environmentally sustainable energy sector while showcasing the technological, regulatory, and operational gaps that research and responsible policy actions must address. • Examines Blockchain's decentralized role in energy trade and finance. • Explores Blockchain's advantages and challenges in energy finance integration. • Reviews Blockchain models for platform, tech, privacy, and security solutions. • Highlights Blockchain's potential to enable trust and direct energy transactions. • Discusses future needs for advanced algorithms and supportive regulations.
This study is the first to scientifically investigate stock indices and currency exchanges that affect crypto price volatility pre and post the FTX (Future Exchanges) collapse event. Weekly series from 1 January 2020 to 31 December 2024 were utilized for the analysis. The ARDL model suggests positive symmetric short- and long-term effects of USA stock indices on Bitcoin and Ethereum prices (p < 0.10), while Japanese stock indices and currency exchanges have negative symmetric short- and long-term effects on Bitcoin and Ethereum price volatility (p < 0.10). The global index MSCI has no symmetric effect. The asymmetric approach NARDL suggests positive and negative asymmetric short- and long-term effects of USA and Japanese stock indices and currency exchanges on Bitcoin and Ethereum price volatility (p < 0.05). This research helps exchange brokers and crypto traders diversify their holdings, reduce stock index and currency exchange risk, and accurately predict Bitcoin and Ethereum price variations.
This paper investigates the diversification, hedging, and safe-haven capabilities of Bitcoin and gold against blue economy and green finance assets using three different MGARCH models (DCC, ADCC, and GO-GARCH) during adverse events such as the COVID-19 health crisis and the 2022 Russia-Ukraine conflict. Blue economy assets, which refer to sectors that sustainably utilize ocean resources, are a key focus alongside green finance assets. The findings reveal that during crises, Bitcoin demonstrates robust safe-haven characteristics, particularly against blue economy assets like BJLE and OCEN. Conversely, gold exhibits pronounced safe-haven properties against specific blue economy and green finance assets such as BJLE and FAN. The GO-GARCH model highlights gold's strong diversification and safe-haven roles, especially against BJLE. Bitcoin, on the other hand, is more effective as a diversifier for PIO. Moreover, the GO-GARCH model consistently outperforms the DCC and ADCC models in terms of hedging effectiveness, showing that gold is the preferred hedging instrument for GNR and TAN, while Bitcoin is more effective for other blue and green assets. The results underscore the distinct roles of Bitcoin and gold in portfolio management strategies, offering insights for investors navigating market uncertainties in the context of sustainable investments.
Hanan Haider Ali, Sumathi Kumaraswamy, Sara Al Balooshi, Yomna Abdulla
This study examines the news impact, persistence and asymmetric effects of stock, oil and cryptocurrency markets in Gulf Cooperation Council (GCC) countries. The diagonal BEKK method is applied to the daily trading prices of three major cryptocurrencies, crude oil and four stock market indices from January 2018 to February 2024. The empirical results indicate a strong, significant volatility spillover between cryptocurrencies, oil and stock prices, but no return spillover effect among these asset classes. A negative news shock in cryptocurrency markets generates more volatility in GCC stock prices than positive news. The study suggests that cryptocurrency price movements are independent of other asset classes, providing portfolio diversification opportunities for investors in GCC countries.
This research employs Multifractal Detrended Fluctuation Analysis (MFDFA) to investigate multifractal properties in financial variables, including Bitcoin prices and economic indicators. Spanning 2019–2022, the analysis reveals multifractal scaling not only in Bitcoin prices, but also in economic indicators such as inflation rates and energy commodity prices. The non-linear singularity spectra unveil the multifaceted nature of scaling properties. Temporal analysis exposes intriguing trends in multifractality with implications for market efficiency. Furthermore, correlation analysis unveils connections among multifractal properties. For instance, a positive correlation between oil prices and Bitcoin suggests similar market forces. The log-log plot of fluctuation function Fq versus lag size demonstrates a power-law relationship, characteristic of multifractal systems. The empirical data’s alignment in log-log space suggests self-similarity in the Bitcoin time series, supporting multifractality. The calculated Hurst exponents values suggest varying degrees of multifractality across the years, with 2021 exhibiting the highest degree and 2022 the lowest. Furthermore, an asymmetry index (0.5767) deviating from 0.5 indicates that the multifractal nature of the Bitcoin market is not symmetric. This research enhances risk assessment and portfolio optimization in finance. It challenges the Efficient Market Hypothesis (EMH), emphasizing the significance of MFDFA in comprehending financial market and economic factor’s relationships.
Abstract This paper examines the dynamic interplay between the global geopolitical risk and eleven decentralized finance (DeFi) digital currencies during the inflationary burden caused by the Russia-Ukraine war episodes. Daily data spanning from 13 October 2021 to 29 October 2024 and the innovative Quantile-Vector Autoregressive (Q-VAR) methodology are employed for estimating the pairwise, joint and network linkages at the lower, middle and upper quantiles. High levels of geopolitical risk are more connected with bull markets of the DeFi assets and new war episodes strengthen this relation. Geopolitical tensions combined with high inflation lead to the GPR becoming major determinant of DeFi markets so contributing to the transition to the digital decentralized cashless financial system. Maker is the leading DeFi asset in this transition and constitutes a promising successor of fiat currencies that suffer from devaluation generated by conflicts.
2017'den sonra kripto para birimlerinde yaşanan fiyat dalgalanmaları, getiri fırsatları ve volatilite, yatırımcıların ilgisini çekerken; büyüyen işlem hacmi ve piyasa değeri, bu varlıkları geleneksel yatırımlara ek olarak yüksek kazanç ve portföy çeşitlendirme olanağı sunan bir seçenek haline getirmiştir. Buradan hareketle çalışmada piyasa değeri en yüksek üç kripto para biriminin (Bitcoin, Ethereum ve Tether USDt) 2017-2024 dönemi için volatilite düzeyleri asimetrik volatilite ölçüm modellerinden EGARCH modeli ile karşılıklı olarak incelenmektedir. EGARCH modellerine göre, Bitcoin ve Ethereum'da kötü haberler, getiri volatilitesini iyi haberlerden daha fazla etkilerken, kaldıraç etkisi gözlemlenmiştir. Buna karşılık, Tether USDt'de iyi haberlerin volatilite üzerindeki etkisi daha güçlü olup, anti-kaldıraç etkisi söz konusudur. Piyasadaki şokların, kripto paraların getiri volatilitesi üzerinde daha kalıcı bir etkiye sahip olduğu ve en çok Ethereum'un getiri oynaklığını etkilediği görülmektedir. Yarı ömür volatilite ölçüsü sonuçları, Bitcoin, Ethereum ve Tether USDt için sırasıyla 7 gün, 8 gün ve 74 gün olduğunu ortaya koymuştur. Bu durum, Bitcoin ve Ethereum’da yaşanan volatilitenin benzer sürede etkisinin kaybolduğunu, ama Tether USDt’de ise daha uzun sürdüğünü göstermektedir. Bunun sebebi Tether USdt kripto paranın stabil coin olmasıdır. Bu bağlamda, yatırımcılar ve portföy yöneticilerinin, kararlarını şekillendirirken kripto paraların asimetrik özellikleri ile oynaklık seviyelerini göz önünde bulundurmaları oldukça önemlidir.
The significant overflow of the cup, symbolizing the total capacity of the world’s real assets, with a superior flow of world financial assets leads to the ongoing spread of excess financial assets, inflating another financial bubble since 2008, in various directions. The ongoing unrestrained emission of money by the «golden antelope» represented by the Federal Reserve System leads to numerous market transformations that distort the picture of the equilibrium market, turning the world economy into a kingdom of crooked mirrors. Many countries could not decide for a long time on recognizing the legitimacy of cryptocurrency transactions at the state level. However, under the pressure of increasing volumes of financial flows generated at the instigation of the states themselves, more and more countries began to officially recognize cryptocurrency transactions. In 2024, Russia joined this list, which makes it relevant to analyze the likely impact of the global cryptocurrency market on the development of the national economy. The purpose of the presented studies is to analyze the expected impact of the development processes of global cryptocurrency markets on the domestic market. The scientific novelty of the obtained results lies in the analysis of the current state of affairs on cryptocurrency exchanges and their comparison with traditional exchanges, the speculative nature of crypto transactions, trading volumes that determine the size of cryptocurrency exchanges, indicators of manipulation in the cryptocurrency market, key results of trading on cryptocurrency exchanges, etc. The practical significance of the obtained results lies in the development of proposals to reduce the risks associated with cryptocurrency transactions that affect both the financial system of Russia and the economy of the country as a whole.
The aim of this research is to investigate the long-term relationships among the dollar exchange rate (TRY/USD), gold (GAU/USD), the Borsa Istanbul 100 Index (BIST 100) and the prices of Bitcoin (BTC/USD), Ethereum (ETH/USD), and Binance Coin (BNB/USD). Since the series contain structural breaks, Fourier unit root tests were used to model the structural breaks. As the method of this study, the relationships between variables in the long term were examined by using Fourier Shin (FSHIN) and Shin (1994) (SHIN) cointegration tests. The findings of this study showed that cryptocurrencies are cointegrated among themselves under structural breaks in the long term; investment instruments are cointegrated among themselves. In addition, as a result of this study, it was determined financial instruments and cryptocurrencies do not move in along over time under structural breaks.
Jahanzaib Alvi, Kehkashan Nizam, S. M. A. Jafri, Muhammad Rehan · 5 authors
This paper investigates the efficacy of deep learning models such as Long-Short Term Memory (LSTM), Convolutional Neural Networks (CNN), and Gated Recurrent Units (GRU) for cryptocurrency price prediction, examining their short-term and long-term forecasting accuracy for investor guidance and advancing AI in financial analysis. The study uses time series analysis with LSTM, CNN, and GRU models on daily cryptocurrency prices from Investing.com, preprocessing data before testing on Bitcoin, Ethereum Classic, Ethereum, Litecoin, Monero, and the other 37 cryptocurrencies. RMSE, MAE, and accuracy rates measure performance. Findings revealed that only six cryptocurrencies were selected for final analysis, including Bitcoin, Ethereum Classic, Ethereum, Litecoin, and Monero. Results indicate that the deep learning models, particularly the LSTM and GRU, can predict cryptocurrency prices with high accuracy, especially for short-term forecasts within a 7-day window. The CNN model demonstrates significant predictive power, suggesting its utility for immediate trading decisions. Across the models, short-term precision was remarkably high, while long-term predictions maintained a moderate level of accuracy. This study presents a comparative analysis of LSTM, GRU, and CNN models for forecasting cryptocurrency prices, emphasizing LSTM and GRU's ability to navigate price volatility and suggesting their use for real-time trading analysis. The study's historical data reliance curtails forecasting unforeseen market shifts. Future studies should include new variables like social sentiment and blockchain analytics and test real-time adaptive models to enhance predictive strength. Model validation in actual market conditions is recommended for practical application.
We develop a flexible supply and demand equilibrium framework that can be used to develop pricing models to forecast Bitcoin’s price trajectory based on its fixed, inelastic supply and evolving demand dynamics. This approach integrates Bitcoin’s unique monetary attributes with demand drivers such as institutional adoption and long-term holding patterns. Using the April 2024 halving as a baseline, we explore model scenarios with varying assumptions about growth in adoption and supply-side constraints, calibrated to real-world data. Our findings indicate that institutional and sovereign accumulation can significantly influence price trajectories, with increasing demand intensifying the impact of Bitcoin’s constrained liquidity. Forecasts suggest that modest withdrawals from liquid supply to strategic reserves could lead to substantial price appreciation over the medium term, while higher withdrawal levels may induce volatility due to supply scarcity. These results highlight Bitcoin’s potential as a long-term investment and underline the importance of integrating economic fundamentals into forward-looking portfolio strategies. Our framework provides flexibility for testing different market scenarios, demand curve functional forms, and parameterizations, offering a tool for investors and policymakers considering Bitcoin’s role as a strategic asset. By advancing a fundamentals-based approach, this study contributes to the broader understanding of how Bitcoin’s supply–demand dynamics influence market behavior.