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在加密货币投资组合中预测波动性的LSTM-GARCH混合模型
Andrés García-Medina1,2, Ester Aguayo-Moreno1
1Unidad Monterrey, Centro de Investigación en Matemáticas, Alianza Centro 502, 66628 Apodaca, Nuevo Leon Mexico.
Computational economics
|June 26, 2023
概括
使用诸如多层感知器 (MLP) 和长短期记忆 (LSTM) 等深度学习模型预测加密货币波动性,比传统的通用自回归条件异构复杂性 (GARCH) 模型显示出更高的性能. 这些先进的模型在预测中提供了更高的准确性,特别是在市场不确定性期间.
科学领域:
- 量化金融 量化金融
- 计算经济学计算经济学
- 机器学习 机器学习
背景情况:
- 加密货币市场具有很高的波动性,需要强大的预测模型.
- 随着COVID-19大流行,市场出现了显著的不确定性,影响了金融时间序列.
- 像GARCH这样的传统模型可能无法完全捕捉到加密货币波动的复杂动态.
研究的目的:
- 为了比较深度学习模型 (MLP,LSTM,LSTM-GARCH) 与加密货币波动性GARCH模型的预测准确度.
- 评估交易量对投资组合风险的影响以及不同投资策略的表现.
- 在世卫组织宣布流行病后的前所未有的市场条件下评估模型性能.
主要方法:
- 采用了通用自回归条件异种性 (GARCH) 模型.
- 使用多层感知器 (MLP) 和长短期记忆 (LSTM) 神经网络.
- 开发了混合LSTM-GARCH模型,将GARCH参数作为LSTM特征.
- 分析了2020年3月疫情宣告周围时期的每小时加密货币数据.
主要成果:
- 深度神经网络变体 (MLP,LSTM,LSTM-GARCH) 在预测波动性方面表现优于GARCH模型,特别是在异质二进制误差,绝对误差和平方误差 (HSE) 方面.
- 一个统一的投资组合的波动性预测,特别是长期的波动性预测,系统地超过了基于夏普比率的稳定币Tether (无风险资产).
- 纳入交易量减少了统一投资组合的风险价值 (VaR).
- 投资组合分配从大流行前集中的比特币 (BTC) 持有转移到大流行后更多多元化的策略,包括BTC的空头.
结论:
- 多层感知器 (MLP) 模型显示了加密货币波动的最佳预测结果,尽管在统计学上与LSTM和LSTM-GARCH没有什么不同.
- 与许多传统金融模型相比,MLP模型提供了更高的性能,并且在计算上比复杂的神经网络更高效.
- 建议使用简单的学习模型来预测高度非线性时间序列中的波动性,例如加密货币市场.
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