论文标题
量化波动性预测的新型方法
A novel approach to quantify volatility prediction
论文作者
论文摘要
金融市场中的波动性预测有助于了解投资的利润并涉及投资风险。但是，由于不规则性，高波动和时间序列中的噪声，预测波动性构成了一项具有挑战性的任务。在最近的Covid-19大流行状况中，使用复杂智能技术的波动性预测吸引了全球研究人员的极大关注。在本文中，在两种方法中基于可靠的最小二乘法的一种新颖而简单的方法a）没有LAR的绝对残留物（LAR）和B），已应用于芝加哥董事会期权交换（CBOE）波动率指数（VIX）十年。为了进行更深入的分析，波动性时间序列已分解为长期趋势，季节性和随机波动。数据集已分为部分。培训数据集和测试数据集。使用均方根误差（RMSE）值实现了验证结果。已经发现，具有LAR方法的稳健最小二乘法可以更好地效果（RMSE = 0.01366）及其组件。长期趋势（RMSE = 0.10087），季节性（RMSE = 0.010343）和剩余波动（RMSE = 0.014783）。首次提出了挥发性及其三个组成部分的广义预测方程。在该领域工作的年轻研究人员可以直接使用提出的预测方程来了解其数据集。
Volatility prediction in the financial market helps to understand the profit and involved risks in investment. However, due to irregularities, high fluctuations, and noise in the time series, predicting volatility poses a challenging task. In the recent Covid-19 pandemic situation, volatility prediction using complex intelligence techniques has attracted enormous attention from researchers worldwide. In this paper, a novel and simple approach based on the robust least squares method in two approaches a) with least absolute residuals (LAR) and b) without LAR, have been applied to the Chicago Board Options Exchange (CBOE) Volatility Index (VIX) for a period of ten years. For a deeper analysis, the volatility time series has been decomposed into long-term trends, and seasonal, and random fluctuations. The data sets have been divided into parts viz. training data set and testing data set. The validation results have been achieved using root mean square error (RMSE) values. It has been found that robust least squares method with LAR approach gives better results for volatility (RMSE = 0.01366) and its components viz. long term trend (RMSE = 0.10087), seasonal (RMSE = 0.010343) and remainder fluctuations (RMSE = 0.014783), respectively. For the first time, generalized prediction equations for volatility and its three components have been presented. Young researchers working in this domain can directly use the presented prediction equations to understand their data sets.
