Su, Yiwen. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (p. 76-84). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Literature Review --- p.9 / Chapter 2.1 --- Hedge Ratio Review --- p.9 / Chapter 2.2 --- Estimating the Hedge Ratio --- p.13 / Chapter 2.2.1 --- Static Hedge Ratio --- p.13 / Chapter 2.2.2 --- "Dynamic Hedge Ratio, Multivariate GARCH Frame-work and DCC Model" --- p.14 / Chapter 3 --- Futures Market Efficiency --- p.19 / Chapter 3.1 --- Market Efficiency and Cointegration Test --- p.20 / Chapter 4 --- Model Specifications and Hedging Strategy --- p.24 / Chapter 4.1 --- Model Specifications --- p.24 / Chapter 4.1.1 --- BGARCH-DCC Model --- p.25 / Chapter 4.1.2 --- Symmetric BGARCH-DCC Model --- p.28 / Chapter 4.1.3 --- Asymmetric BGARCH-DCC Model --- p.31 / Chapter 4.2 --- Hedge Ratio --- p.33 / Chapter 4.2.1 --- MV Hedge Ratio --- p.34 / Chapter 4.2.2 --- Zero-VaR Hedge Ratio --- p.35 / Chapter 4.3 --- Evaluation of Hedge Effectiveness --- p.38 / Chapter 5 --- Data Description and Empirical Results --- p.39 / Chapter 5.1 --- Preliminary Data Analysis --- p.39 / Chapter 5.2 --- Estimation Results --- p.42 / Chapter 5.3 --- Dynamic Hedging Performance --- p.53 / Chapter 6 --- Conclusion --- p.68 / Chapter A --- Equation Derivation --- p.72 / Bibliography --- p.76

abstract: 中国商品期货市场经历30年发展，已初备协调资源分配、对冲经营风险的功能。但受产业自身和期货市场发展的制约，各期货品种市场有效性参差不齐。随着我国经济从增量阶段过渡到存量阶段，期货作为企业的价格管理和风险控制工具的重要性日益凸显，因此研究我国商品期货市场有效性具有非常好的现实意义。 本文开创性的从期货的基本功能——资源配置的角度出发，提出有效市场是指其期货价格能够对本行业社会资源起到合理的调配作用的市场。在内容安排上，本文首先总结了现有国际成熟期货品种的特点并找出能够反映期货对资源配置能力的四个指标假说，分别为期现回归性、利润波动性、库存波动性以及现金流变化，然后通过数学模型证明指标数据和品种成熟度的关联，最后应用该套指标对我国商品市场有效性进行检验。数学方法上，本文先采用Bai-Perron内生多重结构突变模型对时间序列进行突变点检验，然后对断点时间序列分别进行多元回归，并在剔除季节性和周期性后，通过平稳性检验、ARCH效应检验结果来确定相应的Garch模型，并用Garch模型来描述时间序列的波动性。 通过数学验证，我们认为期现回归性、利润波动性、库存波动性以及现金流变化这四个指标可以作为反映期货成熟度的检验指标，用该套方法对国内部分活跃品种检验后发现大连豆粕期货已经具备成熟品种的特征，本文认为豆粕期货市场是有效的；PTA、玉米淀粉期货的四个检验指标在近年来表现出时间序列优化的特点，但因时间较短尚不稳定，可以认为是接近成熟的品种；而螺纹钢和铝期货在多数指标上表现不佳，表明他们对社会资源配置能力较差，因此本文认为螺纹钢和铝期货市场是活跃但非有效的。通过进一步分析，本文认为品种的期现回归性差是制约其资源配置能力发挥的关键因素，而交易标的不明确、 仓单制作难度大、产业参与度低以及期货设计中的其他限制因素又是导致期现回归性差的重要原因。 / Dissertation/Thesis / Doctoral Dissertation Business Administration 2019

In this dissertation, three essays are presented that apply recent advances in time-series methods to the analysis of inflation and stock market index data for Taiwan. Specifically, ARCH and GARCH methodologies are used to investigate claims of increased volatility in economic time-series data since 1980. In the first essay, analysis that accounts for structural change reveals that the fundamental relationship between inflation and its variability was severed by policies implemented during economic liberalization in Taiwan in the early 1980s. Furthermore, if residuals are corrected for serial correlation, evidence in favor of ARCH effects is weakened. In the second essay, dynamic linkages between daily stock returns and daily trading volume are explored. Both linear and nonlinear dependence are evaluated using Granger causality tests and GARCH modelling. Results suggest significant unidirectional Granger causality from stock returns to trading volume. In the third essay, comparative analysis of the frequency structure of the Taiwan stock index data is conducted using daily, weekly, and monthly data. Results demonstrate that the relationship between mean return and its conditional standard deviation is positive and significant only for high-frequency daily data.

This work aims to describe the method of recursive estimation of time series with conditional volatility, used mainly in finance. First, there are described the basic types of models with conditional heteroskedasticity (GARCH) and princi- ples of state-space modeling demonstrated by means of linear models AR and ARMA. Subsequently, there are derived algorithms for recursive estimation of parameters of the GARCH model and its possible modifications including the ones for which recursive estimation formulas have not been yet derived in lit- erature. These algorithms are tested in a simulation study, where their appli- cability in practice is investigated. Finally, we apply these algorithms to real high-frequency data from the stock exchange. The practical part is done us- ing the software Mathematica 11.3. The work also serves as an overview of the current state of online modeling of financial time series. 1

Real estate is commonly viewed as a good diversification tool since the real estate market cycle exhibit low correlations to other asset classes. Moreover, Real Estate Investment Trusts (REITs) have become increasingly popular in the past decades since this investment form offers private investors a convenient way of diversifying stock portfolios with real estate. Some studies investigating the within-country diversification potential of REITs and stocks have been performed. These studies generally suggest poor diversification potential. Hence, we investigate the international diversification potential of REITs from Europe, Asia Pacific and the US for private investors holding European stocks from 2007 to 2019. For Europe and Asia Pacific, REIT markets with different maturity levels are included since emerging and developed REIT markets might have different characteristics affecting the diversification potential. We also examine which market leads which in terms of changes in returns. Moreover, the diversification potential of REITs may depend on the investment horizon, hence the long- and short-run perspectives for private investors are examined. The lesson learned from the Global Financial Crises and European Debt Crisis is that abnormal market conditions may change the behavior of assets on the financial markets, and significantly affect portfolio behavior. Hence, diversification potential in relation to crises is also considered. The methods employed are Johansen’s cointegration, Granger non-causality and DCC-GARCH. Our findings suggest long- and short-run diversification potential of international REITs for European stocks. Cross-regional combinations of REITs and stocks generally offer better diversification potential than within-regional combinations, and emerging REIT markets are preferred over their developed counterparts due to lower conditional correlations. Moreover, changes in stock market returns lead changes in REIT market returns, indicating that stock markets react more quickly to new information on the market. Long- and short-run diversification potential still exists during the crises although increased conditional correlations suggest higher interdependence in this period. However, there is no trend of increasing conditional correlations over the whole sample, suggesting the abnormal market conditions during the financial turmoil did not permanently change the diversification potential of REITs in stock portfolios.

The modelling of stock market volatility is considered to be important for practitioners and academics in finance due to its use in forecasting aspects of future returns. The GARCH class models have now firmly established themselves as one of the foremost techniques for modelling volatility in financial markets. The application of GARCH class models in developed and emerging markets (including the Egyptian Stock Market) provides evidence of GARCH effects in stock returns. However, most of the studies conducted on modelling the volatility of stock returns are based on the aggregated market index. This thesis argues that this will not reflect significant differences of variation in the pattern of volatility associated with different stocks. However, in order to examine the similarities and differences between the conditional variance structures of stocks from the same or different industries in the same equity market, this thesis estimates pooled-panel models. These novel models are used to test for similarities and differences in the conditional variance equation in panels of time series within a general to specific framework of nested tests. This is done using panel samples of sector indices and stocks from the Egyptian Stock Market covering the period from 1997 to 2002. The results suggest that there are similarities in the temporal volatility structures of stocks from the same sector or industry, but there are significant differences in the temporal volatility structures of stocks from different sectors or industries. This suggests that using indices alone for modelling the volatility of an equity market, which is the method used in the majority of studies cited in the literature, may not be appropriate. The thesis concludes with a discussion of some of the implications of these results and suggestions for further research. / Doctor of Philosophy (PhD)

本篇論文主要在檢視Fong與See (2001) 所提出的假說，將其應用於道瓊歐洲石油天然氣指數期貨 (DJ EURO STOXX OIL & GAS Index Futures) 上，是否能得到相同的驗證。 　　在是否加入狀態轉換考量的檢定中，本文採用AIC與BIC準則為判斷的基準，而由於雙狀態下BIC準則易有樣本參數過大的懲罰特性，因此其中又以AIC為較佳判斷的準則。研究結果顯示，有考量狀態轉換的Regime Switching GARCH模型配適度會較無考量狀態轉換的GARCH模型為佳。而在納入狀態轉換的考量下，在Regime Switching GARCH模型及其相關衍生模型的比較中，主要是採用RS-GARCH(1,1)-N，RS-GARCH(1,1)-t以及RS-ARCH(1,1)-t模型作為比較。這裡同樣以AIC與BIC準則為判斷的基準，研究結果顯示，在三模型中，是以RS-GARCH(1,1)-t模型具有最佳的配適度。 　　在預測能力的檢定中，本研究是利用MSE、MAE與R2，來判斷何者具有較佳的解釋能力，並且以DM檢定來進一步驗證。研究結果顯示，在有考量狀態轉換的Regime Switching GARCH模型與無考量狀態轉換的GARCH模型中，是以有考量狀態轉換的Regime Switching GARCH模型具有較佳的預測能力；而在RS-GARCH(1,1)-N，RS-GARCH(1,1)-t以及RS-ARCH(1,1)-t三種衍生模型的比較中，又以同時考量t分配以及有狀態轉換的RS-GARCH(1,1)-t模型具有較佳的預測能力。

Abstract This thesis considers some linear and nonlinear time series models. In the linear case, the analysis of a large number of short time series generated by a first order autoregressive type model is considered. The conditional and exact maximum likelihood procedures are developed to estimate parameters. Simulation results are presented and compare the bias and the mean square errors of the parameter estimates. In Chapter 3, five important nonlinear models are considered and their time series properties are discussed. The estimating function approach for nonlinear models is developed in detail in Chapter 4 and examples are added to illustrate the theory. A simulation study is carried out to examine the finite sample behavior of these proposed estimates based on the estimating functions.

<p>The objective for this master thesis is to investigate the possibility to predict the risk of stocks in financial markets. The data used for model estimation has been gathered from different branches and different European countries. The four data series that are used in the estimation are price series from: Münchner Rück, Suez-Lyonnaise des Eaux, Volkswagen and OMX, a Swedish stock index. The risk prediction is done with univariate GARCH models. GARCH models are estimated and validated for these four data series. </p><p>Conclusions are drawn regarding different GARCH models, their numbers of lags and distributions. The model that performs best, out-of-sample, is the APARCH model but the standard GARCH is also a good choice. The use of non-normal distributions is not clearly supported. The result from this master thesis could be used in option pricing, hedging strategies and portfolio selection.</p>

This thesis consists of four essays, three in the field of random effects models and one in the field of GARCH. The first essay in this thesis, ''Maximum likelihood based inference in the two-way random effects model with serially correlated time effects'', considers maximum likelihood estimation and inference in the two-way random effects model with serial correlation. We derive a straightforward maximum likelihood estimator when the time-specific component follow an AR(1) or MA(1) process. The estimator is also easily generalized to allow for arbitrary stationary and strictly invertible ARMA processes. In addition we consider the model selection problem and derive tests of the null hypothesis of no serial correlation as well as tests for discriminating between the AR(1) and MA(1) specifications. A Monte-Carlo experiment evaluates the finite-sample properties of the estimators, test-statistics and model selection procedures. The second essay, ''Asymptotic properties of the maximum likelihood estimator of random effects models with serial correlation'', considers the large sample behavior of the maximum likelihood estimator of random effects models with serial correlation in the form of AR(1) for the idiosyncratic or time-specific error component. Consistent estimation and asymptotic normality is established for a comprehensive specification which nests these models as well as all commonly used random effects models. The third essay, ''Specification and estimation of random effects models with serial correlation of general form'', is also concerned with maximum likelihood based inference in random effects models with serial correlation. Allowing for individual effects we introduce serial correlation of general form in the time effects as well as the idiosyncratic errors. A straightforward maximum likelihood estimator is derived and a coherent model selection strategy is suggested for determining the orders of serial correlation as well as the importance of time or individual effects. The methods are applied to the estimation of a production function using a sample of 72 Japanese chemical firms observed during 1968-1987. The fourth essay, entitled ''A simple efficient GMM estimator of GARCH models'', considers efficient GMM based estimation of GARCH models. Sufficient conditions for the estimator to be consistent and asymptotically normal are established for the GARCH(1,1) conditional variance process. In addition efficiency results are obtained for a GARCH(1,1) model where the conditional variance is allowed to enter the mean as well. That is, the GARCH(1,1)-M model. An application to the returns to the SP500 index illustrates. / <p>Diss. Stockholm : Handelshögskolan, 2001</p>