Comparação da capacidade preditiva de modelos heterocedásticos através da estimação do value-at-risk

• Autor(a) principal: Amaro, Raphael Silveira
• Data de Publicação: 2016
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Manancial - Repositório Digital da UFSM
• Texto Completo: http://repositorio.ufsm.br/handle/1/4791
• topic: Value-at-risk; Modelos heterocedásticos; Distribuições de probabilidade; Capacidade preditiva; Value-at-risk; Heteroskedastic models; Probability distributions; Predictive ability; CNPQ::CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO

In an increasingly competitive economic environment, as in the current global context, risk management becomes essential for the survival of companies and investment portfolio managers. Both companies and managers need to have a model that can be able to quantify the risks inherent in their investments in the best possible way in order to guide them in making decisions to get the highest expected return on their investments. Currently, there are several heterogeneous models which seek to quantify risk, making the choice of a particular model very complex. In order to confront and find models that can serve, efficiently, to the quantification of risk, the objective of this research is to compare the predictive ability of five models of conditional heteroskedasticity by estimating the Value-at-Risk, assuming eight different statistical probability distributions, for the series of financial ratios of the capital market of the five largest emerging countries: Brazil, Russia, India, China and South Africa, in the period between February 26, 2001 and December 31, 2015. For this goal was achieved, were held predictions of Value-at-Risk for 50 steps ahead, for all competing models in the study, with adjustment of parameters at every step. Since all the forecasts have been computed for every steps forward, it was possible to compare predictive ability of competing models studied by means of some loss functions. The evidences suggests that heterocedastic Component GARCH is preferable, to make predictions of Value-at-Risk, to all other competing models, however the distribution of statistical probability that this model uses interferes too much in the results of forecasts obtained by it. The data for each financial index studied showed to adapt themselves to a particular different type of probability density function, not reflecting a distribution which can be considered superior to all other. Thus, the results do not provide a single and ideal tool for use in the risk measurement, of generalized form, for all capital markets of emerging countries studied, only provide specific tools to be used in each financial index individually. The results found can be used for the purposes previously described or to elaborate statistical formulas that combine different models estimated in order to get better volatilities forecast measures so that it can measure, more precisely, the market risks.