Monitoring Volatility Change for Time Series Based on Support Vector Regression
- Resource Type
- article
- Authors
- Sangyeol Lee; Chang Kyeom Kim; Dongwuk Kim
- Source
- Entropy, Vol 22, Iss 11, p 1312 (2020)
- Subject
- GARCH-type time series
CUSUM monitoring
support vector regression
particle swarm optimization
Science
Astrophysics
QB460-466
Physics
QC1-999
- Language
- English
- ISSN
- 1099-4300
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.