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Title: Essays on Volatility Modeling and Forecasting
Keywords: volatiltity model,nonparametric estimation,semieparametric estimation
Issue Date: 30-Sep-2011
Source: ZHANG SHEN (2011-09-30). Essays on Volatility Modeling and Forecasting. ScholarBank@NUS Repository.
Abstract: This thesis consists of three essays and studies the modeling and forecasting of return volatility. The first essay investigates a new nonstationary nonparametric volatility model, in which the conditional variance of time series is modeled as a nonparametric function of an integrated or near-integrated covariate. This model can generate the long memory property in volatility and allow the nonstationarity in return series. We establish the asymptotic distribution theory for this model and show that it performs reasonably well in the empirical application. The second essay proposes a semiparametric volatility model which combines the nonparametric ARCH function with a persistent covariate. This new model applies the GARCH-X structure under the semiparametric framework, thus it can produce long-memory in volatility given the persistent property in the covariate. We show that it provides a better explanation of volatility in the empirical analysis. The last essay suggests a parametric volatility model and mainly focuses on the multi-step forecasting of volatility, we introduce a long-term component to the HEAVY models to capture the long-memory in volatility. We apply the high-frequency database to our model and other benchmark models and show that our model outperforms the other models.
Appears in Collections:Ph.D Theses (Open)

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