In this study, we examine how advertising and customer satisfaction affect a firm¡¦s systematic risk (£]-risk) under both the highly volatile and tranquil market. This study extends prior studies that primarily considered the effects of marketing initiatives on performance metrics, focusing on systematic risk under the highly volatile and tranquil market. We examine how advertising and customer satisfaction affect a firm¡¦s £]-risk under the two distinct markets. We develop a two-stage model procedure. First, each individual firm¡¦s £]-risk in the both markets is estimated by Fama-French-Carhart-Ang 5-factor model which includes implied volatility index (VIX) as an aggregate volatility factor, along with the estimators of maximum likelihood (MLE) under the Markov switching model. Second, to examine the impact of advertising and customer satisfaction on £]-risk, we estimate empirical models for the dataset of the two distinct markets by the generalized method of moments (GMM) and the quantile regression. The results significantly support our hypotheses that higher advertising and higher customer satisfaction lower a firm¡¦s £]-risk under the overall, highly volatile and tranquil markets from the standpoint of long run. Furthermore, we find an additional discovering that from the view of short term, adverting is negatively significant associated with £]-risk under the highly volatile market, while customer satisfaction is not. Customer satisfaction, however, is negatively significant associated with £]-risk under the tranquil market, while advertising is not.

This thesis addresses asset pricing in Chinese A-share stock markets using a dataset consisting of all shares listed in Shanghai and Shenzhen stock exchanges from January 1997 to December 2007. The empirical work is carried out based on two theoretical foundations: the efficient market hypothesis and behavioural finance. It examines and compares the validity of two traditional asset pricing models and two behavioural asset pricing models. The investigation is initially performed within a traditional asset pricing framework. The three-factor Fama-French model is estimated and then augmented by additional macroeconomic and bond market variables. The results suggest that these traditional asset pricing models fail to explain fully the time-variation of stock returns in Chinese stock markets, leaving non-normally distributed and heteroskedastic residuals, calling for further explanatory variables and suggesting the existence of a structure break. Indeed, the macroeconomic and bond market factors provide little help to the asset pricing model. Using the Fama-French model as the benchmark, further research is done by investigating investor sentiment as the third dimension beside returns and risks. Investor sentiment helps explain the mis-pricing component of returns in the Fama-French model and the time-variation in the factors themselves. Incorporating investor sentiment into the asset pricing model improves the model performance, lessening the importance of the Fama-French factors, and suggesting that in China, sentiment affects both the way in which investors judge risks as well as portfolio returns directly. The sentiment effect on asset pricing is also examined under a nonlinear Markov-switching framework. The stochastic regime-dependent model reveals that stock returns in China are driven by fundamental factors in bear and low volatility markets but are prone to sentiment and become uncoupled from fundamental risks in bull and high volatility markets.

This research consists of two essays. The first essay entitled” Stock Return Forecasting with Sum-of-the-Parts Methodology: Evidence from Around the World”, examines forecasting ability of stock returns by employing the sum-of-the-parts (SOP) modeling technique introduced by Ferreira and Santa-Clara (2011).This approach decomposes return into three components of growth in price-earnings ratio, earnings growth, and dividend-price ratio. Each component is forecasted separately and fitted values are used in forecast model to predict stock return. We conduct a series of one-step ahead recursive forecasts for a wide range of developed and emerging markets over the period February 1995 through November 2014. Decomposed return components are forecasted separately using a list of financial variables and the fitted values from the best estimators are used according to out-of-sample performance. Our findings show that the SOP method with financial variables outperforms the historical sample mean for the majority of countries. Second essay entitled,” Equity Premium Predictability under Regime Shifts: International Evidence”, utilizes the modified version of the dividend-price ratio that alleviates some econometric concerns in the literature regarding the non-stationary and persistent predictor when forecasting international equity premium across different regimes. We employ Markov switching technique to address the issue of non-linearity between the equity premium and the predictor. The results show different patterns of equity premium predictability over the regimes across countries by the modified ratio as predictor. In addition, transition probability analysis show the adverse effect of financial crisis on regime transition probabilities by increasing the probability of switching between regimes post-crisis 2007 implying higher risk perceived by investors as a result of uncertainty inherent in regime transitions.

The Great Moderation means the reduction in the volatility of aggregate economic activity and here we use GDP growth rate to stand for economic activity. In this paper, we apply a Markov switching model to estimate the timing of the Great Moderation in France. Subsequently, by using a Time-varying structural vector autoregression model to determine which are the main variables that cause the reduction of French GDP growth rate and to see the relationship of these variables we choose.

在觀察匯率市場是否具有效率性時，大部分文獻透過檢定「遠期匯率是否為未來即期匯率的不偏估計值」來驗證，然而實證結果多不支持。探究原因後，部分學者於是提出，可能是在效率市場的假設上出了問題。原效率市場假設理性預期與風險中立，可是在現實生活中，人們的行為大多顯現風險趨避的特質，學者因而推論「風險溢酬的存在」或許正是造成遠期匯率偏誤的原因。 　Lucas（1982）在跨期資本資產訂價理論推導中證明出，風險溢酬具有因時而異的性質。Domowitz and Hakkio（1985）對該理論做進一步設定後，得到風險溢酬為兩國間貨幣政策波動差異的函數，因而改良風險溢酬模型為受到匯率預測精確性影響，並以ARCH模型估計。 　本文承續Domowitz and Hakkio（1985）的理論設定，以市場風險解釋風險溢酬，同時引進馬可夫變換模型，用以捕捉因時而異的風險溢酬，並且將其與ARCH-M模型所估計出的風險溢酬加以比較，期望能找出一個對風險溢酬解釋力較佳的模型。

According to Kim and Nelson (1999) and McConnell and Perez-Quiros (2000), the timing of the Great Moderation occurred in U.S. at 1984Q1. Summers (2005) found out several reasons and different timings of the Great Moderation in the G-7 countries and Australia. During the past fifty years, there was a significantly sharp decline in the volatility of the real growth rate in Australia. Between 1968 and 1982, the standard deviation of the real growth rate was 1.416%¡Fhowever, between 1983 and 1996, the standard deviation of the real growth rate drastically reduced to 0.917%. Based on this obvious situation described above, we successively build up a Markov-Switching Model and Time-Varying Structural Autoregressive Model to investigate the structural break and the sources of the Great Moderation in Australia. The findings turn out that improved monetary policy and the decreased oil shock can account for the explanation of the moderation with the break date of 1984Q1.

The core idea of this thesis is to reduce the workload of manual inspection when the performance analysis of an updated software is required. The Central Process- ing Unit (CPU) utilization, which is one of the essential factors for evaluating the performance, is analyzed. The purpose of this work is to apply machine learning techniques that are suitable for detecting the state of the CPU utilization and any changes in the test environment that affects the CPU utilization. The detection re- lies on a Markov switching model to identify structural changes, which are assumed to follow an unobserved Markov chain, in the time series data. A historical behav- ior of the data can be described by a first-order autoregression. Then, the Markov switching model becomes a Markov switching autoregressive model. Another ap- proach based on a non-parametric analysis, a distribution-free method that requires fewer assumptions, called an E-divisive method, is proposed. This method uses a hi- erarchical clustering algorithm to detect multiple change point locations in the time series data. As the data used in this analysis does not contain any ground truth, the evaluation of the methods is analyzed by generating simulated datasets with known states. Besides, these simulated datasets are used for studying and compar- ing between the Markov switching autoregressive model and the E-divisive method. Results show that the former method is preferable because of its better performance in detecting changes. Some information about the state of the CPU utilization are also obtained from performing the Markov switching model. The E-divisive method is proved to have less power in detecting changes and has a higher rate of missed detections. The results from applying the Markov switching autoregressive model to the real data are presented with interpretations and discussions.

This dissertation is a collection of three economics essays on different aspects of carbon emission trading markets. The first essay analyzes the dynamic optimal emission control strategies of two nations. With a potential to become the largest buyer under the Kyoto Protocol, the US is assumed to be a monopsony, whereas with a large number of tradable permits on hand Russia is assumed to be a monopoly. Optimal costs of emission control programs are estimated for both the countries under four different market scenarios: non-cooperative no trade, US monopsony, Russia monopoly, and cooperative trading. The US monopsony scenario is found to be the most Pareto cost efficient. The Pareto efficient outcome, however, would require the US to make side payments to Russia, which will even out the differences in the cost savings from cooperative behavior. The second essay analyzes the price dynamics of the Chicago Climate Exchange (CCX), a voluntary emissions trading market. By examining the volatility in market returns using AR-GARCH and Markov switching models, the study associates the market price fluctuations with two different political regimes of the US government. Further, the study also identifies a high volatility in the returns few months before the market collapse. Three possible regulatory and market-based forces are identified as probable causes of market volatility and its ultimate collapse. Organizers of other voluntary markets in the US and worldwide may closely watch for these regime switching forces in order to overcome emission market crashes. The third essay compares excess skewness and kurtosis in carbon prices between CCX and EU ETS (European Union Emission Trading Scheme) Phase I and II markets, by examining the tail behavior when market expectations exceed the threshold level. Dynamic extreme value theory is used to find out the mean price exceedence of the threshold levels and estimate the risk loss. The calculated risk measures suggest that CCX and EU ETS Phase I are extremely immature markets for a risk investor, whereas EU ETS Phase II is a more stable market that could develop as a mature carbon market in future years.

Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2016-04-25T19:30:00Z No. of bitstreams: 1 Sabrina Monique Schenato Bredow_.pdf: 1816066 bytes, checksum: dfc2fea0a5369d22e5ec14fc8ea0cd5a (MD5) / Made available in DSpace on 2016-04-25T19:30:00Z (GMT). No. of bitstreams: 1 Sabrina Monique Schenato Bredow_.pdf: 1816066 bytes, checksum: dfc2fea0a5369d22e5ec14fc8ea0cd5a (MD5) Previous issue date: 2016-02-29 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Este trabalho analisa a influência do recente ciclo de alta dos preços das commodities sobre a entrada de capital externo no Brasil. Para o alcance desse objetivo, foram utilizadas duas metodologias econométricas diferentes: Modelos de Mudanças de Regimes Markovianos e Modelo Vetorial de Correção de Erros (VAR/VEC). O primeiro modelo foi utilizado para delimitar o ciclo de alta dos preços das commodities e para verificar se este período é concomitante ao período de elevação da entrada de capital externo no Brasil. Os resultados apontam que o recente período de alta dos preços das commodities ocorre entre os anos de 2002 e 2014, que é o último ano da amostra utilizada nesta pesquisa. Ademais, os regimes de alta estimados para as exportações, Investimento Estrangeiro Direto (IED) e Investimento Estrangeiro em Carteira (IEC), que são os três principais agregados do Balanço de Pagamentos que representam o ingresso de capitais externos no país, ocorrem em períodos similares ao observado para a série dos preços das commodities. A partir destes resultados, a influência da alta dos preços das commodities sobre a entrada de capital externo no Brasil foi analisada através do emprego da metodologia VAR/VEC, para o período entre o ano de 2002 e 2014, a partir da estimação de três modelos diferentes, um para cada agregado do Balanço de Pagamentos brasileiro. Os resultados apontam que o ciclo de alta dos preços das commodities influenciou significativamente a entrada de dividas externas no Brasil, sendo que os efeitos mais expressivos ocorrem via comércio e entrada de capitais de curto prazo. / This study analyzes the influence of the recent cycle of high commodity prices on foreign capital inflows in Brazil. To achieve this goal, it was used two different econometric methodologies: Markov-Switching Model and Vector Error Correction Model (VAR/VEC). The first model was used to define the cycle of high commodity prices and to check if this period is concomitant to the raise period of foreign capital inflows in Brazil. The results show that the recent period of high commodity prices occurs between the years 2002 and 2014, which is the last year of the sample used in this research. Moreover, the estimated high regime for exports, Foreign Direct Investment and Foreign Portfolio Investment, which are the three main aggregates of the Balance of Payments representing the inflow of foreign capital in the country occur in similar periods to that observed for the series of commodity prices. From these results, the influence of higher commodity prices on foreign capital inflows in Brazil was analyzed through the use of VAR/VEC methodology for the period between 2002 and 2014, from the estimation of three different models, one for each aggregate of the Balance of Payments. The results show that the cycle of high commodity prices significantly influenced the foreign capital inflows in Brazil, with the most significant effects occur via trade and short-term capital inflows.

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