INSTITUTIONALIZATION OF CRYPTOCURRENCY MARKETS: HOW REGULATORY ANNOUNCEMENTS, FORKS, AND FUTURES TRADING SHAPE BITCOIN'S PRICE BEHAVIOUR

281 International Journal of Social and Educational Innovation Vol. 12, Issue 24, 2025 ISSN (print): 2392 – 6252 eISSN (online): 2393 – 0373 DOI: 10.5281/zenodo.17631879 INSTITUTIONALIZATION OF CRYPTOCURRENCY MARKETS: HOW REGULATORY ANNOUNCEMENTS, FORKS, AND FUTURES TRADING SHAPE BITCOIN’S PRICE BEHAVIOUR Kamaldeen Ibraheem NAGERI Department of Accounting Science Walter Sisulu University, Mthatha, South Africa [email protected] Abstract This study investigates the effects of legal regulation, rule changes, and regulated trading instruments on Bitcoin’s price dynamics and volatility over the period 2013–2022. Using daily time-series data from major cryptocurrency exchanges and financial databases, the study employs a Vector Error Correction Model (VECM) models to capture both shortand long-run relationships under different market regimes. Empirical results reveal that regulatory announcements in the United States and Japan significantly influence Bitcoin’s price and volatility, indicating that legal recognition and enforcement mechanisms enhance investor confidence. In contrast, rule-change events such as hard forks and halving demonstrate asymmetric effects, initially increasing volatility before stabilizing over time. The introduction of Bitcoin futures and options strengthens market maturity and institutional participation but also introduces speculative dynamics, especially during high-volatility periods. The findings underscore the dual nature of regulation: while it promotes market legitimacy and long-term stability, it can simultaneously induce short-term uncertainty and price corrections. Policy implications highlight the necessity of a coordinated global regulatory framework that balances innovation with investor protection, while future research should explore algorithmic trading behaviors, DeFi spillovers, and cross-asset contagion effects in cryptocurrency markets. Keywords: Bitcoin, Regulation, Market Volatility, Hard Fork, Futures Options, Cryptocurrency. International Journal of Social and Educational Innovation (IJSEIro) Volume 12/ Issue 24/ 2025 282 1. Introduction The inception of Bitcoin stemmed from the visionary white-paper by Satoshi Nakamoto, which proposed a peer-to-peer electronic cash system operating without a central authority (Nakamoto, 2008). Bitcoin’s architecture sits at the intersection of cryptography, distributed ledger technology and economic innovation. The currency’s emergence has drawn attention from multiple domains, economics, law, finance, computer science, because it challenges traditional payment, settlement and regulatory infrastructures. Early institutional interest and regulatory reaction by, for instance, the European Central Bank (ECB) and national tax authorities signalled that Bitcoin’s significance extends beyond niche adoption to mainstream financial systems (European Central Bank, 2012; Velde, 2013; HM Revenue & Customs, 2014; Segendorf, 2014). Given Bitcoin’s foundational ambition as a decentralised medium of value and exchange, it necessarily raises questions about governance, oversight and market structure. While technological research has focused on its blockchain, cryptographic security and protocol vulnerabilities (Ober, Katzenbeisser & Hamacher, 2013; Eyal & Sirer, 2014), the legalregulatory dimension has grown steadily in importance. The decentralized nature of Bitcoin has prompted concerns including tax evasion, money laundering and financing illicit activities (Barratt, Ferris & Winstock, 2014; Ron & Shamir, 2014; Van Hout & Bingham, 2013). As such, the interplay between regulation (or lack thereof), rule-changes and the exogenous institutionalisation of Bitcoin into financial systems becomes a critical area of study. In parallel, economists and finance scholars have increasingly investigated Bitcoin’s economic characteristics, its potential as an investment asset (Brito, Shadab & Castillo, 2014; Yermack, 2013), its use as a medium of exchange (Segendorf, 2014; Wang, 2014) and, perhaps most pertinent to this paper, its price-formation mechanisms. Empirical studies have found that Bitcoin’s price depends on an array of factors including trading volume, oil and stock indices, exchange activities and investor sentiment (Kristoufek, 2015; Wang, Xue & Liu, 2016; Koutmos, 2020; Gbadebo et al., 2021). What remains somewhat under-explored is how nonmarket forces, such as legal regulation, rule-changes and formal entry of regulated trading instruments, shape the behaviour of the Bitcoin market. This research will therefore concentrate on the nexus of legal-regulation, rule-change and regulated trading and their effect on the Bitcoin market. Recent papers highlight that regulatory clarity tends to contribute to greater adoption of crypto-assets (van der Linden & Shirazi, 2023) International Journal of Social and Educational Innovation (IJSEIro) Volume 12/ Issue 24/ 2025 283 and that bans or tight restrictions lead to measurable spill-over effects in adjacent fintech sectors (Chen, 2025) or to heightened volatility and volume in the crypto market (Menchetti, Cipollini & Mealli, 2021). Thus, this topic is timely and relevant. Instances of shifts in regulatory stance, the introduction of regulated futures or exchange-traded instruments and the evolving supervisory environment constitute structural events that can materially affect market dynamics. The contribution of this paper lies in its systematic examination of how major regulatory events, whether legalising frameworks, bans, rule-changes or regulated trading entry, embed into the priceand volume-formation process of Bitcoin. By analysing both shortand longterm behaviours, and conducting cointegration and event-study analyses where possible, the paper will assess how regulation and institutional structure alter the interplay of supply, demand, trading activity and investor perception in the Bitcoin market. Ultimately, recognising that Bitcoin is not just a technological artefact but also a regulated financial innovation will help scholars, practitioners and policymakers understand its evolving role in the global financial system. 2. Literature Review The literature on Bitcoin (BTC) is increasingly attentive to structural events, such as regulatory changes, rule-alterations (e.g., forks), and institutionalised trading (such as futures/ETFs), and their impact on price formation and volatility. These events differ from purely market-driven dynamics (like trading volume or macroeconomic shocks) and therefore warrant discrete investigation. Several strands of research now intersect: regulatory/regime-change effects, fork/rule-change effects, and institutional trading-entry effects. Regulatory and rule-change events. One significant strand sees regulatory or legal events (for example, bans, approvals, clarifications) as triggers of price responses. For example, one study documents that the implementation of crypto-asset regulation across 28 countries correlated with reduced local price deviations relative to USD price, particularly when regulation enhanced transparency or permitted banking/payment operations (Dufouleur, 2024). Another study finds that regulation during the COVID-19 era had measurable effects on crypto-market volatility (Zhang et al., 2023). These works highlight that the mere announcement or implementation of regulation constitutes more than noise, they are legitimate structural breaks for the BTC market. As Dufouleur (2024) shows: "implementing regulations has no significant International Journal of Social and Educational Innovation (IJSEIro) Volume 12/ Issue 24/ 2025 284 influence in the short-term; in contrast, markets with partial bans exhibited larger price divergence." Forks and protocol rule-changes. A second stream examines how protocol changes, such as forks in the Bitcoin blockchain, impact Bitcoin’s volatility and correlation structure with its “offspring” coins. For instance, Walter Bazán-Palomino (2021) finds that the volatility contribution of Bitcoin-fork events to overall market volatility is strongest in the first two months post-fork, and the time-varying correlations between BTC and its forks shift from negative/low during high volatility episodes to strongly positive in low volatility periods. He concludes that Bitcoin forks do not serve reliably as hedges against Bitcoin risk. These findings are important because they treat rule-changes not as mere technical curiosities but as events with tangible market impacts (volatility spillovers, correlation regime shifts). Institutional trading and entry of derivatives. A third set of literature highlights how the entry of regulated trading venues (futures, ETFs) or the increasing institutionalisation of Bitcoin trading changes its market dynamics. For example, regulatory approval of Bitcoin futures or ETFs (such as the Chicago Board Options Exchange’s December 2017 futures) is widely discussed in practitioner and academic sources as a pivot point for price dynamics. Events like the major institutional entry via a regulated instrument can shift Bitcoin’s perceived risk profile, liquidity and investor base, thus affecting its price. Practitioners note that such events change the nature of Bitcoin from a fringe, retail-dominated market to one where institutional capital and regulatory oversight matter (Fintech Magazine, 2022). Synthesis and implications for event classification. Taken together, the above bodies of work suggest that structural events in the Bitcoin market (regulation implementation, rulechange/forks, institutional trading entry) are not mere background noise but can act as secondorder drivers of price formation—distinct from the usual supply/demand or macro-economic factors. Accordingly, studies of Bitcoin price dynamics increasingly adopt event-study frameworks or time-varying correlation/volatility models to capture these effects. For example, Bazán-Palomino’s work uses multivariate GARCH models to isolate the immediate (twomonth) and medium-term (beyond) effects of forks on volatility (Bazán-Palomino, 2021). While ample research has explored fundamental drivers of Bitcoin price (mining costs, transaction volume, macro factors), the literature on episodic events, those discrete natures such as regulatory shocks or rule-changes, is comparatively less large but growing. For instance, Dehouche (2021) investigates volatility at daily/weekly/monthly frequencies and finds that Bitcoin’s extreme volatility is especially pronounced at lower aggregation International Journal of Social and Educational Innovation (IJSEIro) Volume 12/ Issue 24/ 2025 285 frequencies, suggesting that many of its price swings are short-term structural shocks rather than slow-moving fundamentals. Along similar lines, Takaishi (2021) demonstrates that the asymmetric volatility and multifractal behaviour of Bitcoin evolve over time, implying that structural events may shift the underlying market regime. Given this backdrop, episodic events (regulations, forks, institutional entry) may act as regimeshift points, changing the statistical properties of returns, altering volatility clustering, or shifting correlation with other assets. For example, an approval of a regulated ETF may increase institutional participation, thereby reducing retail-driven noise, increasing liquidity and thereby reducing extreme swings (or at least changing their nature). Conversely, a regulatory ban may induce panic selling, reduce liquidity, and thereby increase volatility and decouple Bitcoin from conventional assets. The literature confirms both patterns: regulation can reduce price deviation in the long run (Dufouleur 2024) but partial bans can exacerbate divergence. Therefore, for research that seeks to examine how legal-regulation, rule-change and regulated trading affect the Bitcoin market, it is crucial to view these events not only as isolated points but as structural breaks in the price-formation mechanisms of Bitcoin. That means incorporating them explicitly into econometric models (event-study windows, cointegration breaks, regime-switching models) rather than treating them as control variables. Ultimately, the emerging literature suggests that ignoring these event-types risks mis-specification of models and misinterpretation of Bitcoin’s volatility and price behaviour. 3. Methodology This study employs a quantitative econometric approach using secondary time-series data of daily Bitcoin prices, trading volume, and key market events from January 2013 to December 2024. Data on Bitcoin prices and trading volumes were sourced from CoinMarketCap and Blockchain.com, while information regarding major regulatory announcements, legal frameworks, and market rule changes-such as halving events, network forks, and the introduction of futures trading-were compiled from institutional databases including the U.S. Securities and Exchange Commission (SEC), Japan’s Financial Services Agency (JFSA), and reports from the Bank for International Settlements (BIS) and International Monetary Fund (IMF). Macroeconomic and financial variables such as the U.S. Dollar Index (DXY) and Global Volatility Index (VIX) were extracted from the Federal Reserve Economic Data (FRED) and International Journal of Social and Educational Innovation (IJSEIro) Volume 12/ Issue 24/ 2025 286 Bloomberg Terminal, respectively, to control for the influence of global market uncertainty and liquidity conditions. The study period was selected to capture both early market inefficiency (2013-2016) and later institutionalization phases (2017-2024), reflecting distinct regulatory and technological epochs (Corbet et al., 2023; Urquhart & Zhang, 2022). The dependent variables are the logarithm of Bitcoin price (LNPRCE) and logarithm of Bitcoin volatility (LNVOLA). Independent variables include regulatory measures (USREG, VATEX, JPLEG), technological and market structural events (HALVE, FORKS, FUTOPT), and trading activity (LNVOLM). All variables were transformed into natural logarithms to ensure variance stability and interpret coefficients as elasticities (Kristoufek, 2021). Table 1. Variable Definition Variable Definition Data Source Expected Sign LNPRCE Natural log of Bitcoin daily price (USD) CoinMarketCap Dependent LNVOLA Natural log of Bitcoin realized volatility Blockchain.com Dependent LNVOLM Natural log of daily Bitcoin trading volume CoinMarketCap + USREG Dummy for U.S. regulatory events (1 = regulation enacted) SEC Reports ± VATEX Dummy for VAT/taxation rulings on Bitcoin IMF, HMRC ± JPLEG Dummy for Japanese legal recognition of Bitcoin JFSA Reports + HALVE Dummy for Bitcoin halving events Bitcoin.org + FORKS Dummy for hard fork events (e.g., Bitcoin Cash) Blockchain.com ± FUTOPT Dummy for introduction of Bitcoin futures/options CME Group + C Constant - The study employs a Vector Error Correction Model (VECM) framework to assess both the shortand long-term dynamics between Bitcoin prices, volatility, trading volume, and exogenous event variables. The model allows for cointegration among non-stationary time series, capturing equilibrium adjustment following market shocks (Engle & Granger, 1987; Johansen, 1991). The long-run equation of Bitcoin price determination is specified as: 𝐿𝑁𝑃𝑅𝐶𝐸𝑡=𝛼0+𝛼1𝐿𝑁𝑉𝑂𝐿𝑀𝑡−1 +𝛼2𝑈𝑆𝑅𝐸𝐺𝑡−1 +𝛼3𝑉𝐴𝑇𝐸𝑋𝑡−1 +𝛼4𝐽𝑃𝐿𝐸𝐺𝑡−1 +𝛼5𝐻𝐴𝐿𝑉𝐸𝑡−1 +𝛼6𝐹𝑂𝑅𝐾𝑆𝑡−1 +𝛼7𝐹𝑈𝑇𝑂𝑃𝑇𝑡−1 +𝜖𝑡 International Journal of Social and Educational Innovation (IJSEIro) Volume 12/ Issue 24/ 2025 287 Similarly, the volatility model is defined as: 𝐿𝑁𝑉𝑂𝐿𝐴𝑡= 𝛽0+𝛽1𝐿𝑁𝑉𝑂𝐿𝑀𝑡−1 +𝛽2𝑈𝑆𝑅𝐸𝐺𝑡−1 +𝛽3𝑉𝐴𝑇𝐸𝑋𝑡−1 +𝛽4𝐽𝑃𝐿𝐸𝐺𝑡−1 +𝛽5𝐻𝐴𝐿𝑉𝐸𝑡−1 +𝛽6𝐹𝑂𝑅𝐾𝑆𝑡−1 +𝛽7𝐹𝑈𝑇𝑂𝑃𝑇𝑡−1 +𝜇𝑡 To account for the dynamic adjustment towards equilibrium, the short-run relationship in the VECM is given as: Δ𝐿𝑁𝑃𝑅𝐶𝐸𝑡=𝜆1(𝐸𝐶𝑀𝑡−1)+∑𝛾𝑖 𝑝−1 𝑖=1 Δ𝐿𝑁𝑃𝑅𝐶𝐸𝑡−𝑖 +∑𝛿𝑗 𝑝−1 𝑗=1 Δ𝐿𝑁𝑉𝑂𝐿𝑀𝑡−𝑗 +∑𝜃𝑘 𝑘Δ𝑋𝑘,𝑡−𝑗 +𝜂𝑡 where 𝐸𝐶𝑀𝑡−1 is the error correction term derived from the long-run cointegration relationship, 𝜆1 represents the speed of adjustment, and 𝑋𝑘 represents the set of exogenous policy and event variables. A sensitivity analysis was conducted using a Markov Regime-Switching Model (MSM) to test for asymmetric responses of Bitcoin prices to market shocks and policy events, recognizing that market behavior may shift between “low-volatility” and “high-volatility” regimes (Koutmos, 2020). The model is expressed as: 𝐿𝑁𝑃𝑅𝐶𝐸𝑡=𝛿0,𝑆𝑡+𝛿1,𝑆𝑡𝐿𝑁𝑉𝑂𝐿𝑀𝑡−1 +𝛿2,𝑆𝑡𝑍𝑡−1 +𝜈𝑡 where 𝑆𝑡∈ {1,2} denotes the unobserved market regime, and 𝑍𝑡−1 represents the set of regulatory and structural variables. Prior to estimation, all series were subjected to Augmented Dickey-Fuller (ADF) and PhillipsPerron (PP) tests to verify stationarity. The Johansen cointegration test was employed to confirm the existence of a long-run equilibrium among the non-stationary series. Given that both Bitcoin prices and volumes exhibit unit roots but are cointegrated, the VECM approach is appropriate, allowing for both short-term disequilibrium corrections and long-run relationships (Johansen, 1991; Kristoufek, 2021). The estimation was performed using maximum likelihood (ML) methods under the assumption of normally distributed residuals. Diagnostic tests such as the Breusch-Godfrey serial correlation LM test, Jarque-Bera normality test, and White heteroskedasticity test were applied to ensure model adequacy. Robustness was further checked through sensitivity tests employing the Markov regime-switching model and a Dynamic Conditional Correlation GARCH (DCCGARCH) model to assess volatility clustering and conditional variance spillovers (Liang et al., 2020; Bouri et al., 2024). The DCC-GARCH model is expressed as: ℎ𝑡=𝜔+𝛼𝜖𝑡−1 2+𝛽ℎ𝑡−1 International Journal of Social and Educational Innovation (IJSEIro) Volume 12/ Issue 24/ 2025 288 where ℎ𝑡 denotes conditional variance, 𝜖𝑡−1 2 represents past shocks, and 𝛼+𝛽 < 1 ensures stationarity. This multi-model approach allows for a comprehensive evaluation of how regulatory interventions, rule changes, and market innovations influence Bitcoin’s price and volatility under varying conditions of uncertainty and market maturity. The methodological rigor ensures both internal validity and robustness against structural breaks common in cryptocurrency markets (Urquhart & Zhang, 2022; Corbet et al., 2023). 4. Results 4.1 Discussion of Results The empirical results presented in Table 4.1 provide robust evidence of the dynamic influence of regulatory changes, network events, and exchange operations on Bitcoin’s price (LNPRCE) and volatility (LNVola). The regressions exhibit high explanatory power across models, with R² values ranging from 0.88 to 0.94, suggesting that the selected variables capture a substantial portion of Bitcoin’s market behaviour. Three distinct models are estimated: the Regulatory (Reg) model, the Network (Net) model, and the Exchange Operations (ExOp) model. Each highlights the structural determinants, the legal-regulatory, internal protocol, and marketinstitutional events, affecting the Bitcoin ecosystem. The coefficients associated with USREG(-1) (1.90, p < 0.01) and JPLEG(-1) (1.84, p < 0.01) indicate that regulatory developments in major economies (notably the United States and Japan) exert a strong, positive influence on Bitcoin price levels. This result aligns with the signalling theory of regulation, where clear regulatory frameworks reduce uncertainty and enhance investor confidence (Baur & Dimpfl, 2021). A positive regulatory event, such as legal recognition of Bitcoin exchanges or taxation clarity, may thus increase institutional participation and liquidity, raising the equilibrium price. The finding corroborates recent evidence by Dufouleur (2024), who demonstrated that transparent, permissive regulations reduce cross-market price divergence and increase trading efficiency. Interestingly, VATEX(-1), representing fiscal events or taxation adjustments on cryptocurrency transactions, also exhibits a positive and statistically significant coefficient (0.37, p < 0.01). This suggests that even taxation-related regulation may reinforce Bitcoin’s legitimacy, consistent with the argument that taxation frameworks signal formal acceptance and facilitate integration into traditional financial systems (Corbet et al., 2023). The results further show that LNVOLM(-1), the lagged trading volume, remains significant (0.43, p < International Journal of Social and Educational Innovation (IJSEIro) Volume 12/ Issue 24/ 2025 289 0.01), confirming that market activity levels have a reinforcing effect on subsequent price movements. This dynamic feedback between trading intensity and price is consistent with microstructure theory, which posits that higher volumes reflect information-based trading (Kristoufek, 2021). Network Events (Net Model) The second set of estimates captures network-related events, such as Bitcoin halving (HALVE) and forks (FORKS), which directly alter supply conditions and blockchain rules. Both exhibit positive and significant coefficients: HALVE (0.88, p < 0.01) and FORKS (1.06, p < 0.01), with R² ≈ 0.93. These findings are economically intuitive. The halving event, by reducing the reward to miners, constrains Bitcoin’s future supply, creating a deflationary shock that investors anticipate by bidding up the price (Bouri et al., 2024). Similarly, fork events introduce new rule-sets and derivative assets that may expand speculative trading opportunities, increasing overall market valuation in the short run (Bazán-Palomino, 2021). However, in the volatility regression, both HALVE (0.63, p < 0.01) and FORKS (1.30, p < 0.01) also have significant positive effects on LNVola. This indicates that while network events may raise prices, they concurrently heighten uncertainty. This dual impact reflects the structural break hypothesis, where regime shifts in blockchain protocol trigger periods of adjustment as market participants reassess equilibrium (Urquhart & Zhang, 2022). The volatility persistence coefficient LNVOLM(-1) (0.53, p < 0.01) confirms that volatility clustering remains strong in post-event periods, supporting the heteroskedastic nature of Bitcoin returns often documented in GARCH-based studies (Liang et al., 2020; Takaishi, 2021). The third model evaluates the role of regulated trading instruments, such as Bitcoin futures and options (FUTOPT(-1) = 1.60, p < 0.01). The strong positive coefficient on FUTOPT suggests that the introduction and expansion of regulated derivatives markets have significantly boosted Bitcoin’s price levels. This finding accords with the institutionalisation hypothesis, which posits that the availability of standardized, regulated trading platforms enhances liquidity, narrows spreads, and improves market depth (Hendrickson & Luther, 2023). Futures and options allow institutional investors to hedge or speculate within a legal framework, signalling mainstream acceptance and attracting new capital inflows. International Journal of Social and Educational Innovation (IJSEIro) Volume 12/ Issue 24/ 2025 296 leverage and procyclical amplification of risk, especially during periods of policy or technological uncertainty (Kristoufek, 2021; Corbet et al., 2024). Overall, the results reinforce the need for coordinated global policy frameworks that balance innovation with systemic stability. Regulatory harmonization across jurisdictions would prevent market fragmentation and cross-border arbitrage, ensuring that digital asset markets integrate more seamlessly into global financial stability assessments (Dufouleur, 2024). The growing interdependence between cryptocurrency markets and macroeconomic indicators further implies that central banks and financial regulators should incorporate crypto-related variables into their macroprudential surveillance systems (Barontini & Holden, 2020). Future Directions While the current study contributes to understanding the complex interactions between regulation, rule changes, and market outcomes, several avenues remain for future research. First, future studies should adopt high-frequency data to capture intraday volatility responses to policy announcements and market shocks. Such analyses could utilize event-study methodologies or structural vector autoregressive (SVAR) models to disentangle contemporaneous causal effects. Second, cross-market analyses that compare Bitcoin with other leading digital assets such as Ethereum, Solana, and stablecoins would offer deeper insights into heterogeneity in regulatory sensitivity. The increasing institutional adoption of stablecoins and tokenized assets warrants investigation into how these instruments transmit risk and liquidity between traditional and digital markets (Corbet et al., 2023; Bouri et al., 2024). Third, behavioral finance approaches could explore how investor sentiment and regulatory perceptions interact with market fundamentals. Incorporating measures such as social media sentiment indexes, Google Trends, or blockchain transaction flows may reveal psychological mechanisms underlying price formation (Feng et al., 2022). Finally, as central bank digital currencies (CBDCs) evolve, future research should analyze their coexistence and potential competition with decentralized cryptocurrencies. A comparative analysis between regulated digital currencies and decentralized ones could inform optimal policy design, addressing both innovation and stability (Barontini & Holden, 2020). In conclusion, Bitcoin’s market evolution reflects the ongoing institutionalization of decentralized finance. The convergence of regulatory maturity, technological governance, and derivative market development is transforming Bitcoin from a speculative instrument into a more structured and globally integrated asset class. However, sustaining this transformation International Journal of Social and Educational Innovation (IJSEIro) Volume 12/ Issue 24/ 2025 297 requires continued research collaboration between economists, policymakers, and technologists to ensure that the digital financial ecosystem evolves in a transparent, resilient, and equitable manner. References Barontini, C., & Holden, H. (2020). Central bank digital currencies and global monetary stability. BIS Quarterly Review, March. Baur, D. 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