scholarly journals Monitoring Volatility Change for Time Series Based on Support Vector Regression

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1312
Author(s):  
Sangyeol Lee ◽  
Chang Kyeom Kim ◽  
Dongwuk Kim
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Stock Price ◽  
Financial Time Series ◽  
Real Data ◽  
Support Vector ◽  
Tuning Parameters ◽  
Monitoring Process ◽  
Microsoft Corporation ◽  
Cusum Method

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.

scholarly journals Hybrid CUSUM Change Point Test for Time Series with Time-Varying Volatilities Based on Support Vector Regression

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 578
Author(s):  
Sangyeol Lee ◽  
Chang Kyeom Kim ◽  
Sangjo Lee
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Stock Price ◽  
Real Data ◽  
Support Vector ◽  
Garch Models ◽  
Time Varying ◽  
Tuning Parameters ◽  
Scope Of Application ◽  
Validation Set

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.

scholarly journals Financial time series forecasting using twin support vector regression

PLoS ONE ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. e0211402 ◽  
Author(s):  
Deepak Gupta ◽  
Mahardhika Pratama ◽  
Zhenyuan Ma ◽  
Jun Li ◽  
Mukesh Prasad
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Financial Time Series ◽  
Time Series Forecasting ◽  
Support Vector ◽  
Twin Support Vector Regression ◽  
Financial Time

Forecasting Exchange Rates

2016 ◽  
pp. 1864-1883
Author(s):  
Ahmed Radhwan ◽  
Mahmoud Kamel ◽  
Mohammed Y. Dahab ◽  
AboulElla Hassanien
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Exchange Rates ◽  
Support Vector Regression ◽  
Chaos Theory ◽  
Financial Time Series ◽  
Optimization Technique ◽  
Support Vector ◽  
Data Sets ◽  
Financial Time

Accurate forecasting for future events constitutes a fascinating challenge for theoretical and for applied researches. Foreign Exchange market (FOREX) is selected in this research to represent an example of financial systems with a complex behavior. Forecasting a financial time series can be a very hard task due to the inherent uncertainty nature of these systems. It seems very difficult to tell whether a series is stochastic or deterministic chaotic or some combination of these states. More generally, the extent to which a non-linear deterministic process retains its properties when corrupted by noise is also unclear. The noise can affect a system in different ways even though the equations of the system remain deterministic. Since a single reliable statistical test for chaoticity is not available, combining multiple tests is a crucial aspect, especially when one is dealing with limited and noisy data sets like in economic and financial time series. In this research, the authors propose an improved model for forecasting exchange rates based on chaos theory that involves phase space reconstruction from the observed time series and the use of support vector regression (SVR) for forecasting.Given the exchange rates of a currency pair as scalar observations, observed time series is first analyzed to verify the existence of underlying nonlinear dynamics governing its evolution over time. Then, the time series is embedded into a higher dimensional phase space using embedding parameters.In the selection process to find the optimal embedding parameters,a novel method based on the Differential Evolution (DE) geneticalgorithm(as a global optimization technique) was applied. The authors have compared forecasting accuracy of the proposed model against the ordinary use of support vector regression. The experimental results demonstrate that the proposed method, which is based on chaos theory and genetic algorithm,is comparable with the existing approaches.

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