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Title: Forecasting realized covariance in an adaptive framework
Authors: LU JUN
Keywords: realized covariance,forecasting,adaptive procedure,semiparametric,VAR,structural break
Issue Date: 26-Jul-2012
Citation: LU JUN (2012-07-26). Forecasting realized covariance in an adaptive framework. ScholarBank@NUS Repository.
Abstract: Non-stationary financial time series with regime-switch-point inside are frequently encountered by researchers, which pose difficulty in accurately estimating the parameters when some parametric models are applied. The inaccuracy would create non-ignorable risk in financial risk management. Various adaptive regime-switch-point detection techniques have been developed to deal with the non-stationarity feature. However, existing approaches are largely based on univariate time series and the model set-up is rather restrictive. In this thesis, a new adaptive local model selection technique is developed which is based on multidimensional time series, namely, the realized covariance matrix process is modeled under the framework of adaptive local model selection. Together with the dimension reduction technique (common principal component analysis) and the local adaptive vector autoregressive (VAR) model, the combined approach generates satisfying model parameter estimation and realized covariance matrix forecasting results, as well as good financial risk metric measures when applied to simulated and real data set.
Appears in Collections:Ph.D Theses (Open)

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