The collapse of FTX in November 2022 sent shockwaves across the cryptocurrency market, triggering widespread panic, massive sell-offs, and a sharp decline in digital asset valuations. This event not only exposed vulnerabilities in centralized crypto exchanges but also raised critical questions about systemic risk, investor confidence, and the long-term resilience of blockchain-based financial systems. Using advanced econometric modeling, this study evaluates the causal impact of the FTX insolvency on major cryptocurrencies through a counterfactual prediction analysis, offering empirical insights into how the market would have performed in the absence of the collapse.
Understanding the FTX Collapse
FTX was one of the world’s most prominent cryptocurrency derivatives exchanges, founded in 2019 by Sam Bankman-Fried. It quickly gained traction due to its innovative trading products, including futures, options, leveraged tokens, and its native token, FTT. However, in early November 2022, concerns emerged about the financial health of FTX and its sister trading firm, Alameda Research. A key red flag was the revelation that a significant portion of Alameda’s balance sheet consisted of FTT tokens—essentially self-issued assets with questionable liquidity and value.
The crisis escalated when Binance CEO Changpeng Zhao announced plans to liquidate Binance’s holdings of FTT, triggering a wave of market panic. Within days, FTX faced a liquidity crunch as users rushed to withdraw funds. Despite an initial agreement for Binance to acquire FTX, the deal was swiftly called off. On November 11, 2022, FTX filed for bankruptcy, marking one of the most dramatic failures in crypto history.
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The fallout was immediate: Bitcoin (BTC) dropped nearly 16% in November alone, Ethereum (ETH) plunged under heavy selling pressure, and Solana (SOL) saw its price collapse by over 40%. In just three days, the top 15 cryptocurrencies collectively lost $152 billion in market capitalization. The event also revealed deeper issues—such as poor governance, lack of transparency, and asset-liability mismatches—that continue to shape regulatory discussions today.
Methodology: Bayesian Structural Time Series Model
To assess the true causal effect of the FTX collapse, this study employs a Bayesian structural time series (BSTS) model, a robust framework for counterfactual analysis. Unlike traditional difference-in-differences or event studies, BSTS accounts for temporal dynamics, serial correlation, and unobserved confounders by constructing a synthetic control group based on pre-intervention trends.
The model uses two core equations:
- Observation equation: ( y_t = Z_t^T \alpha_t + \varepsilon_t )
- State equation: ( \alpha_{t+1} = T_t \alpha_t + R_t \eta_t )
Where:
- ( y_t ) represents observed cryptocurrency prices,
- ( \alpha_t ) is the latent state vector,
- ( \varepsilon_t ) and ( \eta_t ) are observation and system errors,
- Control variables include exchange rates of major fiat currencies (EUR, GBP, JPY, CNY, INR), which correlate with crypto prices but were unaffected by the FTX event.
By training the model on data from October 3 to November 10, 2022 (pre-collapse), it generates a counterfactual forecast for the post-collapse period (November 12 to December 14). The difference between actual prices and predicted values reveals the causal impact.
Why This Approach Matters
Traditional models often assume static relationships and ignore evolving market conditions. The BSTS model overcomes these limitations by:
- Incorporating time-varying coefficients,
- Using Markov Chain Monte Carlo (MCMC) sampling for uncertainty quantification,
- Allowing integration of exogenous predictors without direct treatment influence.
This makes it ideal for analyzing high-frequency financial data like cryptocurrency prices.
Data and Control Variables
Daily price data for seven major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Binance Coin (BNB), Solana (SOL), Cardano (ADA), and Tether (USDT)—were collected from CoinDesk. The pre-treatment period spans October 3 to November 10, while the post-treatment phase runs from November 12 to December 14, with November 11 as the intervention date.
To ensure accurate counterfactual predictions, the model includes fiat currency exchange rates sourced from the Federal Reserve Economic Data (FRED) and Yahoo Finance. These serve as control variables because:
- They reflect global macroeconomic conditions,
- They are correlated with crypto prices due to investor risk appetite,
- But they were not directly impacted by FTX’s bankruptcy.
Regression results show that these controls explain approximately 75% of price variability (R² = 0.75), with low RMSE (0.03), confirming their predictive power.
Empirical Findings: Quantifying the Damage
The analysis reveals a sharp divergence between actual prices and counterfactual forecasts across all major cryptocurrencies after November 11. The point-wise causal effect measures daily deviations, while cumulative impact tracks total losses over time.
Bitcoin (BTC): Relative Decline of –16%
Despite being considered a relatively stable store of value, BTC fell sharply post-collapse:
- Average observed price: $16,810
- Counterfactual prediction: $20,010
- Causal impact: –$3,190 (95% CI: –$5,120 to –$1,910)
- Relative decline: –16%
This aligns with prior research showing that economic uncertainty negatively affects Bitcoin prices.
Ethereum (ETH): Hardest Hit Among Large Caps
ETH experienced more severe damage:
- Average observed price: $1,240
- Predicted price without collapse: $1,630
- Causal loss: –$390 (95% CI: –$590 to –$120)
- Relative drop: –24%
Intense selling pressure and fears over staked ETH withdrawals amplified losses.
Solana (SOL): Most Vulnerable
SOL suffered the largest relative decline:
- Observed average: $13.81
- Expected average: $23.48
- Total value loss: $232.24 million
- Relative decline: –41%
Given its close ties to FTX’s ecosystem and leadership, SOL was particularly exposed.
Other Cryptocurrencies
| Asset | Relative Decline | Key Insight |
|---|---|---|
| XRP | –20% | Struggled to maintain $0.40 support |
| BNB | –9% | Resilient due to Binance's strong reserves |
| ADA | –21% | Moderate spillover risk |
| USDT | Stable | Minimal deviation; maintained peg |
These findings confirm that the FTX collapse had a statistically significant negative impact on cryptocurrency markets. Without the event, prices would have remained close to predicted levels.
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Frequently Asked Questions (FAQ)
What is counterfactual prediction in finance?
Counterfactual prediction estimates what would have happened in the absence of a specific event—like the FTX collapse—by using historical data and control variables to simulate an alternative outcome. It helps isolate causal effects rather than just observing correlations.
Why did Solana drop more than Bitcoin?
Solana’s deeper decline stems from its strong association with FTX’s founders and ecosystem. Many investors viewed SOL as directly exposed to FTX’s solvency risks, leading to disproportionate sell-offs compared to more decentralized assets like Bitcoin.
Did Tether (USDT) lose its peg during the crisis?
No. Despite market turmoil, Tether maintained its $1.00 peg throughout the period. Its stability underscores the role of stablecoins in preserving liquidity during crises—even when confidence in exchanges wanes.
How reliable is the Bayesian structural model?
The BSTS model is widely recognized for its ability to handle noisy financial data, incorporate uncertainty, and avoid overfitting. Studies have successfully applied it to assess impacts of pandemics, wars, and financial crashes—making it well-suited for crypto market analysis.
Could this happen again with another exchange?
Yes—if proper safeguards aren't implemented. The FTX collapse highlighted dangers of centralized custody, opaque accounting, and excessive leverage. Future risks can be mitigated through transparent proof-of-reserves audits, decentralized protocols, and stronger regulatory oversight.
What lessons should investors take away?
Investors should prioritize platforms with transparent operations, diversify exposure across ecosystems, and understand that even large exchanges carry counterparty risk. Additionally, monitoring on-chain metrics and funding rates can provide early warning signs of distress.
Policy Implications and Future Outlook
The FTX collapse underscores urgent needs in the crypto industry:
- Regulatory Clarity: Regulators must act swiftly to establish clear rules around capital requirements, auditing standards, and customer fund segregation.
- Technological Safeguards: Blockchain solutions like zero-knowledge proofs and on-chain audits can enforce asset-liability balance and prevent self-dealing.
- Decentralization vs. Centralization: While DeFi protocols offer transparency, many users still rely on centralized exchanges. Bridging this gap requires hybrid models that combine ease-of-use with verifiable trustlessness.
- Consumer Protection: Investor protection should be paramount—without stifling innovation through overly burdensome regulations.
As institutional players increasingly enter the space, ensuring market integrity becomes critical. The same mistakes must not be repeated.
Conclusion
This study provides compelling evidence that the FTX collapse significantly harmed cryptocurrency markets, with Solana and Ethereum bearing the brunt of losses. Through rigorous counterfactual analysis using a Bayesian structural model, we demonstrate that digital assets would not have suffered such steep declines absent the bankruptcy event.
Core keywords integrated throughout include: FTX collapse, cryptocurrency market impact, causal estimation, counterfactual prediction, Bayesian structural model, Solana price drop, Ethereum volatility, and Bitcoin decline. These reflect both search intent and thematic depth.
While quantitative models cannot capture sentiment or qualitative factors fully, they offer valuable frameworks for understanding systemic shocks. Moving forward, fostering transparency, adopting decentralized safeguards, and enhancing regulatory coordination will be essential to building a more resilient crypto economy.
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