基于小波GARCH模型的协整策略交易信号指标体系优化

Trading Signal Index Optimization of Co-integration Strategy Based on Wavelet GARCH Model

基于固定阈值构建的协整策略交易信号指标体系极易过早触发建仓交易,导致协整策略在价差波动峰值处平仓止损,不适用于波动集聚时间序列。本文基于状态空间模型,构建时变系数协整统计套利策略,采用五大国有银行A股收盘价时间序列数据进行实证分析,利用GARCH模型提取价差序列波动集聚信息,应用小波降噪技术优化时变交易信号指标体系。小波降噪GARCH时变阈值协整策略在2018年1—6月动荡期行情的夏普比率高达2.284,较同期未降噪时变阈值协整策略提高18.4%,较同期固定阈值协整策略提高109.5%;三组策略在2018年7—12月的平稳期行情中表现惨淡,夏普比率均低于-2,且降噪后夏普比率略低于未降噪策略。实证结果表明:在行情动荡期,基于GARCH模型构建的时变交易阈值可有效捕捉波动集聚信息,提高发生跳跃价差时模型建仓点位精准度,经小波降噪优化可降低因噪声信号错误触发交易的可能,显著提升行情策略夏普比率。在行情平稳期,小波降噪时变阈值协整策略与未降噪、定阈值协整策略表现低迷,均不适用于价差波动较小的平稳期行情。

The trading signal index system of cointegration strategy based on the fixed threshold is easy to trigger the trading too early, which leads to the cointegration strategy closing the stop loss at the peak of spread fluctuation, which is not suitable for the volatility aggregation time series. Based on the state space model, the time-varying coefficient cointegration statistical arbitrage strategy is constructed, the time series data of five state-owned banks’ A-share closing price are used for empirical analysis, GARCH model is used to extract the volatility agglomeration information of spread series, and wavelet de-noising technology is used to optimize the time-varying trading signal index system. The Sharpe ratio of wavelet de-noising GARCH time-varying threshold cointegration strategy is as high as 2.284 in the turbulent period from January to June of 2018, which is 18.4% higher than that of non de-noising time-varying threshold cointegration strategy and 109.5% higher than that of fixed threshold cointegration strategy in the same period;In the stable period from July to December of 2018, the sharpe ratio of the three strategies was lower than-2, and the sharpe ratio after noise reduction was slightly lower than that of the non noise reduction strategy. The empirical results show that: in the period of market turbulence, the time-varying trading threshold based on GARCH model can effectively capture the volatility agglomeration information, improve the accuracy of the model position when the jump spread occurs, and the wavelet de-noising optimization can reduce the possibility of triggering trading due to the wrong noise signal, and significantly improve the bank strategy Sharpe ratio. In the stable period, wavelet de-noising time-varying threshold cointegration strategy and non de noising, fixed threshold cointegration strategy are not suitable for the stable period.