Estimação indireta de modelos R-GARCH / Indirect inference of R-GARCH models

AUTOR(ES)

Jhames Matos Sampaio

FONTE

IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia

DATA DE PUBLICAÇÃO

01/03/2012

RESUMO

Linear processes do not capture the structure of financial data. There is a large variety of nonlinear models available in literature. The class of ARCH models (Autoregressive Conditional Heterokedastic) was introduced by Engle (1982) in order to estimate inflation\ s variance. The idea is that, in this class, returns are serially uncorrelated, but the volatility (conditional variance) depends on past returns. The class of GARCH models (Generalized Autoregressive Conditional Heterokedastic) suggested by Bollerslev (1986, 1987, 1988) can be used to describe the volatility with less parameters than ARCH-type models. GARCH-type models are nonlinear stochastic processes, their distribution are heavy-tailed with time-dependent conditional variance model and they model clustering of volatility. Despite the reasonable description, the way that GARCH models are built imposes limits on the heaviness of the tails of their unconditional distribution. Many studies in financial data point to considerable heaviness of the tails. The class of Randomized Generalized Autoregressive Conditional Heterokedastic (R-GARCH) were proposed by Nowicka (1998) and include the ARCH and GARCH models allowing the use of stable innovations in place of normal distribution. This distribution allows to capture the heaviness tail property. As the autocovariance function does not exist for these processes a new measure of dependence was introduced. Estimation methods and empirical analysis of R-GARCH class, as well as their measures of dependence are not available in literature and are the focus of this work.

ASSUNTO(S)

distribuicoes estáveis finanças. finance indirect inference inferência indireta r-garch r-garch rs-garch rs-garch rt-garch rt-garch séries temporais stable distributions time series

Documentos Relacionados

• IDENTIFICAÇÃO DE MODELOS GARCH USANDO INTELIGÊNCIA COMPUTACIONAL
• Modelos de memória longa, GARCH e GARCH com memória longa para séries financeiras
• Estimação de volatilidade em séries financeiras : modelos aditivos semi-paramétricos e GARCH
• Modelagem de volatilidade via modelos GARCH com erros assimétricos: abordagem Bayesiana
• EVOLUTIONARY INFERENCE APPROACHES FOR ADAPTIVE MODELS