Monitoring Volatility Change for Time Series Based on Support Vector Regression

Entropy

ChangKyeom Kim¹  Sangyeol Lee¹  Dongwuk Kim¹ 

[1] Department of Statistics, Seoul National University, Seoul 08826, Korea;

关键词: GARCH-type time series;    CUSUM monitoring;    support vector regression;    particle swarm optimization;   

DOI  :  10.3390/e22111312

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown