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Modeling Bitcoin Perpetual Swap Funding Rates with GARCH

Article arXiv papers · Author: Sai Srikar Nimmagadda et al.
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Summary

The paper examines the relationship between BitMEX inverse perpetual swap contracts and their associated funding rates. It reports that funding rates are heteroskedastic and uses Granger causality analysis to assess directional relationships between funding rates and Bitcoin inverse perpetual swap contracts. The study also develops a funding-rate prediction approach based on best-fitting GARCH models.

The authors discuss whether funding rates can help gauge market trends and present implications of their findings. The description does not specify the data period, the selected GARCH specifications, forecast accuracy, or the direction and strength of the causal results. Granger causality indicates predictive precedence within the tested data rather than proving an economic mechanism, and the available summary does not show whether the forecasting results remain useful after changing market regimes or accounting for trading costs.

Key ideas

  • The paper studies funding rates on BitMEX inverse perpetual swaps linked to Bitcoin.
  • It characterizes funding rates as heteroskedastic.
  • Granger causality is used to examine predictive relationships between rates and swap contracts.
  • GARCH models are fitted to predict funding rates.
  • The description does not provide forecast accuracy or show that funding rates produce profitable trades.

Tags

CryptoDerivatives pricingPerpetual futuresVolatilityStatistics
Full text
# BitMEX Funding Correlation with Bitcoin Exchange Rate


# BitMEX Funding Correlation with Bitcoin Exchange Rate









This paper examines the relationship between Inverse Perpetual Swap contracts, a Bitcoin derivative akin to futures and the margin funding interest rates levied on BitMEX. This paper proves the Heteroskedastic nature of funding rates and goes onto establish a causal relationship between the funding rates and the Bitcoin inverse Perpetual swap contracts based on Granger causality. The paper further dwells into developing a predictive model for funding rates using best-fitted GARCH models. Implications of the results are presented, and funding rates as a predictive tool for gauging the market trend is discussed.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.