by Wai-chuen Tso. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 90-94). / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Prior Information --- p.5 / Chapter 2.1 --- Prior Distribution --- p.6 / Chapter 2.2 --- Quantification of Prior Belief --- p.10 / Chapter 2.2.1 --- Prior Belief --- p.10 / Chapter 2.2.2 --- Some Basic Concepts of Fuzzy Set Theory --- p.12 / Chapter 2.2.3 --- Quantification --- p.16 / Chapter 2.3 --- Specification and Determination of Model Parameters --- p.20 / Chapter 2.3.1 --- A Questionnaire --- p.21 / Chapter 2.3.2 --- Parameter Value --- p.22 / Chapter 2.3.3 --- Determination of Degree of Fuzziness --- p.23 / Chapter 2.4 --- Comments --- p.26 / Chapter 2.4.1 --- Interpretation of Time Length of Poisson Process --- p.26 / Chapter 2.4.2 --- Likelihood Interpretation of Membership Value --- p.28 / Chapter 2.4.3 --- Comparison with Existing Modeling --- p.30 / Chapter 2.4.4 --- Conclusion of Prior Information --- p.31 / Chapter 3 --- Posterior Analysis --- p.33 / Chapter 3.1 --- Posterior Analysis by Monte Carlo Method --- p.34 / Chapter 3.1.1 --- Monte Carlo Method --- p.34 / Chapter 3.1.2 --- Estimation of Posterior Mean and Posterior Variance of Log-odds Ratio --- p.35 / Chapter 3.1.3 --- Construction of Credible Region of Log-odds Ratio --- p.38 / Chapter 3.1.4 --- Estimation of Posterior Mean of Cell Probability --- p.41 / Chapter 3.2 --- Sampling of Prior Cell Frequency Vector by Gibbs Sampler --- p.42 / Chapter 3.2.1 --- Gibbs Sampler --- p.42 / Chapter 3.2.2 --- Two Sampling Algorithms --- p.45 / Chapter 3.2.3 --- Acceptance-Rejection Algorithm --- p.50 / Chapter 3.3 --- Some Practical Problems --- p.51 / Chapter 3.3.1 --- Number of Iterations in Gibbs Sampler --- p.51 / Chapter 3.3.2 --- Sample Size of Gibbs Sample --- p.53 / Chapter 4 --- Simulation Study --- p.58 / Chapter 4.1 --- Multinomial Model --- p.59 / Chapter 4.1.1 --- Determination of Number of Iterations --- p.61 / Chapter 4.1.2 --- Determination of Sample Size --- p.62 / Chapter 4.1.3 --- Posterior Estimation --- p.63 / Chapter 4.1.4 --- Sensitivity Analysis --- p.64 / Chapter 4.2 --- Poisson Model --- p.71 / Chapter 4.2.1 --- Determination of Number of Iterations --- p.72 / Chapter 4.2.2 --- Determination of Sample Size --- p.73 / Chapter 4.2.3 --- Posterior Estimation --- p.74 / Chapter 4.2.4 --- Sensitivity Analysis --- p.75 / Chapter 4.3 --- Conclusion --- p.82 / Chapter 5 --- Conclusions and Discussions --- p.85 / References --- p.90

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

有實證研究表明，傳統的信貸風險結構模型顯著低估了違約概率以及信貸收益率差。傳統的結構模型有三個可能的問題：1. 因為正態假設，布朗模型在模擬公司資產價值的過程中未能捕捉到極端事件2. 市場微觀結構噪聲扭曲了股票價格所包含信息3. 在到期日前任何時間，標準BS 期權理論方法不足以描述任何破產的可能性。這些問題在過去的文獻中曾分別提及。而在本文中，在不同的信用風險結構模型的基礎上，我們提出了貝葉斯方法去估算公司價值的跳躍擴散過程和微觀結構噪聲。因為企業的資產淨值不能在市場上觀察，本文建議的貝葉斯方法可對隱藏變量和泊松衝擊作出一定的估算，並就後驗分佈進行財務分析。我們應用馬爾可夫鏈蒙特卡羅方法（MCMC）和吉布斯採樣計算每個參數的後驗分佈。以上的做法，允許我們檢查結構性信用風險模型的偏差主要是來自公司價值的分佈、期權理論方法或市場微觀結構噪聲。我們進行模擬研究以確定模型的表現。最後，我們以新興市場的數據實踐我們的模型。 / There is empirical evidence that structural models of credit risk significantly underestimate both the probability of default and credit yield spreads. There are three potential sources of the problems in traditional structural models. First, the Brownian model driving the firm asset value process may fail to capture extreme events because of the normality assumption. Second, the market micro-structure noise in trading may distort the information contained in equity prices within the estimation process. Third, the standard Black and Scholes option-theoretic approach may be inadequate to describe the consequences of bankruptcy at any time before maturity. These potential problems have been handled separately in the literature. In this paper, we propose a Bayesian approach to simultaneously estimate the jump-diffusion firm value process and micro-structure noise from equity prices based on different structural credit risk models. As the firm asset value is not observable but the equity price is, the proposed Bayesian approach is useful in the estimation with hidden variable and Poisson shocks, and produces posterior distributions for financial analysis. We demonstrate the application using the Markov chain Monte Carlo (MCMC) method to obtain the posterior distributions of parameters and latent variable. The proposed approach enables us to check whether the bias of the structural credit risk model is mainly caused by the firm value distribution, the option-theoretic method or the micro-structure noise of the market. A simulation study is conducted to ascertain the performance of our model. We also apply our model to the emerging market data. / Detailed summary in vernacular field only. / Chan, Sau Lung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 62-65). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese. / List of Tables --- p.vii / List of Figures --- p.viii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background and Intuition --- p.5 / Chapter 2.1 --- Merton Model with Trading Noise --- p.7 / Chapter 2.2 --- Black-Cox Model with Default Barrier --- p.10 / Chapter 2.3 --- Double Exponential Jump Diffusion Model (KJD Model) --- p.11 / Chapter 2.4 --- Equity Value via Laplace Transforms --- p.13 / Chapter 2.5 --- KJD Model with Trading Noises --- p.15 / Chapter 3 --- Bayesian Analysis --- p.17 / Chapter 3.1 --- Gibbs Sampling and Metropolis-Hastings Method --- p.17 / Chapter 3.2 --- Merton Model with Trading Noises (M1) --- p.19 / Chapter 3.2.1 --- Prior Distribution for M1 --- p.19 / Chapter 3.2.2 --- Posterior Distribution for M1 --- p.20 / Chapter 3.3 --- Merton Model with Default Barrier (M2) --- p.22 / Chapter 3.3.1 --- Prior Distribution for M2 --- p.23 / Chapter 3.3.2 --- Posterior Distribution for M2 --- p.23 / Chapter 3.4 --- KJD Model with Trading Noises (M3) --- p.25 / Chapter 3.4.1 --- Prior Distribution for M3 --- p.26 / Chapter 3.4.2 --- Posterior Distribution for M3 --- p.27 / Chapter 3.5 --- KJD Model with Default Barrier (M4) --- p.33 / Chapter 3.5.1 --- Prior Distribution for M4 --- p.34 / Chapter 3.5.2 --- Posterior Distribution for M4 --- p.35 / Chapter 4 --- Numerical Examples --- p.42 / Chapter 4.1 --- Simulation Analysis --- p.42 / Chapter 4.2 --- Empirical Study --- p.46 / Chapter 4.2.1 --- BEA and DBS, 2003-2004 --- p.46 / Chapter 4.2.2 --- HSBC, 2008-2009 --- p.49 / Chapter 5 --- Conclusion --- p.60 / Bibliography --- p.62

"Classification systems change from census to census for a variety of reasons. The change from 1970 U.S Census to 1980 U.S Census classification was so dramatic that studying the changes and making comparisons are too complicated and expensive. Treating the actual census results as unknown, we simulated a new Census data base reflecting the real situation in 1970 & 1980 classification systems. One of our objective is to explain the process by which codes change so that the researchers can better understand how the new data bases were created. The second objective is to show how this newly created data base is then used to study the comparability of the two classification systems. In this project we do not attempt any estimative or predictive inference. We simply simulate the industry and occupation codes in the U.S. Census public-use samples via a model similar to the one used for multiple imputation."

Each year millions of dollars are wagered on the NFL during the season. A few people make some money, but most often the only real winner is the sports book. In this project, the effect of data transformation on the paired comparison model of Glickman and Stern (1998) is explored. Usual transformations such as logarithm and square-root are used as well as a transformation involving a threshold. The motivation for each of the transformations if to reduce the influence of blowouts on future predictions. Data from the 2003 and 2004 NFL seasons are examined to see if these transformations aid in improving model fit and prediction rate against a point spread. Strategies for model-based wagering are also explored.

Made available in DSpace on 2016-04-26T20:22:13Z (GMT). No. of bitstreams: 1 Nuria Lopez Cabaleiro Suarez.pdf: 945389 bytes, checksum: bb0c2eefbb638efa38c887e9d2a46bc2 (MD5) Previous issue date: 2013-10-30 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / This dissertation has its start point in the dichotomy pointed by Thomas Kuhn that places law always between the internal development of its own science (attending to the legal norm, auto reference) and attending social demands (will of Constitution; fundamental rights, theory of justice, in other words, hetero references). For this purpose, analyses in a historical the philosophical grounds of the thesis on legal interpretation with higher impact, to search for a paradigmatic solution, in line with the recent philosophy of science and with the models of subject after the linguisticpragmatic turn. Concludes for the adoption of the subject as the philosophical center of decision theory e presents its dynamic from the viewpoint of game theory / Esta dissertação parte da dicotomia apontada por Thomas Kuhn que coloca o direito entre o desenvolvimento interno de sua ciência (atendimento ao critério da norma jurídica positiva, autorreferência) e o atendimento às exigências sociais (vontade de Constituição, direitos fundamentais, teoria da justiça, ou seja, heterorreferências). Para tanto, analisa em um histórico as bases filosóficas das teses de maior repercussão sobre interpretação jurídica para buscar uma solução paradigmática em consonância com a recente filosofia da ciência e com os modelos de sujeito posteriores a reviravolta linguístico-pragmática. Conclui pela adoção do sujeito como matriz filosófica da teoria da decisão jurídica e apresenta a dinâmica de tal teoria a partir da teoria dos jogos

Thesis. 1976. Ph.D.--Massachusetts Institute of Technology. Dept. of Civil Engineering. / Microfiche copy available in Archives and Engineering. / Vita. / Bibliography: leaves 252-261. / by Mircea Grigoriu. / Ph.D.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING / Includes bibliographical references. / by Saul Brian Gelfand. / M.S.

Xin-Yuan Song. / "December 2000." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (p. 128-142). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Thesis. 1976. M.S.--Massachusetts Institute of Technology. Dept. of Civil Engineering. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Bibliography: leaves 134-138. / by Peter Kitanidis. / M.S.