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.

This dissertation investigates the pricing behaviors of two major energy commodities, U.S. natural gas and crude oil, using times series models. It examines the relationships between U.S. natural gas price variations and changes in market fundamentals within a two-state Markov-switching framework. It is found that the regime-switching model does a better forecasting job in general than the linear fundamental model without regime-switching framework, especially in the case of 1-step-ahead forecast. Studies are conducted of the dynamics between crude oil price and U.S. dollar exchange rates. Empirical tests are applied to both full sample (1986—2010) and subsample (2002—2010) data. It is found that causality runs in both directions between the oil and the dollar. Meanwhile, a theoretical 5-country partial dynamic portfolio model is constructed to explain the dynamics between oil and dollar with special attention to the roles of China and Russia. It is shown that emergence of China‘s economy enhances the linkage between oil and dollar due to China's foreign exchange policy. Further research is dedicated to the role of speculation in crude oil and natural gas markets. First a literature review on theory of speculation is conducted. Empirical studies on speculation in commodity markets are surveyed, with special focus on energy commodity market. To test the theory that speculation may affect commodity prices by exaggerating the signals sent by market fundamentals, this essay utilizes the forecast errors from the first essay to investigate the forecasting ability of speculators' net long positions in the market. Limited evidence is provided to support the bubble theory in U.S. natural gas market. In conclusion, this dissertation explores both fundamentals and speculators' roles in the U.S. natural gas and global crude oil markets. It is found that market fundamentals are the major driving forces for the two energy commodities price booms seen during the past several years.

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This thesis examines stock price bubbles in the Stock Exchange of Thailand (SET) from its establishment in April 1975 until December 2012 using regime-switching bubble models, on the main aggregated market index, called the SET Index, and several disaggregated stock indices by industrial sector. The results suggest some evidence of bubble-like behaviour in these indices, most especially when a structural break is included at July 1997, the date when Thailand switched to adopting a managed floating exchange rate system. Given the limitations of published stock price indices in Thailand a new, consistent index was computed the K-NI. The econometric test results using this new index indicate strong evidence of stock price bubbles in several industrial sectors and at least some evidence of bubbles in all industry groups in the SET. Finally, the standard model is extended to study the transmission of bubbles between industry groups. The results indicate some levels of contagion in the Technology sector, as well as, in several other industry groups, while the Resources sector seems to be relatively isolated.

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 dissertation proposes three approaches for financial risk management. In the first topic, we investigate the stock return and risk of financial holding companies via Markov regime-switching model. The model reduces the disadvantage of traditional linear model, which disregard information of another regime if there exist structural change during the estimation periods. The empirical result shows that all financial holding companies have different stock risk between state 0 and state 1. Moreover, stock risks of all financial holding companies are significant lower after listing. That is, financial holding companies have diversification benefits after listing. In the second topic, we gauge the credit risk of guarantee issue in a bills finance company in Taiwan by a market-based model. Since bills finance companies engage in short-term loans, we renew the contract that can extend short-term loans to mid-and long-term loans. We find that the recovery rate, different industries and business cycle have significant impact on the credit risk of the bills finance company. In the third topic, we relax the assumption of Jarrow, Lando and Turnbull (1997), and propose an elaborate model to gauge the credit risk of Taiwanese bank loans. The empirical result indicates that the credit risk is heavily reliant on the recovery rate. Therefore, collateral value check procedure is very important, which has been found in previous topic. On the other hand, we find that the credit risk management is indifferent between banks participated in financial holding companies and others. That is, banks do not have better credit risk management if take part in financial holding companies. In conclusion, we expect approaches of the dissertation will be helpful for Taiwan¡¦s financial institutions to rise to the challenge of financial risk in the future.

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.

本篇論文主要在外匯市場建立市場投資組合、利差交易投資組合與動能交易投資組合，探討透過不同情境適當改變投資組合比重配置，是否能夠顯著提升交易策略的報酬表現。 以1999年1月至2015年10月為樣本期間，根據28個國家外匯市場資料建構市場投資組合、利差交易投資組合與動能交易投資組合等，之後根據三種投資組合報酬情況透過馬可夫情境轉換模型區分成三種情境。按三種情境的各種投資組合超額報酬表現，再利用馬可維茲的平均數-變異數投資組合模型配置各情境下各項交易的比重，再依據計算出的預期情境與相對應比重進行投資。其結果顯示在樣本期間內，本篇論文的交易策略相較於外匯市場投資組合、利差交易投資組合與動能交易組合有較佳的投資表現。 在樣本外測試部分，採用自2012年中開始的連續情境二資料進行分析。報酬方面，在其他交易型態呈現負報酬較多情況下，就本文交易策略而言，投資者隨時根據其各種交易平均報酬與共變異數進行交易比重配置，適時放空交易策略或投資無風險資產，產生正報酬。但從標準差可以推斷投資者面對未來的不確定，在整個樣本外期間歷時的34個月當中標準差亦無法有效降低，說明了投資者面對下一期總體環境的高不確定性。 / In this thesis, we mainly investigate whether it could improve the performance of currency portfolio by adjusting weights among carry trade, momentum and market return in foreign exchange market under different kinds of regimes. Based on a sample of 28 market currencies, we form three kinds of transactions in our portfolio, including carry trade, momentum, and market return. Under Markov switching model, we divide the sample period into three regimes, and then determine weights among carry trade, momentum and market return by parameters of each re-gime using Markowitz mean-variance analysis. Finally, we invest different weights among three transactions according to each expected regime. We find the result that although the return of the strategy is just a little higher than the carry trade, the risk is much lower compared to other transactions. In our out-of-sample testing, we analyze the performance by using the data of the regime two which begins September, 2012. With the respect to the return, most of other risky transactions have negative return, but we get positive return by adjusting the long position and short position according to the result of the mean-variance anal-ysis. However, we can not effectively reduce risk by using the strategy, and in the meantime it can explain the high uncertainty investors face toward the next period.