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Market dynamics, cyclical patterns and market states: Is there a difference between digital currencies markets?

机译:市场动态,周期性模式和市场状态:数字货币市场有区别吗?

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Purpose - In this paper, the authors seek to investigate the dynamics of Bitcoin, Litecoin, Ethereum and Ripple daily returns and volatilities. Design/methodology/approach - In this paper, the authors apply the MS-ARMA model on daily returns of Bitcoin (19/04/2013-13/02/2018), Ripple (05/08/2013-14/02/2018), Litcoin (29/04/2013-14/02/2018) and Ethereum (08/02/2015-14/02/2018). This model allows capture of the nonlinear structure in both the conditional mean and the conditional variance of cryptocurrency returns. Findings - All the cryptocurrency markets show regime switching in the return-generating process. Market dynamics seem to be governed by two different states which differ from one cryptocurrency market to another in terms of mean return, volatility and interstate dynamics. These findings can be explained by investors' behavior, i.e. speculative trading and herding behavior. By choosing to participate (or imitating some investors) in some cryptocurrency markets (in particular Bitcoin market), they affect the price movements and therefore the market dynamics in the short run. Practical implications - Identifying the different market states provides information for investors to make more accurate portfolio decisions in the virtual market and follow the market timing strategy. Originality/value - This paper attempts to analyze potential nonlinear structure in cryptocurrencies returns and analyze if there is a difference between the cryptocurrencies market cycles. So, the search for congruent and adequate specification to reproduce the stock returns dynamics in the virtual market still remains the concern of several empirical studies. This research not only examines the behavior of stock returns in the cryptocurrencies' market but also highlights the existence of nonlinearity propriety as a stylized fact.
机译:目的 - 在本文中,提交人寻求调查比特币,利奇素,以外雷亚姆和涟漪日常回报和挥发性的动态。设计/方法/方法 - 在本文中,作者将MS-ARMA模型适用于比特币的每日回报(19/04 / 2013-13 / 02/202 / 2018),波纹(05/08 / 2013-14 / 02/2018 ),Litcoin(29/04 / 2013-14/02 / 2018)和Ethereum(08/02 / 2015-14/02 / 2018)。该模型允许在密码均值的条件均值和条件方差中捕获非线性结构。调查结果 - 所有加密货币市场都显示回报生成过程中的政权切换。市场动态似乎受到两种不同状态的管辖,这在平均回报,波动和州际动态方面与另一个不同的国家不同。这些发现可以通过投资者的行为来解释,即推测交易和掠夺行为。通过选择参加(或模仿一些投资者)在一些加密货币(特别是比特币市场)中,它们影响价格变动,因此在短期内部的市场动态。实际意义 - 识别不同的市场国家为投资者提供更多准确的投资组合决策,并遵循市场时机战略。原创性/值 - 本文试图分析加密货币中的潜在非线性结构返回并分析如果隐络货币市场周期存在差异。因此,在虚拟市场中寻找一致性和充足的规范来复制股票回报动态仍然是若干实证研究的关注。这项研究不仅检查了加密货币市场的股票回报的行为,还突出了非线性礼仪的存在作为风格化事实。

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