Robust estimation and resampling in financial time series models

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Volatility modelling has its special significance in the financial world as it is an indispensablefactor in quantifying risk, derivative pricing and so on. A reasonable model will provide accurateforecast of volatilities and is therefore of great importance to financial market participants and policymakinginstitutions. As a popular volatility model proposed by Bollerslev (1986), the generalizedautoregressive conditional heterscedasticity (GARCH) model has been studied by numbers ofresearchers. Since then, some extended models have also been proposed, e.g., IGARCH, GJR,TGARCH, and these models have enriched the prediction of volatility.AIM: A commonly used method for estimating the unknown parameters in GARCH-type modelsis quasi maximum likelihood estimation (QMLE), in which the errors are assumed to follow normaldistribution. However, studies have shown that heavy tails exist in error distributions for most of thefinancial time series that we come across in practice which makes QMLE an inappropriate approach.Therefore, there is a great need for developing more robust methods which work for heavy-taileddistribution and this is what I will investigate my PhD research.Specifically, let { } t X (t Z ) denote a series of observations for financial time series andconsider a general model that