Hybrid CUSUM Change Point Test for Time Series with Time-Varying Volatilities Based on Support Vector Regression
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)...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-05-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/22/5/578 |
| _version_ | 1827716456618393600 |
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| author | Sangyeol Lee Chang Kyeom Kim Sangjo Lee |
| author_facet | Sangyeol Lee Chang Kyeom Kim Sangjo Lee |
| author_sort | Sangyeol Lee |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T19:42:01Z |
| format | Article |
| id | doaj.art-96ecb242c5834888b3b2baa5df51e388 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T19:42:01Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-96ecb242c5834888b3b2baa5df51e3882023-11-20T01:09:58ZengMDPI AGEntropy1099-43002020-05-0122557810.3390/e22050578Hybrid CUSUM Change Point Test for Time Series with Time-Varying Volatilities Based on Support Vector RegressionSangyeol Lee0Chang Kyeom Kim1Sangjo Lee2Department of Statistics, Seoul National University, Seoul 08826, KoreaDepartment of Statistics, Seoul National University, Seoul 08826, KoreaDepartment of Statistics, Seoul National University, Seoul 08826, KoreaThis 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.https://www.mdpi.com/1099-4300/22/5/578GARCH time serieschange point detectionCUSUM of squares testsupport vector regressionmachine learning |
| spellingShingle | Sangyeol Lee Chang Kyeom Kim Sangjo Lee Hybrid CUSUM Change Point Test for Time Series with Time-Varying Volatilities Based on Support Vector Regression Entropy GARCH time series change point detection CUSUM of squares test support vector regression machine learning |
| title | Hybrid CUSUM Change Point Test for Time Series with Time-Varying Volatilities Based on Support Vector Regression |
| title_full | Hybrid CUSUM Change Point Test for Time Series with Time-Varying Volatilities Based on Support Vector Regression |
| title_fullStr | Hybrid CUSUM Change Point Test for Time Series with Time-Varying Volatilities Based on Support Vector Regression |
| title_full_unstemmed | Hybrid CUSUM Change Point Test for Time Series with Time-Varying Volatilities Based on Support Vector Regression |
| title_short | Hybrid CUSUM Change Point Test for Time Series with Time-Varying Volatilities Based on Support Vector Regression |
| title_sort | hybrid cusum change point test for time series with time varying volatilities based on support vector regression |
| topic | GARCH time series change point detection CUSUM of squares test support vector regression machine learning |
| url | https://www.mdpi.com/1099-4300/22/5/578 |
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