加密货币平台上的最优市场中性多变量配对交易

IF 2.1 Q2 BUSINESS, FINANCE
Hongshen Yang, Avinash Malik
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引用次数: 0

摘要

本研究提出了一种新颖的多元货币对交易套利方法,称为最优交易技术(OTT)。我们提出了一种有选择地将法定货币与加密货币锚定形成一个 "桶 "的方法,以便同时监控和利用交易机会。为了解决来自多个交易信号的定量冲突,我们设计了一种新颖的双目标凸优化公式,以平衡投资者在盈利能力和风险承受能力之间的偏好。我们知道,加密货币具有巨大的金融风险。因此,这一过程包括可调整的参数,如波动率惩罚和行动阈值。在 2020 年至 2022 年期间的加密货币市场实验中,OTT 实现了 15.49% 的年化利润,其中包括一波强劲的牛市和熊市。此外,附录中详述的补充实验将 OTT 的适用范围扩展到了后 COVID 时期的其他主要加密货币,验证了该模型在各种市场条件下的稳健性和有效性。套利操作提供了一种新的交易视角,不需要外部做空或在套利期间持有中间体。需要注意的是,本研究承认加密货币投资具有高风险性,可能会出现大幅波动和潜在损失。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal Market-Neutral Multivariate Pair Trading on the Cryptocurrency Platform
This research proposes a novel arbitrage approach in multivariate pair trading, termed the Optimal Trading Technique (OTT). We present a method for selectively forming a “bucket” of fiat currencies anchored to cryptocurrency for monitoring and exploiting trading opportunities simultaneously. To address quantitative conflicts from multiple trading signals, a novel bi-objective convex optimization formulation is designed to balance investor preferences between profitability and risk tolerance. We understand that cryptocurrencies carry significant financial risks. Therefore this process includes tunable parameters such as volatility penalties and action thresholds. In experiments conducted in the cryptocurrency market from 2020 to 2022, which encompassed a vigorous bull run followed by a bear run, the OTT achieved an annualized profit of 15.49%. Additionally, supplementary experiments detailed in the appendix extend the applicability of OTT to other major cryptocurrencies in the post-COVID period, validating the model’s robustness and effectiveness in various market conditions. The arbitrage operation offers a new perspective on trading, without requiring external shorting or holding the intermediate during the arbitrage period. As a note of caution, this study acknowledges the high-risk nature of cryptocurrency investments, which can be subject to significant volatility and potential loss.
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来源期刊
CiteScore
3.70
自引率
8.70%
发文量
100
审稿时长
11 weeks
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