本文目的在於探討匯率不確定性對台灣出口波動之影響。本文應用Barkoulas et al.(2002)理論架構，利用台灣1989年至2007年的月資料。實證結果發現：美元、日圓兌新台幣的匯率波動對於台灣出口美、日兩國的數量並無明顯的影響。美元兌新台幣的匯率波動對於以美國為進口國的台灣出口波動則有正向的影響；日圓兌新台幣的匯率波動對於以日本為進口國的台灣出口波動卻沒有顯著影響。本文認為：造成美元匯率波動主要支配力量，來自於貨幣政策制定者掌握之資訊優勢差異；造成日圓匯率波動的來源則無主要支配力量的存在。造成此種結果的原因在於貨幣政策制定者長久以來所建立的政策可信度所致，削減了造成美元匯率波動的另外二股力量。因此，新台幣兌換美元匯率波動取決於貨幣政策制定者掌握經濟真實狀況的能力與其貨幣政策方向。 / This paper investigates into the effect of exchange rate uncertainty on Taiwan export volatility. Under the theoretical framework of Barkoulas et al.(2002) and the empirical monthly data of Taiwan exports from 1989 to 2007, it is summarized that the exchange rate volatility of NTD/USD and NTD/JPY had no effect on the Taiwan exporting volume toward U.S. or Japan. However, the exchange rate volatility of NTD/USD did have positive effect on the export volatility of Taiwan to U.S. while that of NTD/JPY had no significant effect on the export volatility of Taiwan to Japan.It is argued that the dominant source of NTD/USD exchange rate volatility resulted from the variance of monetary authorities’ information advantage. On the other hand, it exists no such a dominant source in NTD/JPY exchange rate volatility.

The objective of the thesis is to examine interdependencies among the stock markets of the Czech Republic, Hungary, Poland and Germany in the period 2008-2010. Two main methods are applied in the analysis. The first method is based on the use of high-frequency data and consists in the computation of realized correlations, which are then modeled using the heterogeneous autoregressive (HAR) model. In addition, we employ realized bipower correlations, which should be robust to the presence of jumps in prices. The second method involves modeling of correlations by means of the Dynamic Conditional Correlation GARCH (DCC-GARCH) model, which is applied to daily data. The results indicate that when high-frequency data are used, the correlations are biased towards zero (the so-called "Epps effect"). We also find quite significant differences between the dynamics of the correlations from the DCC-GARCH models and those of the realized correlations. Finally, we show that accuracy of the forecasts of correlations can be improved by combining results obtained from different models (HAR models for realized correlations, HAR models for realized bipower correlations, DCC-GARCH models).

本文主要是研究浮動匯率期間匯率波動對出口產業的影響。一般認為，匯率波動匯會使出口廠商的利潤風險增加，所以波動對於出口量的影響是為負的效果。不過，由於許多國外的研究的結果並不一定支持這種看法。本文針對台灣1984到1995年的資料進行實證研究，並且分別就不同出口產業對匯率波動的反應程度做討論，包括紡織類，塑膠化學類，電子類，機械類及基本金屬類五種產業，主要分為兩個架構分析： 　　（一）衡量匯率波動因子：對於匯率波動的衡量分成兩種方法：一種是以過去匯率變動的方式來衡量，另一種是以本期匯率預測的誤差來衡量，大部份的文獻都是採用前者。在此，為了將廠商事先避險的行為引入，所以採用後者的方法，將預測到的波動與未預測到的波動分離開來。 　　（二）匯率波動對各產業出口量的影響：將所有符合I(1)性質的變數用Johansen的方法做長期共整合關析的估計，再利用Granger Representation Theorem導出短期誤差修正模型，並將符合I(0)性質的波動因子引入模型當中，以便觀察匯率波動對出口量的影響。結果發現，各產業的出口量皆與匯率波動間存在明顯的負相關，其中以電子產業的影響最顯著，紡織類次之，基本金屬類影響最小，根據產品的特性分析可發現：當出口競爭愈激烈者，或是出口彈性愈大者，相對來講，會對匯率波動的反應較敏感。

住宅抵押貸款保險(Mortgage Insurance)為管理違約風險的重要工具，在2008年次級房貸風暴後更加受到金融機構的關注。為了能更準確且更有效率的預測房價及合理評價住宅抵押貸款保險，本文延續Christoffersen, Heston and Jacobs (2006)對股票報酬率的研究，提出新的GARCH模型，利用Inverse Gaussian分配取代常態分配來捕捉房價序列中存在的自我相關以及典型現象(stylized facts)，並且同時考慮房價市場中所隱含的價格跳躍現象。本文將新模型命名為IG-GARJI模型，以便和傳統GARCH模型作區分。由於傳統的GARCH模型在計算保險價格時，通常不存在封閉解，必須藉由模擬的方法來計算價格，會增加預測的誤差，本文提供IG-GARJI模型半封閉解以增進預測效率與準確度，並利用Bühlmann et al. (1996)提出的Esscher transform方法找出其風險中立機率測度，而後運用Heston and Nandi (2000)提出之遞迴方法，找出適合的住宅抵押貸款保險評價模型。實證結果顯示，在新建房屋市場中，使用Inverse Gaussian分配會比常態分配的表現要好；對於非新建房屋，不同模型間沒有顯著的差異。另外，本文亦引用Bardhan, Karapandža, and Urošević (2006)的觀點，利用不同評價模型來比較若房屋所有權無法及時轉換時，對住宅抵押貸款保險價格帶來的影響，為住宅抵押貸款保險提供更準確的評價方法。 / Mortgage insurance products represent an attractive alternative for managing default risk. After the subprime crisis in 2008, more and more financial institutions have paid highly attention on the credit risk and default risk in mortgage market. For the purpose of giving a more accurate and more efficient model in forecasting the house price and evaluate mortgage insurance contracts properly, we follow Christoffersen, Heston and Jacobs (2006) approach to propose a new GARCH model with Inverse Gaussian innovation instead of normal distribution which is capable of capturing the auto-correlated characteristic as well as the stylized facts revealed in house price series. In addition, we consider the jump risk within the model, which is widely discussed in the house market. In order to separate our new model from traditional GARCH model, we named our model IG-GARJI model. Generally, traditional GARCH model do not exist an analytical solution, it may increase the prediction error with respect to the simulation procedure for evaluating mortgage insurance. We propose a semi-analytical solution of our model to enhance the efficiency and accuracy. Furthermore, our approach is implemented the Esscher transform introduced by Bühlmann et al. (1996) to identify a martingale measure. Then use the recursive procedure proposed by Heston and Nandi (2000) to evaluate the mortgage insurance contract. The empirical results indicate that the model with Inverse Gaussian distribution gives better performance than the model with normal distribution in newly-built house market and we could not find any significant difference between each model in previously occupied house market. Moreover, we follow Bardhan, Karapandža, and Urošević (2006) approach to investigate the impact on the mortgage insurance premium due to the legal efficiency. Our model gives another alternative to value the mortgage contracts.

銅價在近年來不斷的創下歷史新高，由於台灣蓬勃的電子、半導體、工具機產業皆需要銅，因此銅進口量位居全球第五(ICSG,2009)，使得台灣企業的生產成本受國際銅價的波動影響甚鉅，全球有70%的銅價是按照英國倫敦金屬交易所(London Metal Exchange, LME)的牌價進行貿易，因此本研究欲建置預測模式以預測銅價未來趨勢。 　　本研究之資料來源為2003年1月2日至2011年7月14日的LME三月期銅價，並依文獻探討選取LME的銅庫存、三月期鋁價、三月期鉛價、三月期鎳價、三月期鋅價、三月期錫價，以及金價、銀價、石油價格、美國生產者物價指數、美國消費者物價指數、聯邦資金利率作為影響因素的分析資料。時間序列分析、類神經網路已被廣泛的用於預測股市及期貨，本研究先藉由向量自我迴歸模型篩選出有影響力的變數，同時建置GARCH時間序列預測模型與具有遞迴的Elman類神經網路預測模型，再整合兩者建置GARCH-Elman類神經網路預測模型。 本研究之向量自我迴歸模型顯示銅價與金、鋁、銅庫存前第1期；自身前第2期；鎳、錫前第3期；鋅前第4期的變動有負向的影響；受到石油前第2期的變動有正向的影響，這其中以銅的自我解釋變異最高，銅庫存最低，推測其影響已有效率地反映到銅價上。也驗證預測模型必須考量總體經濟變數，且變數先經向量自我迴歸模型的篩選能因減少雜訊而提升類神經網路的預測能力。依此建置的GARCH模型有33.81%的累積報酬率、Elman類神經網路38.11%、整合兩者的GARCH-Elman類神經網路56.46%，皆優於實際銅價指數的累積報酬率。對銅有需求的企業者，能更為準確的預測漲跌趨勢，依此判斷如何跟原物料供應商簽訂合約的價格與期間，使其免於價格趨勢的誤判而提高生產成本，並提出五點建議供未來研究者參考。 / The recent copper price in London Metal Exchange (LME) has breaking the historical high. Taiwan’s booming electronics, semiconductor and machine tool industry causing copper import volume ranked fifth in the world (ICSG, 2009). Because of 70% of copper worldwide trade in accordance with the price of the London Metal Exchange, this study using time series and neural networks to build the LME copper price forecast model. 　　This study considering copper, copper stocks, aluminum, lead, nickel, zinc, tin, gold, silver, oil ,federal funds rate, CPI and PPI during the period of 2003/1/2 to 2011/7/14. Time series model and neural networks have been widely used for forecasting the stock market and futures. In this study, using Vector Autoregressive (VAR) model screened influential variables, building GARCH model and Elman neural network to forecast the LME copper price; and further, integrating this two models to build GARCH-Elman neural network prediction model. 　　This study’s VAR models show that the copper has negative effect with gold, aluminum, copper stocks, nickel, tin, zinc and itself. And has positive impact with oil prices. The highest of explained variance is copper. Copper stocks are lowest, speculating that its impact has been efficiently reflecting on the price of copper. Verifying the prediction model must consider the macroeconomics variables. Using VAR model screened influential variables can reduce noise to enhance the predictive ability of the neural network. This study’s GARCH model has 33.81% of the cumulative rate of return, Elman neural network has 38.11% and the GARCH-Elman neural network has 56.46%. All of them are better than the actual price of copper.

Made available in DSpace on 2010-04-20T21:00:30Z (GMT). No. of bitstreams: 3 marciocecilioturma2004.pdf.jpg: 19602 bytes, checksum: 0772484d1cb46349dfbfb25620b5cdae (MD5) marciocecilioturma2004.pdf: 859203 bytes, checksum: 346a3e7d5751118ff894a182d7512b56 (MD5) marciocecilioturma2004.pdf.txt: 86793 bytes, checksum: e0c91b2715fc569bc6ec29bfce078e69 (MD5) Previous issue date: 2007-01-23T00:00:00Z / Há forte evidência que os retornos das séries financeiras apresentam caudas mais pesadas que as da distribuição normal, principalmente em mercados emergentes. No entanto, muitos modelos de risco utilizados pelas instituições financeiras baseiam-se em normalidade condicional ou não condicional, reduzindo a acurácia das estimativas. Os recentes avanços na Teoria de Valores Extremos permitem sua aplicação na modelagem de risco, como por exemplo, na estimação do Valor em Risco e do requerimento de capital. Este trabalho verifica a adequação de um procedimento proposto por McNeil e Frey [1999] para estimação do Valor em Risco e conseqüente requerimento de capital às principais séries financeiras de retornos do Brasil. Tal procedimento semi-paramétrico combina um modelo GARCH ajustado por pseudo máxima verossimilhança para estimação da volatilidade corrente com a Teoria de Valores Extremos para estimação das caudas da distribuição das inovações do modelo GARCH. O procedimento foi comparado através de backtestings com outros métodos mais comuns de estimação de VaR que desconsideram caudas pesadas das inovações ou a natureza estocástica da volatilidade. Concluiu-se que o procedimento proposto por McNeil e Frey [1999] mostrou melhores resultados, principalmente para eventos relacionados a movimentos negativos nos mercados . Futuros trabalhos consistirão no estudo de uma abordagem multivariada de grandes dimensões para estimação de VaR e requerimento de capital para carteiras de investimentos. / There is a strong evidence that financial return series are heavy-tailed, mostly in emerging markets. However, most of the risk models used by financial institutions are based in conditional or non-conditional normality, which reduces the accuracy of the estimates. The recent advances in Extreme Value Theory permit its application to risk measuring, such as Value at Risk and capital adequacy estimates. This work verifies the adequacy of a procedure proposed by McNeil and Frey [1999] to VaR and consequent capital requirement estimates for the main financial return series in Brazil. This semi parametric procedure combines a pseudo-maximumlikelihood fitting GARCH model to estimate the current volatility and the Extreme Value Theory (EVT) to estimate the tails of the innovations distribution of the GARCH model. Using backtestings the procedure was compared to other common methods of VaR estimation that disregard heavy tails of the innovations or the stochastic nature of the volatility. The procedure proposed by McNeil and Frey [1999] showed better results, mostly for negative events in the financial market2 . Further works will consist of studying a high dimensional multivariate approach to estimate VaR and capital requirements for portfolios of investment instruments.

Text in English with summaries in English and Afrikaans / The introduction of single stock futures to a market presents the opportunity to assess an individual company's response to futures trading directly, in contrast to the market-wide impact obtained from index futures studies. Thirty-eight South African companies were evaluated in terms of a possible price, volume, and volatility effect due to the initial trading of their respective single stock futures contracts. An event study revealed that SSF trading had little impact on the underlying share prices. A normalised volume comparison pre to post SSF trading showed a general increase in spot market trading volumes. The volatility effect was the main focus of this study with a GARCH(1,1) model establishing a volatility structure (pattern of behaviour) per company. Results showed a reduction in the level and changes in the structure of spot market volatility. In addition, a dummy variable regression could find no evidence of an altered company-market relationship (systematic risk) post futures. / Business Management / M.Com. (Business Management)

This study aims to analyse the precrisis period on the oil markets with a primary objective of assessing the role of speculation in the commodity's price development and its volatility. First it depicts the rapidly increasing speculative activity on the futures market together with the parallel oil price surge. The speculation is initially proxied by non-commercial traders' positions and subsequently quantified by Working's T-index. The paper then uses speculative traders' positions and both spot and futures prices to test for Granger causality within the framework of VAR models. For the sake of consistency it also evaluates causal links between speculation and inventories level. Further the study investigates the speculation impact on volatility of oil prices by employing various approaches in volatility quantification including GARCH models. Contrary to expectations we find that the speculatio's impact on both prices and their volatility is rather insignificant. In the last chapter we therefore seek for an explanation of the oil price developments by examining the market fundamentals. The interaction of supply and demand finally gives substantial evidence for understanding the price developments in the precrisis period.

Este trabajo de investigación examina el impacto de la volatilidad del tipo de cambio real, como proxy de la incertidumbre cambiaria, en el desempeño de las exportaciones totales para un panel de países de América Latina en el periodo 1989-2018. Se utilizan las variables como brecha de las exportaciones, brecha del tipo de cambio real, brecha del producto bruto interno, la brecha de la demanda mundial y la brecha de los término de intercambio. Asimismo, se estima el comportamiento de la volatilidad del tipo de cambio real modelizándola a través de modelos GARCH. Se estima un modelo panel de Vectores Autorregresivos para una muestra equilibrada de cinco países de América Latina (Argentina, Brasil, Chile, México y Perú) para el periodo 1989-2018. Los resultados sugieren que la volatilidad del tipo de cambio real tiene un efecto negativo en las exportaciones de los países seleccionados. Adicionalmente, esta investigación es relevante porque proporciona evidencia empírica de países con diferentes características económicas para comprender el efecto de las variaciones del tipo de cambio real en el desempeño de las exportaciones y, por ende, en la estabilidad del crecimiento económico. / This research work examines the impact of real exchange rate volatility, as a proxy for exchange rate uncertainty, on the performance of total exports for a panel of Latin American countries in the period 1989-2018. Variables such as the export gap, the real exchange rate gap, the gross domestic product gap, the world demand gap, and the trade terms gap are used. Likewise, the behavior of the volatility of the real exchange rate is estimated by modeling it through GARCH models. A panel model of Autoregressive Vectors is estimated for a balanced sample of five Latin American countries (Argentina, Brazil, Chile, Mexico, and Peru) for the period 1989-2018. The results suggest that the volatility of the real exchange rate has a negative effect on the exports of the selected countries. Additionally, this research is relevant because it provides empirical evidence from countries with different economic characteristics to understand the effect of variations in the real exchange rate on export performance and, therefore, on the stability of economic growth. / Trabajo de investigación

Cette thèse présente trois contributions indépendantes. La première partie se concentre sur la modélisation de la moyenne conditionnelle des rendements du marché actions : le rendement espéré du marché. Ce dernier est souvent modélisé à l'aide d'un processus AR(1). Cependant, des études montrent que lors de mauvaises périodes économiques la prédictibilité des rendements est plus élevée. Etant donné que le modèle AR(1) exclut par construction cette propriété, nous proposons d'utiliser un modèle CIR. Les implications sont étudiées dans le cadre d'un modèle espace-état bayésien. La deuxième partie est dédiée à la modélisation de la volatilité des actions et des volumes de transaction. La relation entre ces deux quantités a été justifiée par l'hypothèse de mélange de distribution (MDH). Cependant, cette dernière ne capture pas la persistance de la variance, à la différence des spécifications GARCH. Nous proposons un modèle à deux facteurs combinant les deux approches, afin de dissocier les variations de volatilité court terme et long terme. Le modèle révèle plusieurs régularités importantes sur la relation volume-volatilité. La troisième partie s'intéresse à l'analyse des stratégies d'investissement optimales sous contrainte «drawdown ». Le problème étudié est celui de la maximisation d'utilité à horizon fini pour différentes fonctions d'utilité. Nous calculons les stratégies optimales en résolvant numériquement l'équation de Hamilton-Jacobi-Bellman, qui caractérise le principe de programmation dynamique correspondant. En se basant sur un large panel d'expérimentations numériques, nous analysons les divergences des allocations optimales / This PhD thesis presents three independent contributions. The first part is concentrated on the modeling of the conditional mean of stock market returns: the expected market return. The latter is often modeled as an AR(1) process. However, empirical studies have found that during bad times return predictability is higher. Given that the AR(1) model excludes by construction this property, we propose to use instead a CIR model. The implications of this specification are studied within a flexible Bayesian state-space model. The second part is dedicated to the modeling of stocks volatility and trading volume. The empirical relationship between these two quantities has been justified by the Mixture of Distribution Hypothesis (MDH). However, this framework notably fails to capture the obvious persistence in stock variance, unlike GARCH specifications. We propose a two-factor model of volatility combining both approaches, in order to disentangle short-run from long-run volatility variations. The model reveals several important regularities on the volume-volatility relationship. The third part of the thesis is concerned with the analysis of optimal investment strategies under the drawdown constraint. The finite horizon expectation maximization problem is studied for different types of utility functions. We compute the optimal investments strategies, by solving numerically the Hamilton–Jacobi–Bellman equation, that characterizes the dynamic programming principle related to the stochastic control problem. Based on a large panel of numerical experiments, we analyze the divergences of optimal allocation programs