(None) Technical Summary of Thesis: The topic of my thesis is currency crisis. Currency crises have been a recurrent feature of the international economy from the invention of paper money. They are not confined to particular economies or specific region. They take place in developed, emerging, and developing countries and are spread all over the globe. Countries that experience currency crises face economic losses that can be huge and disruptive. However, the exacted toll is not only financial and economic, but also human, social, and political. It is clear that the currency crisis is a real threat to financial stability and economic prosperity. The main objective of this thesis is to analyze the determinants of currency crises for twenty OECD countries and South Africa from 1970 through 1998. It systematically examines the role of economic fundamentals and contagion in the origins of currency crises and empirically attempts to identify the channels through which the crises are being transmitted. It also examines the links between the incidence of currency crises and the choice of exchange rate regimes as well as the impact of capital market liberalization policies on the occurrence of currency crises. The first chapter identifies the episodes of currency crisis in our data set. Determining true crisis periods is a vital step in the empirical studies and has direct impact on the reliability of their estimations and the relevant policy implications. We define a period as a crisis episode when the Exchange Market Pressure (EMP) index, which consists of changes in exchange rates, reserves, and interest rates, exceeds a threshold. In order to minimize the concerns regarding the accuracy of identified crisis episodes, we apply extreme value theory, which is a more objective approach compared to other methods. In this chapter, we also select the reference country, which a country’s currency pressure index should be built around, in a more systematic way rather than by arbitrary choice or descriptive reasoning. The second chapter studies the probability of a currency exiting a tranquil state into a crisis state. There is an extensive literature on currency crises that empirically evaluate the roots and causes of the crises. Despite the interesting results of the current empirical literature, only very few of them account for the influence of time on the probability of crises. We use duration models that rigorously incorporate the time factor into the likelihood functions and allow us to investigate how the amount of time that a currency has already spent in the tranquil state affects the stability of a currency. Our findings show that high values of volatility of unemployment rates, inflation rates, contagion factors (which mostly work through trade channels), unemployment rates, real effective exchange rate, trade openness, and size of economy increases the hazard of a crisis. We make use of several robustness checks, including running our models on two different crisis episodes sets that are identified based on monthly and quarterly type spells. The third chapter examines the links between the incidence of currency crises and the choice of exchange rate regimes as well as the impact of capital market liberalization policies on the occurrence of currency crises. As in our previous paper, duration analysis is our methodology to study the probability of a currency crisis occurrence under different exchange rate regimes and capital mobility policies. The third chapter finds that there is a significant link between the choice of exchange rate regime and the incidence of currency crises in our sample. Nevertheless, the results are sensitive to the choice of the de facto exchange rate system. Moreover, in our sample, capital control policies appear to be helpful in preventing low duration currency crises. The results are robust to a wide variety of sample and models checks.

In this study, the most prominent goodness-of-fit tests for censored samples are reviewed. Power properties of goodness-of-fit statistics of the null hypothesis that a sample which is censored from right, left and both right and left which comes from uniform, normal and exponential distributions are investigated. Then, by a similar argument extreme value, student t with 6 degrees of freedom and generalized logistic distributions are discussed in detail through a comprehensive simulation study. A variety of real life applications are given. Suitable test statistics for testing the above distributions for censored samples are also suggested in the conclusion.

The aim of this research project was to estimate parameters for the distribution of annual maximum flood levels for the Zambezi River at Katima Mulilo. The estimation of parameters was done by using the maximum likelihood method. The study aimed to explore data of the Zambezi's annual maximum flood heights at Katima Mulilo by means of fitting the Gumbel, Weibull and the generalized extreme value distributions and evaluated their goodness of fit.

The presence of different types of defects is an important reality for manufacturers and users of engineering materials. Generally, the defects are either considered to be the unwanted products of impurities in the raw materials or to have been introduced during the manufacturing process. In high-quality steel materials, such as tool steel, the defects are usually non-metallic inclusions such as oxides or sulfides. Traditional methods for purity control during standard manufacturing practice are usually based on the light optical microscopy scanning of polished surfaces and some statistical evaluation of the results. Yet, as the steel manufacturing process has improved, large defects have become increasingly rare. A major disadvantage of the traditional quality control methods is that the accuracy decreases proportionally to the increased rarity of the largest defects unless large areas are examined. However, the use of very high cycle fatigue to 109 cycles has been shown to be a powerful method to locate the largest defects in steel samples. The distribution of the located defects may then be modelled using extreme value statistics. This work presents new methods for determining the volume distribution of large defects in high-quality steels, based on ultrasonic fatigue and the Generalized Extreme Value (GEV) distribution. The methods have been developed and verified by extensive experimental testing, including over 400 fatigue test specimens. Further, a method for reducing the distributions into one single ranking variable has been proposed, as well as a way to estimate an ideal endurance strength at different life lengths using the observed defects and endurance limits. The methods can not only be used to discriminate between different materials made by different process routes, but also to differentiate between different batches of the same material. It is also shown that all modes of the GEV are to be found in different steel materials, thereby challenging a common assumption that the Gumbel distribution, a special case of the GEV, is the appropriate distribution choice when determining the distribution of defects. The new methods have been compared to traditional quality control methods used in common practice (surface scanning using LOM/SEM and ultrasound C-scan), and suggest a greater number of large defects present in the steel than could otherwise be detected.

Thesis (Ph. D.)--University of California, San Diego, 2005. / Vita. Includes bibliographical references (leaves 118-122).

Title: Stochastical inference in the model of extreme events Author: Jan Dienstbier Department/Institute: Department of probability and mathematical statistics Supervisor of the doctoral thesis: Doc. RNDr. Jan Picek, CSc. Abstract: The thesis deals with extremal aspects of linear models. We provide a brief explanation of extreme value theory. The attention is then turned to linear models Yn×1 = Xn×pβp×1 + En×1 with the errors Ei ∼ F, i = 1, . . . , n fulfilling the do- main of attraction condition. We examine the properties of the regression quantiles of Koenker and Basset (1978) under this setting we develop theory dealing with extremal characteristics of linear models. Our methods are based on an approximation of the regression quantile process for α ∈ [0, 1] expanding older results of Gutenbrunner et al. (1993). Our result holds in [α∗ n, 1 − α∗ n] with a better rate of α∗ n → 0 than the other approximations described previously in the literature. Consecutively we provide an ap- proximation of the tails of regression quantile. The approximations of the tails enable to develop theory of the smooth functionals, which are used to establish a new class of estimates of extreme value index. We prove T(F−1 n (1 − knt/n)) is consistent and asymp- totically normal estimate of extreme for any T member of the class....

In dieser Arbeit wurde erstmalig die Anwendung der Extremwerttheorie auf Wartezeiten eines lichtsignalgesteuerten Verkehrsknotenpunktes untersucht. Anhand der Verkehrsstärken eines realen Knotenpunktes wurden mit der Simulationssoftware PTV Vissim 100 Datensätze mit individuellen Wartezeiten erzeugt. Als Referenz wurde eine zweite Simulationsreihe durchgeführt. Diese erfolgte mit 15 % höherer Verkehrsstärke. Mittels der Blockmaximum-Methode wurden aus den erzeugten Datensätzen die Maxima ausgewählt, welche mit der Maximum-Likelihood Methode an eine Extremwertverteilung angepasst wurden. Die Bewertung der Schätzung wurde mit dem Kolmogorov-Smirnov Test vorgenommen. Anschließend wurde die Wahrscheinlichkeit, dass bestimmte Wartezeiten überschritten werden (Value at Risk) berechnet. Im Ergebnis konnten 22 % der geschätzten Extremwertverteilungen mit ausreichender Güte angepasst werden. Für die restlichen Datensätze sollte nach Alternativen zur angemessenen Beschreibung gesucht werden.

[pt] A partir da década de 90, a metodologia Value at Risk (VaR) se difundiu pelo mundo, tanto em instituições financeiras quanto em não financeiras, como uma boa prática de mensuração de riscos. Em geral, abordagens paramétricas são muito utilizadas pelo mercado, apesar de freqüentemente não levarem em conta uma característica muito encontrada nas distribuições dos retornos de ativos financeiros: a presença de caudas pesadas. Uma abordagem baseada na Teoria dos Valores Extremos (TVE) é uma boa solução quando se deseja modelar caudas de distribuições probabilísticas que possuem tal característica. Em contra partida, poucos são os trabalhos que procuram desenvolver a TVE aplicada a ativos de renda-fixa. Com base nisto, este estudo propõe uma abordagem de simples implementação de cálculo de VaR para ativos de renda-fixa baseado na Teoria dos Valores Extremos. / [en] Since the 90 decade, the use of Value at Risk (VaR) methodology has been disseminated among both financial and non-financial institutions around the world, as a good practice in terms of risks management. In spite of the fact that it does not take into account one of the most important characteristics of financial assets returns distribution - fat tails (excess of kurtosis), the parametric approach is the most used method for Value at Risk measurement. The Extreme Value Theory (EVT) is an alternative method that could be used to avoid the underestimation of Value at Risk, properly modeling the characteristics of probability distribution tails. However, there are few works that applied EVT to fixed-income market. Based on that, this study implements a simple approach to VaR calculation, in which the Extreme Value Theory is applied to fixed-income assets.

[pt] Os métodos clássicos para estudo de valores extremos de séries temporais se apóiam nas chamadas distribuições de extremos. Uma alternativa é o método P.O.T. (Peaks Over Threshold), desenvolvido por hidrologistas, o qual estuda apenas os valores da série que excedem um dado patamar. Esses procedimentos são baseados em hipóteses restritivas. Nesse trabalho desenvolvemos modelos sobre extremos que podem ser utilizados em situações mais gerais. Eles são essencialmente modelos lineares dinâmicos com inferência Bayesiana, nos quais as observações têm um distribuição de extremos. Embora essas distribuições não sejam da famí­lia exponencial, toda a análise é feita explicitamente, sem aproximações numéricas. Tratamos ainda da construção de distribuições a priori não informáticas. Finalmente, a partir desses modelos retomamos problemas clássicos de previsão de extremos. / [en] The classical approaches for extreme values studies make use the so called Extreme Values Distribution. An alternative approach, known as P.O.T. (Peaks Over Threshold) developed by hydrologists considers only excedances over a given threshold value. All the existing approaches are in a sense, based on constrained hupothesis. In this thesis we developed forecasting models for extreme values that are dynamic linear model as the underlying formulation, and the Bayesian inference. Although the process observation follows an extreme values distribution and, therefore not a member of the exponential family, we were able to formulate explicitly the model with no use of numerical approximations throughout, Concerning the parameter priors, we use in the model formulation the Jeffery`s non informative prior.

This diploma thesis studies extreme value theory and its application in finan- cial risk management, when focusing on computation of well-known risk measure - Value at Risk (VaR). The first part of the thesis reviews theoretical background. In particular, it rigorously discusses the extreme value theory when emphasi- zing fundamentals theorems and their consequences followed by the summary of methods based on this theory, specifically, Block Maxima method, Hill met- hod and Peaks over Threshold method. Moreover, specific issues that may arise in such applications and ways how to deal with these problems are described. The second part of the thesis contains extensive empirical study, which together with theoretical foundings applies each of the examined method to real market data of the closing prices of Dow Jones Industrial Average stock index, stocks of JPMorgan and stock index Russell 2000 in order to compare methods based on extreme value theory together with the classic methodology RiskMetrics. 1