Forecasting volatility based on wavelet support vector machine

Authors:
Ling-Bing Tang;Ling-Xiao Tang;Huan-Ye Sheng
Affiliations:
Computational Finance Laboratory, Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China and Department of Comp ...;School of Economics, Changsha University of Science and Technology, 45 Chiling Road, Tianxin District, Changsha 410076, China;Computational Finance Laboratory, Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 4
Cited 7

A Theory for Multiresolution Signal Decomposition: The Wavelet Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets

Ten lectures on wavelets
The nature of statistical learning theory

The nature of statistical learning theory
The wavelet transform, time-frequency localization and signal analysis

IEEE Transactions on Information Theory

An analysis of the yield spread as a predictor of inflation in Brazil: Evidence from a wavelets approach

Expert Systems with Applications: An International Journal
A hybrid-forecasting model based on Gaussian support vector machine and chaotic particle swarm optimization

Expert Systems with Applications: An International Journal
Forecasting industrial production in Brazil: Evidence from a wavelet approach

Expert Systems with Applications: An International Journal
Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings

Neurocomputing
Trading team composition for the intraday multistock market

Decision Support Systems
Heavy-tailed mixture GARCH volatility modeling and Value-at-Risk estimation

Expert Systems with Applications: An International Journal
Forecasting large scale conditional volatility and covariance using neural network on GPU

The Journal of Supercomputing

Quantified Score

Hi-index	12.06

Visualization

Abstract

One of the challenging problems in forecasting the conditional volatility of stock market returns is that general kernel functions in support vector machine (SVM) cannot capture the cluster feature of volatility accurately. While wavelet function yields features that describe of the volatility time series both at various locations and at varying time granularities, so this paper construct a multidimensional wavelet kernel function and prove it meeting the mercer condition to address this problem. The applicability and validity of wavelet support vector machine (WSVM) for volatility forecasting are confirmed through computer simulations and experiments on real-world stock data.