利用高频标普收益率缩放特性构建日内交易模型
文章 arXiv papers · 作者: Fulvio Baldovin et al.
总结
本文利用汇总后的标普500高频收益率的缩放特性，构建鞅随机模型。该模型旨在复现早盘时段的条件期望，之后扩展到午盘交易。模型预测会生成日内趋势跟踪信号；这些信号来自模型，而非图表形态。所提策略试图利用标普数据中存在、但模型中不存在的线性相关性。
报告称，样本内和样本外测试表现优于基于非对称GARCH过程的基准，并发现了小幅套利机会。作者指出，若不存在这些线性相关性，利润就会消失；他们还讨论了该方法在对冲标普相关产品波动率风险方面的潜在用途。所提供的描述未给出测试日期、实施假设、交易成本或数值绩效详情，因此难以评估稳健性和可交易性。报告的优势取决于指定的数据特征和模型比较。
核心观点
- 研究利用汇总高频收益率的缩放特性，构建用于条件期望的鞅模型。
- 模型从早盘时段扩展到午盘交易。
- 模型预测生成日内趋势跟踪信号，不使用基于图表的标准。
- 报告中的策略利润取决于标普数据中存在的线性相关性。
- 测试报告显示其优于非对称GARCH基准，但未提供实施详情。
标签
全文
# Ensemble properties of high frequency data and intraday trading rules # Ensemble properties of high frequency data and intraday trading rules Regarding the intraday sequence of high frequency returns of the S&P index as daily realizations of a given stochastic process, we first demonstrate that the scaling properties of the aggregated return distribution can be employed to define a martingale stochastic model which consistently replicates conditioned expectations of the S&P 500 high frequency data in the morning of each trading day. Then, a more general formulation of the above scaling properties allows to extend the model to the afternoon trading session. We finally outline an application in which conditioned forecasting is used to implement a trend-following trading strategy capable of exploiting linear correlations present in the S&P dataset and absent in the model. Trading signals are model-based and not derived from chartist criteria. In-sample and out-of-sample tests indicate that the model-based trading strategy performs better than a benchmark one established on an asymmetric GARCH process, and show the existence of small arbitrage opportunities. We remark that in the absence of linear correlations the trading profit would vanish and discuss why the trading strategy is potentially interesting to hedge volatility risk for S&P index-based products.
在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0
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