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|Title:||Advancing Financial Market Volatility Measurement and Forecasting|
|Abstract:||This thesis proposes several new and useful financial econometric tools to facilitate risk analysis, portfolio choice and forecasting. This thesis starts with an introduction in Chapter 1. The research background, motivation and the structure of the thesis are illustrated in this chapter. Chapter 2 proposes a class of models that jointly model returns and ex-post variance measures under a Markov switching framework. Both univariate and multivariate return versions of the model are introduced. Estimation can be conducted under a fixed dimension state space or an infinite one. The proposed models can be seen as nonlinear common factor models subject to Markov switching and are able to exploit the information content in both returns and ex-post volatility measures. Applications to equity returns compare the proposed models to existing alternatives. The empirical results show that the joint models improve density forecasts for returns and point predictions of return variance. Using the information in ex-post volatility measures can increase the precision of parameter estimates, sharpen the inference on the latent state variable and improve portfolio decisions. Chapter 3 offers a new exact finite sample approach to estimating ex-post variance using Bayesian nonparametric methods. Until now ex-post variance estimation has been based on infill asymptotic assumptions that exploit high-frequency data. In contrast to the classical counterpart, the proposed method exploits pooling over high frequency observations with similar variances. Bayesian nonparametric variance estimators under no noise, heteroskedastic and serially correlated microstructure noise are introduced and discussed. Monte Carlo simulation results show that the proposed approach can increase the accuracy of variance estimation. Applications to equity data and comparison with realized variance and realized kernel estimators are included. Chapter 4 extends the third chapter to estimate the ex-post covariance matrix of asset returns from high-frequency data. As before, pooling is used to improve estimation accuracy and the method does not rely on infill asymptotic assumptions. In addition, the proposed covariance estimator is guaranteed to be positive definite. Furthermore, a new synchronization method of observations based on data augmentation is introduced. The Bayesian estimator is made robust to independent microstructure noise and nonsynchronous trading. According to Monte Carlo simulations, the new estimator is very competitive with existing estimators. Empirical applications evaluate the new estimator from an economic perspective. Finally, Chapter 5 concludes and summarizes the contribution of this thesis to the literature.|
|Appears in Collections:||Open Access Dissertations and Theses|
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