Lee Sangyeol, Kim Chang Kyeom, Kim Dongwuk
Department of Statistics, Seoul National University, Seoul 08826, Korea.
Entropy (Basel). 2020 Nov 17;22(11):1312. doi: 10.3390/e22111312.
This paper considers monitoring an anomaly from sequentially observed time series with heteroscedastic conditional volatilities based on the cumulative sum (CUSUM) method combined with support vector regression (SVR). The proposed online monitoring process is designed to detect a significant change in volatility of financial time series. The tuning parameters are optimally chosen using particle swarm optimization (PSO). We conduct Monte Carlo simulation experiments to illustrate the validity of the proposed method. A real data analysis with the S&P 500 index, Korea Composite Stock Price Index (KOSPI), and the stock price of Microsoft Corporation is presented to demonstrate the versatility of our model.
本文考虑基于累积和（CUSUM）方法与支持向量回归（SVR）相结合，对具有异方差条件波动率的顺序观测时间序列中的异常进行监测。所提出的在线监测过程旨在检测金融时间序列波动率的显著变化。使用粒子群优化（PSO）对调谐参数进行最优选择。我们进行蒙特卡罗模拟实验以说明所提方法的有效性。给出了对标准普尔500指数、韩国综合股价指数（KOSPI）以及微软公司股价的实际数据分析，以证明我们模型的通用性。
