本文由三篇文章構成。首篇是關於多維變或然分佈預測的檢驗。第三篇是關於非貝斯結構性轉變的VAR 模型。或然分佈預測的檢驗是基於檢驗PIT(probability integral transformation) 序的均勻份佈性質與獨性質。第一篇文章基於Clements and Smith (2002) 的方法提出新的位置正變換。這新的變換改善原有的對稱問題，以及提高檢驗的power。第二篇文章建對於多變或然分佈預測的data-driven smooth 檢驗。通過蒙特卡模擬，本文驗證這種方法在小樣本下的有效性。在此之前，由於高維模型的複雜性，大部分的研究止於二維模型。我們在文中提出有效的方法把多維變換至單變。蒙特卡模擬實驗，以及在組融據的應用中，都證實這種方法的優勢。最後一篇文章提出非貝斯結構性轉變的VAR 模型。在此之前，Chib(1998) 建的貝斯結構性轉變模型須要預先假定構性轉變的目。因此他的方法須要比較同構性轉變目模型的優。而本文提出的stick-breaking 先驗概，可以使構性轉變目在估計中一同估計出。因此我們的方法具有robust 之性質。通過蒙特卡模擬，我們考察存在著四個構性轉變的autoregressive VAR(2) 模型。結果顯示我們的方法能準確地估計出構性轉變的發生位置。而模型中的65 個估計都十分接近真實值。我們把這方法應用在多個對沖基回報序。驗測出的構性轉變位置與市場大跌的時段十分吻合。 / This thesis consists of three essays on financial econometrics. The first two essays are about multivariate density forecast evaluations. The third essay is on nonparametric Bayesian change-point VAR model. We develop a method for multivariate density forecast evaluations. The density forecast evaluation is based on checking uniformity and independence conditions of the probability integral transformation of the observed series in question. In the first essay, we propose a new method which is a location-adjusted version of Clements and Smith (2002) that corrects asymmetry problem and increases testing power. In the second essay, we develop a data-driven smooth test for multivariate density forecast evaluation and show some evidences on its finite sample performance using Monte Carlo simulations. Previous to our study, most of the works are up to bivariate model as it is difficult to evaluate with the existing methods. We propose an efficient dimensional reduction approach to reduce the dimension of multivariate density evaluation to a univariate one. We perform various Monte Carlo simulations and two applications on financial asset returns which show that our test performs well. The last essay proposes a nonparametric extension to existing Bayesian change-point model in a multivariate setting. Previous change-point model of Chib (1998) requires specification of the number of change points a priori. Hence a posterior model comparison is needed for di erent change-point models. We introduce the stick-breaking prior to the change-point process that allows us to endogenize the number of change points into the estimation procedure. Hence, the number of change points is simultaneously determined with other unknown parameters. Therefore our model is robust to model specification. We preform a Monte Carlo simulation of bivariate vector autoregressive VAR(2) process which is subject to four structural breaks. Our model estimate the break locations with high accuracy and the posterior estimates of the 65 parameters are closed to the true values. We apply our model to various hedge fund return processes and the detected change points coincide with market crashes. / Detailed summary in vernacular field only. / Ko, Iat Meng. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 176-194). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Multivariate Density Forecast Evaluation: A Modified Approach --- p.7 / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Evaluating Density Forecasts --- p.13 / Chapter 2.3 --- Monte Carlo Simulations --- p.18 / Chapter 2.3.1 --- Bivariate normal distribution --- p.19 / Chapter 2.3.2 --- The Ramberg distribution --- p.21 / Chapter 2.3.3 --- Student’s t and uniform distributions --- p.24 / Chapter 2.4 --- Empirical Applications --- p.24 / Chapter 2.4.1 --- AR model --- p.25 / Chapter 2.4.2 --- GARCH model --- p.27 / Chapter 2.5 --- Conclusion --- p.29 / Chapter 3 --- Multivariate Density Forecast Evaluation: Smooth Test Approach --- p.39 / Chapter 3.1 --- Introduction --- p.39 / Chapter 3.2 --- Exponential Transformation for Multi-dimension Reduction --- p.47 / Chapter 3.3 --- The Smooth Test --- p.56 / Chapter 3.4 --- The Data-Driven Smooth Test Statistic --- p.66 / Chapter 3.4.1 --- Selection of K --- p.66 / Chapter 3.4.2 --- Choosing p of the Portmanteau based test --- p.69 / Chapter 3.5 --- Monte Carlo Simulations --- p.70 / Chapter 3.5.1 --- Multivariate normal and Student’s t distributions --- p.71 / Chapter 3.5.2 --- VAR(1) model --- p.74 / Chapter 3.5.3 --- Multivariate GARCH(1,1) Model --- p.78 / Chapter 3.6 --- Density Forecast Evaluation of the DCC-GARCH Model in Density Forecast of Spot-Future returns and International Equity Markets --- p.80 / Chapter 3.7 --- Conclusion --- p.87 / Chapter 4 --- Stick-Breaking Bayesian Change-Point VAR Model with Stochastic Search Variable Selection --- p.111 / Chapter 4.1 --- Introduction --- p.111 / Chapter 4.2 --- The Bayesian Change-Point VAR Model --- p.116 / Chapter 4.3 --- The Stick-breaking Process Prior --- p.120 / Chapter 4.4 --- Stochastic Search Variable Selection (SSVS) --- p.121 / Chapter 4.4.1 --- Priors on Φ[subscript j] = vec(Φ[subscript j]) = --- p.122 / Chapter 4.4.2 --- Prior on Σ[subscript j] --- p.123 / Chapter 4.5 --- The Gibbs Sampler and a Monte Carlo Simulation --- p.123 / Chapter 4.5.1 --- The posteriors of ΦΣ[subscript j] and Σ[subscript j] --- p.123 / Chapter 4.5.2 --- MCMC Inference for SB Change-Point Model: A Gibbs Sampler --- p.126 / Chapter 4.5.3 --- A Monte Carlo Experiment --- p.128 / Chapter 4.6 --- Application to Daily Hedge Fund Return --- p.130 / Chapter 4.6.1 --- Hedge Funds Composite Indices --- p.132 / Chapter 4.6.2 --- Single Strategy Hedge Funds Indices --- p.135 / Chapter 4.7 --- Conclusion --- p.138 / Chapter A --- Derivation and Proof --- p.166 / Chapter A.1 --- Derivation of the distribution of (Z₁ - EZ₁) x (Z₂ - EZ₂) --- p.166 / Chapter A.2 --- Derivation of limiting distribution of the smooth test statistic without parameter estimation uncertainty ( θ = θ₀) --- p.168 / Chapter A.3 --- Proof of Theorem 2 --- p.170 / Chapter A.4 --- Proof of Theorem 3 --- p.172 / Chapter A.5 --- Proof of Theorem 4 --- p.174 / Chapter A.6 --- Proof of Theorem 5 --- p.175 / Bibliography --- p.176
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_328180 |
| Date | January 2013 |
| Contributors | Ko, Iat Meng., Chinese University of Hong Kong Graduate School. Division of Economics. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | electronic resource, electronic resource, remote, 1 online resource (xviii, 194 leaves) : ill. (some col.) |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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