Trading of Bitcoin is spread about multiple venues where buying and selling is offered in various currencies. However, all exchanges trade one common good and by the law of one price, the different prices should not deviate in the long run. In this context, we are interested in which platform is the most important one in terms of price discovery. To this end, we use a pairwise approach accounting for a potential impact of exchange rates. The contribution to price discovery is measured by Hasbrouck's and Gonzalo and Granger's information share. We then derive an ordering with respect to the importance of each market which reveals that the Chinese OKCoin platform is the leader in price discovery of Bitcoin, followed by BTC China. Overall, the exchange rate is neither affected by Bitcoin trading nor does it contribute decisively to its price discovery.
Bitcoins are traded on various exchange platforms and, therefore, prices may differ across trading venues. We aim to investigate return connectedness across eight of the major exchanges of Bitcoin, both from a static and a dynamic viewpoint. To this end, we employ an extension of the order‐invariant forecast error variance decomposition proposed by Diebold and Yilmaz (2012) to a generalized vector error correction framework. Our results suggest that there is strong connectedness among the exchanges, as expected, although some of them behave dissimilarly. We identify Bitfinex and Coinbase as leading exchanges during the considered period, while Kraken as a follower exchange. We also obtain that connectedness across exchanges is strongly dynamic, as it evolves over time.
The study of connectedness is key to assess spillover effects and identify lead-lagrelationships among market exchanges trading the same asset. By means of an extension of Dieboldand Yilmaz (2012) econometric connectedness measures, we examined the relationships of five majorBitcoin exchange platforms during two periods of main interest: the 2017 surge in prices and the 2018decline. We concluded that Bitfinex and Gemini are leading exchanges in terms of return spillovertransmission during the analyzed time-frame, while Bittrexs act as a follower. We also found thatconnectedness of overall returns fell substantially right before the Bitcoin price hype, whereas itleveled out during the period the down market period. We confirmed that the results are robust withregards to the modeling strategies.
In this paper we aimed to examine the profitability of technical trading rules in the Bitcoin market by using trend-following and mean-reverting strategies. We applied our strategies on the Bitcoin price series sampled both at 5-min intervals and on a daily basis, during the period 1 January 2012 to 20 August 2019. Our findings suggest that, overall, trading on daily data is more profitable than going intraday. Furthermore, we concluded that the Buy and Hold strategy outperforms the examined alternatives on an intraday basis, while Simple Moving Averages yield the best performances when dealing with daily data.
Despite the current growing interest in Bitcoins—and cryptocurrencies in general—financial instruments, as well as studies related to them, are quite underdeveloped. Therefore, this article aims to provide a suitable pricing model for options written on this peculiar underlying. This is done through an artificial neural network approach, where classical pricing models—namely the trinomial tree, Monte Carlo simulation, and explicit finite difference method—are used as input layers. Results show that options written on Bitcoin turn out to be systematically overpriced when considering classical methods, whereas a noticeable improvement in price predictions is achieved by means of the proposed neural network model.
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