Monitoring Volatility Change for Time Series Based on Support Vector Regression

Monitoring Volatility Change for Time Series Based on Support Vector Regression

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...

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Bibliographic Details
Main Authors: Sangyeol Lee, Chang Kyeom Kim, Dongwuk Kim
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Entropy
Subjects:
GARCH-type time series
CUSUM monitoring
support vector regression
particle swarm optimization
Online Access:https://www.mdpi.com/1099-4300/22/11/1312
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spelling doaj-bd0fa58d11564d8d9e0d59662bc3ea8e2020-11-25T04:09:54ZengMDPI AGEntropy1099-43002020-11-01221312131210.3390/e22111312Monitoring Volatility Change for Time Series Based on Support Vector RegressionSangyeol Lee0Chang Kyeom Kim1Dongwuk Kim2Department of Statistics, Seoul National University, Seoul 08826, KoreaDepartment of Statistics, Seoul National University, Seoul 08826, KoreaDepartment of Statistics, Seoul National University, Seoul 08826, KoreaThis 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.https://www.mdpi.com/1099-4300/22/11/1312GARCH-type time seriesCUSUM monitoringsupport vector regressionparticle swarm optimization
collection DOAJ
language English
format Article
sources DOAJ
author Sangyeol Lee
Chang Kyeom Kim
Dongwuk Kim
spellingShingle Sangyeol Lee
Chang Kyeom Kim
Dongwuk Kim
Monitoring Volatility Change for Time Series Based on Support Vector Regression
Entropy
GARCH-type time series
CUSUM monitoring
support vector regression
particle swarm optimization
author_facet Sangyeol Lee
Chang Kyeom Kim
Dongwuk Kim
author_sort Sangyeol Lee
title Monitoring Volatility Change for Time Series Based on Support Vector Regression
title_short Monitoring Volatility Change for Time Series Based on Support Vector Regression
title_full Monitoring Volatility Change for Time Series Based on Support Vector Regression
title_fullStr Monitoring Volatility Change for Time Series Based on Support Vector Regression
title_full_unstemmed Monitoring Volatility Change for Time Series Based on Support Vector Regression
title_sort monitoring volatility change for time series based on support vector regression
publisher MDPI AG
series Entropy
issn 1099-4300
publishDate 2020-11-01
description 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.
topic GARCH-type time series
CUSUM monitoring
support vector regression
particle swarm optimization
url https://www.mdpi.com/1099-4300/22/11/1312
work_keys_str_mv AT sangyeollee monitoringvolatilitychangefortimeseriesbasedonsupportvectorregression
AT changkyeomkim monitoringvolatilitychangefortimeseriesbasedonsupportvectorregression
AT dongwukkim monitoringvolatilitychangefortimeseriesbasedonsupportvectorregression
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