She, Yingni. / "August 2011." / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 43-49). / Abstracts in English and Chinese. / Chapter 1. --- Introduction --- p.1 / Chapter 2. --- Literature Review --- p.6 / Chapter 2.1 --- Investor Sentiment Measures --- p.6 / Chapter 2.2 --- Chinese Stock Market Overview --- p.13 / Chapter 3. --- Chinese Investor Sentiment Measure --- p.16 / Chapter 3.1 --- Data and Variables --- p.16 / Chapter 3.2 --- Methodology --- p.21 / Chapter 3.3 --- Empirical Results --- p.22 / Chapter 3.4 --- Investor Sentiment Behavior --- p.24 / Chapter 4. --- Threshold Autoregressive Model --- p.29 / Chapter 4.1 --- Methodology --- p.29 / Chapter 4.2 --- Estimated Results --- p.31 / Chapter 4.3 --- Forecasting Performance --- p.36 / Chapter 4.4 --- Trading Strategy --- p.38 / Chapter 5. --- Conclusion --- p.41 / References --- p.43

A major objective of this thesis is to study the statistical inference problem for GARCH-type models, including fractionally-integrated (FI) GARCH, fractional (F) GARCH, long-memory (LM) GARCH, and non-stationary GARCH models. / Among various types of generalizations to the ARCH models, fractionally-integrated (FI) GARCH model proposed in Baillie et al. (1996) and Bollerslev and Mikkelson (1996) is one of the most interesting ones as it offered many challenging theretical problems. / Parameters in the ARCH-type models are commonly estimated using the quasi-maximum likelihood estimator (QMLE). To establish consistency and asymptotic normality of the QMLE, one usually has to impose stringent assumptions, see Robinson and Zaffaroni (2006) and Straumann (2005). They have to assume that a stationary solution to the true model exists and this solution has some finite moments. These two assumptions are too restrictive to be applied to FIGARCH models. Formal results of the asymptotic properties of the QMLE of the FIGARCH models are still not available. Progresses on asymptotic theory of QMLE have only been made on certain models that resemble the FIGARCH model, including the FGARCH model of Ding and Granger (1996) and Robinson and Zaffaroni (2006), the LM-GARCH model of Robinson and Zaffaroni (1997) and the non-stationary ARCH model, but not the FIGARCH model itself. / This study attempts to solve the FIGARCH problem and extend the current findings on FGARCH, LM-GARCH and non-stationary GARCH models. We show that if the fractional parameter d is known, the QMLE for the parameters are strongly consistent and asymptotically normal. The results of LM-GARCH (0, d, 0) model in Konlikov (2003a,b) will be generalized to encompass the LM-GARCH(p, d, q) models. We also furnish a general result for non-stationary GARCH (p, q) models, extending the results of Jensen and Rahbek (2004) on weak consistency and asymptotic normality of the QMLE of the non-stationary GARCH (1, 1) models. / Ng, Chi Tim. / "June 2007." / Adviser: Chan Ngai Hang. / Source: Dissertation Abstracts International, Volume: 69-01, Section: B, page: 0398. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references. / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

by Gilbert Chiu-sing Lui. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 112-117). / Chapter 1. --- Introduction --- p.1 / Chapter 2. --- Models and Assumptions --- p.4 / Chapter 3. --- Asymptotics of FM-SEA Estimators --- p.15 / Chapter 3.1. --- Model without Determinstic Trends --- p.15 / Chapter 3.2. --- Model with Determinstic Trends --- p.27 / Chapter 4. --- Asymptotics of FM-SEA Estimators of VAR System --- p.33 / Chapter 4.1. --- General Model --- p.33 / Chapter 4.2. --- Model with d = 4 --- p.44 / Chapter 5. --- Monte Carlo Experimental Results --- p.49 / Chapter 6. --- Conclusion --- p.54 / Chapter 7. --- Mathematical Appendix --- p.56 / Chapter 8. --- References --- p.112

The reliability of monetary policy as an economic stabilisation tool depends on the understanding of the empirical effects of policy intervention on macroeconomic aggregates. Since investigating the interdependencies between macroeconomic variables necessarily involves studying their interactions over time, time series analysis is an important tool. This thesis sets out to examine the presence and effects of nonstationarity in the form of a structural break in a basic VAR of four endogenous variables. Specifically, the transmission of a monetary policy shock on the macroeconomic aggregate of 11 Euro Area countries is estimated for the period 1999–2017, employing variables based on previous studies. A Quandt-Andrews breakpoint test is used to identify the break date, and a comparison is made between the periods. This study finds support for the presence of a break in the regression estimate from the breakpoint test, although the reults from the IRFs cannot be shown to be statistically significant, nor to be bias-free.

Since the Euro has founded in 1999, the Asian Currency has become an important issue. The most important prerequisite for adopting common currency for the countries in the area is the synchronization of business cycle. This paper analyses the degree and responses of business cycles for Asian countries when they face to the common shocks. The empirical findings from this paper can be summarized as follows. First, the shocks of Japanese economy are more important to Asian countries than the shocks from the United States, except for Thailand and Indonesia. Second, Malaysia and the Philippine are substantially influenced by the Thailand. Therefore, the Asian economy is evidently forming its regional (or bloc) economy continually. The findings from this paper are in the same line with the result from Hazel (2001), who concludes the business cycles of Japan and Korea are commoved. The degree of synchronization of business cycles for Thailand, Malaysia, and the Philippine are quite high as well.

In this thesis, we propose a novel trust management scheme for improving routing reliability in wireless ad hoc networks. It is grounded on two classic autoregression models, namely Autoregressive (AR) model and Autoregressive with exogenous inputs (ARX) model. According to this scheme, a node periodically measures the packet forwarding ratio of its every neighbor as the trust observation about that neighbor. These measurements constitute a time series of data. The node has such a time series for each neighbor. By applying an autoregression model to these time series, it predicts the neighbors future packet forwarding ratios as their trust estimates, which in turn facilitate it to make intelligent routing decisions. With an AR model being applied, the node only uses its own observations for prediction; with an ARX model, it will also take into account recommendations from other neighbors. We evaluate the performance of the scheme when an AR, ARX or Bayesian model is used. Simulation results indicate that the ARX model is the best choice in terms of accuracy.

In this thesis, we propose a novel trust management scheme for improving routing reliability in wireless ad hoc networks. It is grounded on two classic autoregression models, namely Autoregressive (AR) model and Autoregressive with exogenous inputs (ARX) model. According to this scheme, a node periodically measures the packet forwarding ratio of its every neighbor as the trust observation about that neighbor. These measurements constitute a time series of data. The node has such a time series for each neighbor. By applying an autoregression model to these time series, it predicts the neighbors future packet forwarding ratios as their trust estimates, which in turn facilitate it to make intelligent routing decisions. With an AR model being applied, the node only uses its own observations for prediction; with an ARX model, it will also take into account recommendations from other neighbors. We evaluate the performance of the scheme when an AR, ARX or Bayesian model is used. Simulation results indicate that the ARX model is the best choice in terms of accuracy.

<p>This dissertation presents a quantitative study on the relationship between financial intermediation and the macroeconomy of the United States. It consists of two major chapters, with the first chapter studying adverse shocks to interbank market lending, and with the second chapter studying a theoretical model where aggregate balance sheets of the financial and non-financial sectors play a key role in financial intermediation frictions.</p><p>In the first chapter, I empirically investigate a novel macroeconomic shock: the funding liquidity shock. Funding liquidity is defined as the ability of a (financial) institution to raise cash at short notice, with interbank market loans being a very common source of short-term external funding. Using the "TED spread" as a proxy of aggregate funding liquidity for the period from 1971M1 to 2009M9, I first discover that, by using the vector-autoregression approach, an unanticipated adverse TED shock brings significant recessionary effects: industrial production and prices fall, and the unemployment rate rises. The contraction lasts for about twenty months. I also recover the conventional monetary policy shock, the macro impact of which is in line with the results of Christiano et al (1998) and Christiano et al (2005) . I then follow the factor model approach and find that the excess returns of small-firm portfolios are more negatively impacted by an adverse funding liquidity shock. I also present evidence that this shock as a "risk factor" is priced in the cross-section of equity returns. Moreover, a proposed factor model which includes the structural funding liquidity and monetary policy shocks as factors is able to explain the cross-sectional returns of portfolios sorted on size and book-to-market ratio as well as the Fama and French (1993) three-factor model does. Lastly, I present empirical evidence that funding liquidity and market liquidity mutually affect each other.</p><p>I start the second chapter by showing that, in U.S. data, the balance sheet health of the financial sector, as measured by its equity capital and debt level, is a leading indicator of the balance sheet health of the nonfinancial sector. This fact, and the apparent role of the financial sector in the recent global financial crisis, motivate a general equilibrium macroeconomic model featuring the balance sheets of both sectors. I estimate and study a model within the "loanable funds" framework of Holmstrom and Tirole (1997), which introduces a double moral hazard problem in the financial intermediation process. I find that financial frictions modeled within this framework give rise to a shock transmission mechanism quantitatively different from the one that arises with the conventional modeling assumption, in New Keynesian business cycle models, of convex investment adjustment costs. Financial equity capital plays an important role in determining the depth and persistence of declines in output and investment due to negative shocks to the economy. Moreover, I find that shocks to the financial intermediation process cause persistent recessions, and that these shocks explain a significant portion of the variation in investment. The estimated model is also able to replicate some aspects of the cross-correlation structure of the balance sheet variables of the two sectors.</p> / Dissertation

To resolve the inflation caused by overheated economy in 1992, China executed Macroscopic Control Policy to stabilize the fluctuation of price standard in 1993. It seemed to achieve the effort of controlling inflation. However, because of longtime Macroscopic Control Policy after Asian Financial Crisis, it resulted in negative impacts. CPI in China has been minus quantity for 39 months from October in 1997 to December in 2000. And CPI turned plus into minus from April in 1998 to January in 2000. And CPI turned plus into minus from April in 1998 to January in 2000. The growth rate of RPI is -2.6% and that of CPI is -0.8% in 1998. It declined to -3.0%(RPI) and -1.4%(CPI) in 1999. The growth rate of GDP has fallen down since 1992. The main purpose of this paper is to explore the reason of the deflation late in 1990 in China. I want to find out why deflation was happened in china? What is the main cause of deflation in china? What are the impacts and shocks to china economic growth by these causes? How are the impacts and shocks to china economic growth by these causes? The whole supply and demand and money contraction resulted in the downfall of GDP and CPI. To prevent the phenomena of overheated economy since 1993, most of investment moved away China because of Macroscopic Control Policy. Under this kind of situation, we couldn¡¦t say that the investments were excess. Therefore, the main reason isn¡¦t prices dropping caused by too much supply. China continued Deflation Policy after Asian Financial Crisis in 1997, so the speed of economy development decreased slowly. It also reduced the whole consumption, public spending, investment export, and money supply and demand. The effect of negative development resulted in the deflation of economy development.

There are three purposes in this thesis. First of all, the thesis examines the market herding behavior of real estates in Taiwan. Second, if herding exists in the markets, what factors cause herding behavior? Finally, how does herding behavior affect market performance? The empirical results show there is no herding evidence in the real estates markets when market moves downward. There are different results between different real estates and periods when market moves upward. There is obvious herding behavior in the pre-sale houses in Taipei City. Herding behavior also exist lag phenomenon. During lag period, there is obvious herding behavior in the pre-sale houses in Taipei when market moves upward. There is weak herding behavior in the other real estates, including pre-sale houses of Taipei County, existing houses and land. We find that macro factors such as money supply, GDP growth rate, saving amount, incoming and micro factors such as market depth and market return in the last period will cause herding behavior. Finally, herding behavior, price volatility and transaction amount volatility have Granger causality. Herding behavior will makes price more efficient and increases transaction.