当前位置: X-MOL 学术Entropy › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Hybrid CUSUM Change Point Test for Time Series with Time-varying Volatilities based on Support Vector Regression
Entropy ( IF 2.7 ) Pub Date : 2020-05-20 , DOI: 10.3390/e22050578
Sangyeol Lee , Chang Kyeom Kim , Sangjo Lee

This study considers the problem of detecting a change in the conditional variance of time series with time-varying volatilities based on the cumulative sum (CUSUM) of squares test using the residuals from support vector regression (SVR)-generalized autoregressive conditional heteroscedastic (GARCH) models. To compute the residuals, we first fit SVR-GARCH models with different tuning parameters utilizing a time series of training set. We then obtain the best SVR-GARCH model with the optimal tuning parameters via a time series of the validation set. Subsequently, based on the selected model, we obtain the residuals, as well as the estimates of the conditional volatility and employ these to construct the residual CUSUM of squares test. We conduct Monte Carlo simulation experiments to illustrate its validity with various linear and nonlinear GARCH models. A real data analysis with the S&P 500 index, Korea Composite Stock Price Index (KOSPI), and Korean won/U.S. dollar (KRW/USD) exchange rate datasets is provided to exhibit its scope of application.

中文翻译:

基于支持向量回归的时变波动时间序列混合 CUSUM 变化点检验

本研究考虑了使用支持向量回归 (SVR)-广义自回归条件异方差 (GARCH) 的残差基于平方检验的累积和 (CUSUM) 检测具有时变波动率的时间序列的条件方差变化的问题楷模。为了计算残差,我们首先使用训练集的时间序列拟合具有不同调整参数的 SVR-GARCH 模型。然后,我们通过验证集的时间序列获得具有最佳调整参数的最佳 SVR-GARCH 模型。随后,基于选定的模型,我们获得残差以及条件波动率的估计值,并使用这些来构建平方检验的残差 CUSUM。我们进行了蒙特卡罗模拟实验,以说明其对各种线性和非线性 GARCH 模型的有效性。提供了标准普尔 500 指数、韩国综合股价指数 (KOSPI) 和韩元/美元 (KRW/USD) 汇率数据集的真实数据分析,以展示其应用范围。
更新日期:2020-05-20
down
wechat
bug