In 2016, BitMEX introduced a novel type of crypto derivates – Perpetual Swaps, i.e., futures with an infinite term. Perpetual swaps provide a new strategic risk management tool for cryptocurrencies due to their custody-free nature, high leverage, and funding mechanism, but there has been little quantitative analysis on the their benefits. In this paper, we introduce a trading strategy that combines a Quanto Perpetual Swap with a spot position to benefit from the funding mechanism. We compare our strategy with a long-only investment in the underlying cryptocurrency and a similar strategy based on Linear Perpetual Swaps to evaluate their performances in a large-scale backtest covering the years 2021 and 2022. Our analysis shows that our strategy generates positive returns in bullish market phases of the underlying with lower volatility.

This paper investigates the implied volatility spillovers of three commodities (bitcoin, gold and oil) onto the stock market (VIX) to determine if bitcoin behaves differently from other commodities in terms of its effect on stock market behaviour. To capture any asymmetry in terms of the change in implied volatility of these commodity markets, we apply a time-lagged asymmetric quantile regression (QR), a nonlinear and heterogeneity-consistent model. Through the data analysis with daily implied volatility data from January 2019 to November 2023 we find an asymmetric relation: impacts of positive changes in gold and oil implied volatility on changes in VIX are stronger than impacts of negative changes, particularly at upper quantiles. This finding supports the intuition that the increase in volatility has a stronger spillover effect than an equivalent magnitude decrease in volatility. We also confirm that gold and oil have tail risk in contrast to bitcoin. We further find that implied volatility in the bitcoin market has less explanatory power with respect to implied volatility of the stock market compared to gold and oil. Taken together, these results can support policy makers and market participants by informing them that bitcoin differs in nature to traditional commodities and works as an effective diversification tool.

This paper examines the hedging effectiveness of Bitcoin futures by comparing one form of the constant model, the conventional OLS method, with the time-varying model in estimating the optimal hedge ratio. For the time-varying model, we employ a powerful technique, Kalman filter, a r ecursive a lgorithm w hich h as n umerous real-time, technological applications, but has not been employed in the context of Bitcoin optimal hedge ratio analysis. Through applying the spot and futures daily settlement prices from 18th December 2017 to 30th November 2022 to the two models, we confirm that t he B itcoin futures is an effective instrument for risk hedging. Additionally, we find the dynamic model based on the Kalman filter p erforms b etter - especially in 2019 and 2020 - than the conventional OLS method in terms of risk reduction, supporting previous findings in the context of other commodity futures. We also certify that the Kalman filter s uccessfully c aptures the trend of the optimal hedge ratio, thus enabling hedgers to decide when to change their hedging strategy. Furthermore, we verify the volatile evolution of the estimated time-varying Bitcoin optimal hedge ratio, suggesting the need to further search for a better hedging instrument which achieves a less volatile time path to avoid excessive trading costs.

Yield farming has been an immensely popular activity for cryptocurrency holders since the explosion of Decentralized Finance (DeFi) in the summer of 2020. In this Systematization of Knowledge (SoK), we study a general framework for yield farming strategies with empirical analysis. First, we summarize the fundamentals of yield farming by focusing on the protocols and tokens used by aggregators. We then examine the sources of yield and translate those into three example yield farming strategies, followed by the simulations of yield farming performance, based on these strategies. We further compare four major yield aggregators—Idle, Pickle, Harvest and Yearn—in the ecosystem, along with brief introductions of others. We systematize their strategies and revenue models, and conduct an empirical analysis with on-chain data from example vaults, to find a plausible connection between data anomalies and historical events. Finally, we discuss the benefits and risks of yield aggregators.

This paper examines the determinants of the volatility of futures prices and basis for three commodities: gold, oil and Bitcoin – often dubbed solid, liquid and digital gold – by using contract-by-contract analysis which has been previously applied to crude oil futures volatility investigations. By extracting the spot and futures daily prices as well as the maturity, trading volume and open interest data for the three assets from 18th December 2017 to 30th November 2021, we find a positive and significant role for trading volume and a possible negative influence of open interest (when significant) in shaping the volatility in all three assets, supporting earlier findings in the context of oil futures. Additionally, we find maturity has a relatively positive significance for Bitcoin and oil futures price volatility. Furthermore, our analysis demonstrates that maturity affects the basis of Bitcoin and gold positively – confirming the general theory that the basis converges to zero as maturity nears for Bitcoin and gold – while oil is affected in both directions.