Title :
Estimation of financial indices volatility using a model with time-varying parameters
Author :
Tobar, Felipe A. ; Orchard, Marcos E. ; Mandic, Danilo P. ; Constantinides, A.G.
Author_Institution :
Imperial Coll. London, London, UK
Abstract :
A class of stochastic volatility models (SVMs) with time-varying parameters is presented for online volatility estimation in nonstationary environments. This is achieved by modelling both the volatility and model parameters as states of a hidden Markov model (HMM), thus allowing for the use of particle filters to estimate the resulting posterior densities. The proposed models, based on the logarithmic SVM and the unobserved GARCH model, are evaluated for the estimation of the volatility of the NASDAQ-C and the Chilean IGPA financial indices between June 2007 and January 2010, where the late-2000s financial crisis is included. Simulations show that the proposed time-varying models are well suited for online volatility estimation as (i) they achieve an accuracy comparable to those of offline (batch) algorithms, and (ii) their parameters can be used to identify market changes.
Keywords :
autoregressive processes; financial data processing; hidden Markov models; particle filtering (numerical methods); stock markets; support vector machines; Chilean IGPA financial index; GARCH model; HMM; NASDAQ-C financial index; SVM; financial indices volatility estimation; hidden Markov model; logarithmic SVM; model parameters; particle filters; stochastic volatility models; support vector machines; time-varying parameters; Adaptation models; Data models; Estimation; Hidden Markov models; Mathematical model; Stochastic processes; Support vector machines;
Conference_Titel :
Computational Intelligence for Financial Engineering & Economics (CIFEr), 2104 IEEE Conference on
Conference_Location :
London
DOI :
10.1109/CIFEr.2014.6924090
