Research

We propose a novel measure of Bitcoin fragility based on the asymmetric behavior of extreme returns within a parametric jump-diffusion framework. Fragility is defined as the relative dominance of crash-driven over boom-driven tail risk and admits a direct economic interpretation in terms of the existence of moments of gross and inverted returns. Unlike event-based approaches that focus on identifying individual jumps or decomposing realized volatility, our framework treats jumps as structural components shaping tail behavior and emphasizes moment dominance rather than jump timing or transmission. Using daily Bitcoin returns from 2013 to 2024 and rolling-window estimation, we document substantial time variation in fragility and examine its association with global uncertainty and volatility indicators. The empirical analysis is deliberately reduced-form and does not aim at causal identification or forecasting. We find that economic policy uncertainty is positively associated with Bitcoin fragility, while equity market volatility is negatively associated. These relationships operate primarily through changes in jump intensities rather than jump probabilities, indicating that macro-financial uncertainty affects the severity of extreme outcomes more than their frequency. Overall, our results characterize Bitcoin as a fragile speculative asset whose downside tail dominance varies systematically with global financial conditions, highlighting a distinct dimension of tail risk complementary to existing volatility- and dependence-based measures.

We develop a reproducible three-step protocol to clean daily cryptocurrency data from CoinMarketCap (CMC), one of the most used data providers in academic research. The procedure targets three recurring anomalies that distort market-level indicators: (i) extreme market-cap spikes, (ii) one-day and multi-day dips in Bitcoin dominance, and (iii) abnormal trading volumes. Using more than 28,000 cryptocurrencies from 2014–2024, we show that the method modifies only a small subset of data while improving the reliability of key market indicators. We do not adjust prices or returns, preserving actual trading conditions. As an application, we construct dynamic investable universes using cleaned data and realistic constraints based on market capitalization and volume. This exercise shows that cleaning and filtering jointly produce more reliable universes, reducing spurious extremes and making them suitable for empirical asset pricing research and portfolio construction.

The cryptocurrency market operates continuously, leading to frequent price fluctuations and information dissemination. This can hinder investors from reacting promptly to market changes, a phenomenon attributed to investors' limited attention. Research in traditional markets shows that the limited attention bias allows successful implementation of momentum strategies. However, past research on cryptocurrency markets finds mixed results. To resolve the puzzle, we utilize a survivorship bias-free dataset while accounting for variations in market capitalization and trading volume. This differentiation is crucial given young and tech affine retail investors' inclination toward smaller-capitalized cryptocurrencies, due to their higher risk tolerance and limited attention. More risk averse investors such as institutional investors, in contrast, focus more on top cryptocurrencies. In line with expectations, we find effective momentum strategies among larger-capitalized cryptocurrencies.

We propose a novel approach to improve short-term equity return predictability by analyzing truncated high-frequency return distributions. We segment returns into core and tail components, focusing on core and tail asymmetries — differences between `typical' or `extreme' upside and downside variances. Our empirical findings show that these predictors achieve an in-sample adjusted R^2 of about 7% and an out-of-sample R^2 exceeding 3% for one-month-ahead market return forecasts, outperforming traditional predictors such as valuation ratios and the variance risk premium. It is the core asymmetry, rather than the tail asymmetry that drives the predictive power.

This paper examines whether stablecoins are truly stable and evaluates the extent to which central bank digital currencies (CBDCs) provide a more resilient foundation for digital money. We argue that stablecoin stability remains contingent on collateral quality, liquidity conditions, and issuer credibility, making these instruments vulnerable to redemption shocks and shifts in macro-financial conditions. Using new evidence on Tether’s reserve composition and market dynamics between 2023 and 2025, we document the growing systemic relevance of stablecoins alongside persistent fragilities linked to liquidity mismatches and interest rate exposure. Against this backdrop, CBDCs emerge as a credible public alternative anchored in central bank balance sheets and financial stability mandates. The paper highlights how the future monetary architecture will depend not only on technological innovation, but also on institutional design and regulatory choices.

This paper examines how tokenization and programmable finance may reshape European financial sovereignty. We argue that distributed ledger technologies and tokenized infrastructures could reduce fragmentation in post-trade financial markets by integrating asset ownership, settlement, and collateral mobility within more unified programmable environments. The paper further analyzes stablecoins, tokenized deposits, and central bank digital currencies (CBDCs) as competing models of tokenized money, highlighting their implications for monetary sovereignty, financial stability, and the future architecture of digital finance in Europe.

Crypto-assets constitute a large and heterogenous asset class. Only a minority of crypto-assets qualify as crypto-currencies, while the large majority provide services that go beyond Bitcoin’s peer-to-peer payment system pioneered by Nakamoto (2008). This paper introduces a comprehensive taxonomy spanning crypto-currencies, service crypto-assets, smart-contract platforms, distributed applications and tokenized assets (including both fungible and non-fungible tokens). To illustrate stylized facts and capture various risks of the cross-section of crypto-assets this paper relies on a novel dataset covering more than 14,000 crypto-assets.