Non-parametric estimation of a multiscale CHARN model using SVR

Abstract

The present paper studies the non-parametric estimation of volatility in financial time series. Support Vector Regression (SVR) is applied and compared with alternative techniques for estimating a Conditional Heteroskedastic AutoRegressive Nonlinear (CHARN) model. A multiscale decomposition analysis decomposes a time series into several scales, and then several separated SVRs, equal to the number of scales, are fed for running the CHARN model by scaled decomposed series. A multiscale CHARN model, which is an incorporation of wavelet decomposition analysis with a CHARN model estimated by SVR, is proposed and investigated using real-world data sets. To exploit the locality advantage of wavelets, we use wavelet analysis and combine it with SVR. Wavelets are able to economically describe phenomena that are heterogeneous. Recent studies on heterogeneous agents in financial markets theoretically support advocated scale-based analysis of markets, and, of course, the multiscale CHARN model proposition, since it is believed that each scale corresponds to each heterogeneous agent who acts differently in the market. Our objective is to improve the accuracy of estimation by applying multiscale decomposition. The results of applying SVR to estimate the CHARN model in a single resolution on real-world time series indicate that it outperforms the technical benchmarks. Also, the multiscale CHARN model estimated by SVR is more accurate than a single scale model.