Published October 28, 2026 | Version v2.0
Preprint Open

The Lead–Lag Illusion in Cryptocurrency

Contributors

  • 1. Independent Researcher

Description

Preprint & Dataset: Lead-Lag Illusion in Cryptocurrency

This record contains both the preprint manuscript and the derived event-level dataset supporting the research titled "Lead-Lag Illusion in Cryptocurrency" by Freddy Agus Wibowo.

1. Manuscript Overview:
The study screens all 136 unordered pairs among 17 hourly cryptocurrency series to identify robust lead-lag relationships using stochastic-crossover events. After applying Benjamini-Hochberg false discovery rate (FDR) correction and a six-layer validation protocol, only one pair (BTCJPY-SOLUSD) survives. The findings highlight the fragility of hourly lead-lag structures once multiple testing is controlled.

2. Dataset Overview & Compliance Note:
The dataset contains the derived event-level data required to replicate the matching logic and statistical tests presented in the paper.
Note on Data Source: The raw hourly (H1) OHLC price data were obtained from the Exness MetaTrader 4 (MT4) platform (extracted on October 9, 2026). Due to broker data redistribution policies, the raw OHLC price files cannot be publicly shared. Instead, this repository provides the derived events (timestamps, indicator values, regime labels, and match classifications), which fully supports the replication of the study's statistical methodology.

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Additional details

Related works

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Preprint: 10.5281/zenodo.23165422 (DOI)
Preprint: 10.5281/zenodo.23354718 (DOI)

Dates

Created
2026-10-23
The study screens all 136 unordered pairs among 17 hourly cryptocurrency series to identify robust lead-lag relationships using stochastic-crossover events.

References

  • Benjamini, Y., Hochberg, Y., 1995. Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B 57, 289–300.
  • Efron, B., Tibshirani, R., 1986. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statistical Science 1, 54–75.
  • Harvey, C.R., Liu, Y., Zhu, H., 2016. … and the cross-section of expected returns. Review of Financial Studies 29, 5–68.
  • Jia, Y., Wu, Y., Yan, S., Liu, Y., 2023. A seesaw effect in the cryptocurrency market: Understanding the return cross predictability of cryptocurrencies. Journal of Empirical Finance 74, 101428.
  • Kurihara, T., Matsumoto, T., 2026. Price transmission from Bitcoin to altcoins: High-frequency evidence and implications for trading strategy. Asia-Pacific Financial Markets, forthcoming.
  • Politis, D.N., Romano, J.P., 1994. The stationary bootstrap. Journal of the American Statistical Association 89, 1303–1313.
  • Romano, J.P., Wolf, M., 2005. Stepwise multiple testing as formalized data snooping. Econometrica 73, 1237–1282.
  • Sullivan, R., Timmermann, A., White, H., 1999. Data-snooping, technical trading rule performance, and the bootstrap. Journal of Finance 54, 1647–1691.
  • White, H., 2000. A reality check for data snooping. Econometrica 68, 1097–1126.
  • Wibowo, F.A., 2026. Mapping the safe-haven network: An exhaustive search for lead-lag structures across timeframes. Working paper, version 1.0, 5 October 2026.
  • Wilson, E.B., 1927. Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association 22, 209–212.