Cryptocurrency#
Cryptocurrency markets exhibit distinctive properties that set them apart from traditional financial markets: extreme volatility, 24/7 trading without circuit breakers, rapid market microstructure evolution, fragmentation across exchanges, and a participant base that mixes retail, institutional, and algorithmic traders. Understanding these properties is essential for building strategies that are grounded in the actual mechanics of crypto markets rather than naive applications of equity or futures frameworks.
The research here covers foundational crypto asset pricing — how crypto risk factors are structured, what drives cross-sectional return differences, and how systemic risk propagates across the ecosystem. Topics include the empirical distribution of crypto returns, low-volatility anomalies, factor models tailored to crypto, cryptocurrency contagion dynamics, and the practical implications for institutional investors entering the space.
Related topics include DeFi & AMMs for on-chain market mechanics, Carry Trade for funding rate strategies, Momentum for crypto momentum strategies, and Order Flow for microstructure-driven approaches.
An Investor’s Guide to Crypto#
We provide practical insights for investors seeking exposure to the growing cryptocurrency space. Today, crypto is much more than just bitcoin, which historically dominated the space but accounted for just a 21% share of total crypto trading volume in 2021. We discuss a wide variety of tokens, highlighting both their functionality and their investment properties. We critically compare popular valuation methods. We contrast buy-and-hold investing with more active styles. We only deem return data from 2017 representative, but the use of intraday data boosts statistical power. Underlying crypto performance has been notoriously volatile, but volatility-targeting methods are effective at controlling risk, and trend-following strategies have performed well. Crypto assets display a low correlation with traditional risky assets in normal times, but the correlation also rises in the left tail of these risky assets. Finally, we detail important custody and regulatory considerations for institutional investors.
Low-volatility strategies for highly liquid cryptocurrencies#
This paper develops and tests low-volatility investment strategies for liquid cryptocurrencies, combining volatility targeting with stop-loss rules to control downside risk. The authors document that lower-volatility crypto assets deliver competitive risk-adjusted returns compared to higher-volatility alternatives, consistent with the low-volatility anomaly documented in equity markets.
Cryptocurrencies: Stylized Facts and Risk Based Momentum Investing#
This paper documents the distributional characteristics of cryptocurrency returns, including extreme fat tails, volatility clustering, and unique autocorrelation structures. The authors develop risk-based momentum strategies that account for these distributional properties, showing that momentum signals calibrated for crypto’s unique characteristics outperform equity-style implementations.
Cryptocurrency trading: A systematic mapping study#
A comprehensive literature review mapping the academic research on cryptocurrency trading strategies. The survey covers technical analysis approaches, machine learning methods, market microstructure studies, and quantitative strategies, providing a structured overview of the state of crypto trading research.
Optimal Allocation to Cryptocurrencies in Diversified Portfolios#
This paper uses quantitative optimization to determine the optimal portfolio allocation to cryptocurrencies for a mean-variance investor. The analysis finds that the optimal allocation to BTC and ETH in a diversified portfolio is approximately 2.7%, balancing the diversification benefits of low crypto-equity correlations against the high volatility of crypto assets.
Risks and Returns of Cryptocurrency#
A foundational paper on cryptocurrency asset pricing that characterizes the risk-return tradeoff for major cryptocurrencies. The authors document systematic risk factors in crypto markets and analyze how these factors relate to traditional asset pricing models, providing a framework for thinking about crypto risk premia.
Common Risk Factors in Cryptocurrency#
This paper develops a three-factor model for cryptocurrency returns — market, size, and momentum factors — analogous to the Fama-French model in equities. The factor model explains a substantial portion of the cross-section of crypto returns and provides a framework for evaluating whether trading strategies generate genuine alpha or simply load on common factors.
A Factor Model for Cryptocurrency Returns#
Daniele Bianchi and Mykola Babiak develop a latent-factor model for cryptocurrency returns using Instrumented Principal Component Analysis (IPCA). Rather than relying only on a small set of hand-crafted observable factors, the model extracts latent risk drivers from a large cross-section of cryptocurrency pairs while allowing factor loadings to vary with observable characteristics such as liquidity, size, reversal, and downside risk.
Our summary: this paper is the natural next step after the early crypto factor literature. Whereas Liu-Tsyvinski-Wu establish that crypto has its own factor structure, Bianchi and Babiak ask whether a more flexible latent-factor model can describe that structure better than fixed observable factors. The answer is yes: crypto returns appear to have a richer, time-varying risk architecture than simple bottom-up factor portfolios can capture.
ASRI: An Aggregated Systemic Risk Index for Cryptocurrency Markets#
This paper develops a systemic risk framework for cryptocurrency markets that simultaneously identifies high-risk periods and characterizes the market regime. The ASRI (Aggregated Systemic Risk Index) combines multiple risk indicators to provide an early warning system for periods of elevated contagion risk in crypto ecosystems.
Crypto Contagion#
This paper examines how the introduction of Bitcoin ETFs affected the diversification properties of crypto assets within traditional portfolios. The authors find that ETF introduction inverted the diversification relationship — crypto assets became more correlated with equities — demonstrating that institutional adoption changed the fundamental risk characteristics of the asset class.
The Low-Volatility Factor in Cryptocurrency Asset Selection#
Research Article #25 from Trading Research Hub. Studies the low volatility factor in cryptocurrency markets. The study examines whether selecting lower-volatility cryptocurrencies produces better risk-adjusted returns than investing in higher-volatility alternatives, as documented in traditional equity markets.
The article provides a factor-based analysis of crypto assets sorted by volatility, testing whether the low volatility premium documented in academic finance literature extends to digital assets. The practical implications for portfolio construction and asset selection in crypto are explored through systematic backtesting.
By Pedma.
Bitcoin Price Formation and Signed Order Flow#
Bitcoin prices are strongly shaped by who is aggressively buying and selling — 60% of the variation in its daily returns can be explained by signed order flow. Unlike stocks, which have multiple price anchors beyond trade imbalances, Bitcoin’s order flow explains substantially more variance. In this respect, Bitcoin resembles currencies more than equities.
The analysis, using Binance futures data, replicates prior research that found signed flow previously explained 85% of Bitcoin’s variability. While order flow’s explanatory power has decreased over time, it remains significant at 60% on average, with shorter timeframes revealing ranges from 40% to 80%.
Mentioned by Lev (@LeafiestParapet) in a discussion about Bitcoin’s flow-driven price dynamics.
By Lev.
Optimal Buy-and-Hold Asset Allocation: A Multi-Horizon Drawdown-Constrained Approach#
Yanshu Wang and Luna Wang (Peking University) investigate optimal buy-and-hold portfolio allocation using drawdown-constrained optimization across multiple investment horizons of 5, 10, 20, and 30 years. Using historical ETF data from 1996 to 2026, the paper discovers a remarkably consistent “40/40/20” framework — 40% growth equities, 40% gold, and 20% Bitcoin — that maximizes risk-adjusted returns across all tested time horizons.
Our summary: the paper’s main contribution is not the specific weights but the framework: drawdown-constrained optimization consistently lands on assets with genuinely different regime characteristics rather than piling into a single low-volatility bucket. The stability of the 40/40/20 split across horizons from 5 to 30 years is the surprising result.
Key metrics: optimized portfolios deliver Sharpe ratios 2-6x higher than conventional benchmarks. The optimal maximum drawdown constraint converges to -36% to -38% across all horizon lengths.
Mentioned by Ivan Blanco in this discussion.