BitMEX is the largest unregulated bitcoin derivatives exchange, listing contracts suitable for leverage trading and hedging. Using minute‐by‐minute data, we examine its price discovery and hedging effectiveness. We find that BitMEX derivatives lead prices on major bitcoin spot exchanges. Bid–ask spreads, interexchange spreads, and relative trading volumes are important determinants of price discovery. Further analysis shows that BitMEX derivatives have positive net spillover effects, are informationally more efficient than bitcoin spot prices, and serve as effective hedges against spot price volatility. Our evidence suggests that regulators prioritize the investigation of the legitimacy of BitMEX and its contracts.

This paper studies the contribution of newly launched SSE 50 Index‐based options and futures to price discovery. We find that the derivatives markets quickly begin exhibiting price leadership over the corresponding spot market, despite their short history; the information share from both derivatives markets rose from 59.84% in mid‐2015 to 84.6% in mid‐2017. Using substantial regulation changes during the sample period, we test the trading cost hypothesis. The increases in derivatives transaction costs do not immediately impede their roles in price discovery. Findings suggest that in nascent and immature markets, investors’ trading experience matters more than trading costs.

We propose a quantum harmonic oscillator as a model for the market force which draws a stock return from short-run fluctuations to the long-run equilibrium. The stochastic equation governing our model is transformed into a Schrödinger equation, the solution of which features “quantized” eigenfunctions. Consequently, stock returns follow a mixed χ distribution, which describes Gaussian and non-Gaussian features. Analyzing the Financial Times Stock Exchange (FTSE) All Share Index, we demonstrate that our model outperforms traditional stochastic process models, e.g., the geometric Brownian motion and the Heston model, with smaller fitting errors and better goodness-of-fit statistics. In addition, making use of analogy, we provide an economic rationale of the physics concepts such as the eigenstate, eigenenergy, and angular frequency, which sheds light on the relationship between finance and econophysics literature.

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