Novel Data-Driven Fuzzy Algorithmic Volatility Forecasting Models with Applications to Algorithmic Trading
Novel Data-Driven Fuzzy Algorithmic Volatility Forecasting Models with Applications to Algorithmic Trading
复制标题
新型数据驱动的模糊算法波动率预测模型及其在算法交易中的应用
DOI：
10.1109/fuzz48607.2020.9177735
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
R. Thulasiram
中科院分区：
文献类型：
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作者：
A. Thavaneswaran;You Liang;Zimo Zhu;R. Thulasiram
算法交易的爆炸式增长一直是金融行业最突出的趋势之一。在本文中，两个策略的算法交易，如布林线和简单的移动平均线（SMA）交叉策略的模糊设置进行了研究。常用的布林线交易策略假设资产价格与SMA之间的差异呈正态分布。然而，它表明，数据驱动的t分布更适合于模拟资产的价格和SMA之间的差异。提出了一种新的数据驱动的模糊布林线交易策略。一个好的策略应该有一个良好的算法投资回报率与低算法波动性。因此，预测算法波动性和确定算法回报的适当分布在算法交易中起着至关重要的作用。夏普比率（Sharpe Ratio，SR）是一个衡量平均算法回报率超过每单位算法波动率的指标。对于一类具有不同窗口大小的SMA交叉策略，基于包括数据驱动波动率估计（DDVE）在内的各种风险度量计算SR的模糊估计。SR模糊预测计算使用两个最近提出的波动预测模型，如数据驱动的指数加权移动平均（DD-EWMA）和数据驱动的神经波动模型。使用模糊方法的主要原因是提供SR的α-cuts（区间预测）。一组广泛交易的科技股的实证应用表明，所提出的模型提供SR的预测误差很小。
The explosion of algorithmic trading has been one of the most prominent trends in the finance industry. In this paper, two strategies for algorithmic trading such as Bollinger bands and the simple moving average (SMA) crossover strategy are studied in the fuzzy settings. The commonly used Bollinger bands trading strategy assumes that the difference between an asset’s price and its SMA is normally distributed. However, it is shown that a data-driven t distribution is more appropriate to model the difference between an asset’s price and its SMA. A novel data-driven fuzzy Bollinger bands strategy is proposed for algo trading. A good strategy should have a good algo return on investment with low algo volatility. Therefore, forecasting algo volatility and identifying an appropriate distribution of algo returns play a crucial role in algo trading. Sharpe Ratio (SR) is a measure of average algo return earned in excess of the risk-free rate per unit of algo volatility. For a class of SMA crossover strategies with varying window sizes, fuzzy estimates of SR are computed based on various risk measures including the data-driven volatility estimate (DDVE). SR fuzzy forecasts are computed using two recently proposed volatility forecasting models such as data-driven exponentially weighted moving average (DD-EWMA) and data-driven neuro volatility models. The main reason of using the fuzzy approach is to provide α-cuts (interval forecasts) of the SR. An empirical application on a set of widely traded technology stocks shows that the proposed models deliver forecasts of SR with small errors.