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n South African Journal of Industrial Engineering - A new multivariate nonlinear model to handle the volatility transmission

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Abstract

Price volatility of stocks is an important issue in stock markets. It should also be taken into account that the stochastic nature of volatility affects decision-makers' minds to a great extent. Therefore, predicting price volatility could help them make proper decisions. In this paper, a new multivariate fractionally integrated generalised autoregressive conditional heteroscedasticity (MFIGARCH) model is proposed to handle the price volatility in stocks. In this model, a long-term parameter is considered and estimated along with other parameters. In estimating the parameters of this nonlinear model, the maximum likelihood estimation method, which could be solved by standard econometric packages, is applied. However, these packages are no longer efficient when the size of the model increases. Thus meta-heuristic approaches, which stochastically seek optimal or near-optimal solutions, were used. In this paper, the well-known Particle Swarm Optimisation (PSO) meta-heuristic method is used for solving the suggested multivariate FIGARCH model. Hence the main objective of this paper is to introduce a new model for addressing the stock price volatility (i.e., the development of FIGARCH to create the MFIGARCH model) and to apply an efficient estimation method (i.e. PSO) for finding the parameters of the problem.

Die wisselvalligheid van die aandeelprys is 'n belangrike kwessie vir aandelemarkte. Die stogastiese aard van dié wisselvalligheid beïnvloed besluitnemers op 'n groot skaal en die voorspelling van die wisselvalligheid kan die besluitneming verbeter. 'n Nuwe, meerveranderlike gedeeltelik geïntegreerde veralgemeende outoregressiewe voorwaardelike heteroskedastisiteit (MFIGARCH) model word voorgestel om die wisselvalligheid in aandeelpryse te hanteer. In hierdie model word, onder andere, 'n langtermyn parameter oorweeg en beraam. Vir die beraming van parameters in hierdie nie-lineêre model word die maksimum waarskynlikheidsmetode (wat opgelos kan word deur middel van standaard ekonometriese pakkette) toegepas. Hierdie pakkette is egter oneffektief wanneer die model vergroot. Dus word meta-heuristiese, wat optimale of byna-optimale oplossings stogasties soek, ingespan. Die welbekende partikel swerm optimering metode word gebruik vir die oplos van die voorgestelde meerveranderlike model. Die hoof doel van hierdie studie is om die nuwe meerveranderlike model om die wisselvalligheid in aandeelprys aan te spreek, bekend te stel en om effektiewe parameter beraming deur middel van partikel swerm optimering toe te pas.

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/content/indeng/25/3/EJC165160
2014-11-01
2016-12-06
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