Financial Volatility Forecasting by Least Square Support Vector Machine Based on GARCH, EGARCH and GJR Models: Evidence from ASEAN Stock Markets
International Journal of Economics and FinancePublished 12 January 2010Open access
Phichhang Ou, Hengshan Wang
Citations27
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
Using hybrid models, LSSVM provides improved performances in forecasting the leverage effect volatilities, especially during the recently global financial market crashes in 2008.
Abstract
In this paper, we aim at comparing semi-parametric method, LSSVM (Least square support vector machine), with the classical GARCH(1,1), EGARCH(1,1) and GJR(1,1) models to forecast financial volatilities of three major ASEAN stock markets. More precisely, the experimental results suggest that using hybrid models, GARCH-LSSVM, EGARCH-LSSVM and GJR-LSSVM provides improved performances in forecasting the leverage effect volatilities, especially during the recently global financial market crashes in 2008.
Keywords
Decision SciencesEconomics, Econometrics and Finance
The Nature of Statistical Learning Theory
39,279 Citations1995Vladimir Vapnik
Neural Networks: A Comprehensive Foundation
29,816 Citations1998Simon Haykin
Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Journal of EconometricsGeneralized autoregressive conditional heteroskedasticity
22,300 Citations1986Tim Bollerslev
EconometricaAutoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
20,637 Citations1982Robert F. Engle
Cambridge University Press eBooksAn Introduction to Support Vector Machines and Other Kernel-based Learning Methods
13,883 Citations2000Nello Cristianini, John Shawe‐Taylor
EconometricaConditional Heteroskedasticity in Asset Returns: A New Approach
10,400 Citations1991Daniel B. Nelson
Neural Processing LettersLeast Squares Support Vector Machine Classifiers
9,375 Citations1999Johan A. K. Suykens, Joos Vandewalle
A least squares version for support vector machine (SVM) classifiers that follows from solving a set of linear equations, instead of quadratic programming for classical SVM's.
The Journal of FinanceOn the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks
8,683 Citations1993Lawrence R. Glosten, Ravi Jagannathan +1 more
The Journal of FinanceMeasuring and Testing the Impact of News on Volatility
3,698 Citations1993Robert F. Engle, Victor Ng
NeurocomputingWeighted least squares support vector machines: robustness and sparse approximation
1,249 Citations2002Johan A. K. Suykens, Jos De Brabanter +2 more
The methods of this paper are illustrated for RBF kernels and demonstrate how to obtain robust estimates with selection of an appropriate number of hidden units, in the case of outliers or non-Gaussian error distributions with heavy tails.
Cambridge University Press eBooksA Long Memory Property of Stock Market Returns and a New Model
993 Citations2010Zhuanxin Ding, Robert F. Engle +1 more
The Journal of FinanceMeasuring and Testing the Impact of News on Volatility
763 Citations1993Robert Engle, Victor Ng
Machine LearningBenchmarking Least Squares Support Vector Machine Classifiers
749 Citations2003Tony Van Gestel, Johan A. K. Suykens +6 more
Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances, consistently very good when compared to a variety of methods described in the literature.
Neural NetworksOptimal control by least squares support vector machines
530 Citations2001Johan A. K. Suykens, Joos Vandewalle +1 more
This paper introduces the use of least squares support vector machines (LS-SVM's) for the optimal control of nonlinear systems including examples on swinging up an inverted pendulum with local stabilization at the endpoint and a tracking problem for a ball and beam system.
IEEE Transactions on Neural NetworksFinancial time series prediction using least squares support vector machines within the evidence framework
497 Citations2001Tony Van Gestel, Johan A. K. Suykens +6 more
The one step ahead prediction performances obtained on the prediction of the weekly 90-day T-bill rate and the daily DAX30 closing prices show that significant out of sample sign predictions can be made with respect to the Pesaran-Timmerman test statistic.
Journal of International EconomicsThe statistical properties of daily foreign exchange rates: 1974–1983
438 Citations1988David A. Hsieh
International Journal of ForecastingOn a threshold heteroscedastic model
296 Citations2005Cathy W. S. Chen, Mike K. P. So
Journal of Empirical FinanceAn artificial neural network-GARCH model for international stock return volatility
228 Citations1997Ronnie Donaldson, Mark J. Kamstra
In-sample and out-of-sample comparisons reveal that the ANN model captures volatility effects overlooked by GARCH, EGARCH and GJR models and produces out- of-sample volatility forecasts which encompass those from other models.
Expert Systems with ApplicationsImproving forecasts of GARCH family models with the artificial neural networks: An application to the daily returns in Istanbul Stock Exchange
166 Citations2008Melike Bildirici, Özgür Ömer Ersin
This study discussed the ARCH/GARCH family models and enhanced them with artificial neural networks to evaluate the volatility of daily returns for 23.10.1987-22.02.2008 period in Istanbul Stock Exchange and proposed ANN-APGarch model to increase the forecasting performance of APGARCH model.
Quantitative FinanceEstimating GARCH models using support vector machines*
103 Citations2003Fernando Pérez‐Cruz, Julio A. Afonso-Rodríguez +1 more
Support vector machines are a new nonparametric tool for regression estimation and it is shown that GARCH models can be estimated using SVMs and that such estimates have a higher predicting ability than those obtained via common ML methods.
Expert Systems with ApplicationsForecasting volatility based on wavelet support vector machine
65 Citations2008Lingbing Tang, Lingxiao Tang +1 more
A multidimensional wavelet kernel function is constructed and it is proved it meeting the mercer condition to address the problem of general kernel functions in support vector machine cannot capture the cluster feature of volatility accurately.
Chinese PhysicsChaotic time series prediction using least squares support vector machines
37 Citations2004Ye Meiying, Xiaodong Wang
The LS-SVM achieves higher generalization performance than traditional neural networks and provides an accurate chaotic time series prediction and has fast convergence.
Neural Computing and ApplicationsGARCH prediction using spline wavelet support vector machine
15 Citations2009Lingbing Tang, Huanye Sheng +1 more
A spline wavelet support vector machine (SWSVM) is proposed to forecast the volatility of financial time series based on generalized autoregressive conditional heteroscedasticity model and gains the cluster feature of volatility well.
SSRN Electronic JournalSupport Vector Regression Based GARCH Model with Application to Forecasting Volatility of Financial Returns
14 Citations2008Shiyi Chen, Kiho Jeong +1 more
The experiment shows that, under both varying and fixed forecasting schemes, the SVR-based GARCH outperforms the MA, the recurrent NN and the parametric GARCH based on the criteria of mean absolute error (MAE) and directional accuracy (DA).