<p>The purpose of this paper is to investigate the predictive power of the variables advanced notice of dismissal (layoffs) and vacancies for the unemployment rate. Based on the Box Jenkins Methodology, the paper makes use of Granger causality and out-of-sample tests to compare the forecast performance of a naïve reference model and the two models extended to include either lagged values of layoffs or vacancies. It is shown that layoffs make up a significant leading variable, exhibiting particularly strong predictive power at forecast horizons of 2-6 months. It is also shown that the predictive power of vacancies is more ambiguous. Vacancies constitute a valuable explanatory variable for the unemployment rate, but does not possess the same leading, predictive qualities as layoffs.</p>

The purpose of this paper is to investigate the predictive power of the variables advanced notice of dismissal (layoffs) and vacancies for the unemployment rate. Based on the Box Jenkins Methodology, the paper makes use of Granger causality and out-of-sample tests to compare the forecast performance of a naïve reference model and the two models extended to include either lagged values of layoffs or vacancies. It is shown that layoffs make up a significant leading variable, exhibiting particularly strong predictive power at forecast horizons of 2-6 months. It is also shown that the predictive power of vacancies is more ambiguous. Vacancies constitute a valuable explanatory variable for the unemployment rate, but does not possess the same leading, predictive qualities as layoffs.

Numerous authors note failure of private insurance markets to provide affordable and comprehensive crop insurance. Economic logic suggests that index contracts potentially may have some advantages when compared with traditional (farm based) crop insurance. It is also a matter of common knowledge that weather is an important production factor and at the same time one of the greatest sources of risk in agriculture. Hence introduction of crop insurance contracts, based on weather indexes, might be a reasonable approach to mitigate problems, associated with traditional crop insurance products, and possibly lower the cost of insurance for end users. In spite of the fact that before the financial crisis of 2008-09 market for weather derivatives was the fastest growing derivatives market in the USA, agricultural producers didn’t express much interest in application of weather derivatives to management of their systematic risk. There are several reasons for that, but the most important one is the presence of high basis risk, which is represented by its two major components: technological (i.e. goodness of fit between yield and weather index) and geographical basis. Majority of the researchers is focusing either on pricing of weather derivatives or on mitigation of geographical basis risk. At the same time the number of papers researching possible ways to decrease technological basis is quite limited, and always assumes linear dependency between yields and weather variables, while estimating the risk reducing efficiency of weather contracts, which is obviously large deviation from reality. The objective of this study is to estimate the risk reducing efficiency of crop insurance contracts, based on weather derivatives (indexes) in the state of Texas. The distributions of representative farmer’s profits with the proposed contracts are compared to the distributions of profits without a contract. This is done to demonstrate the risk mitigating effect of the proposed contracts. Moreover the study will try to account for a more complex dependency structures between yields and weather variables through usage of copulas, while constructing joint distribution of yields and weather data. Selection of the optimal copula will be implemented in the out-of-sample efficient framework. An effort will be done to identify the most relevant periods of year, when weather has the most significant influence on crop yields, which should be included in the model, and to discover the most effective copula to model joint weather/yield risk. Results suggest that effective insurance of crop yields in the state of Texas by the means of proposed weather derivatives is possible. Besides, usage of data-mining techniques allows for more accurate selection of the time periods to be included in the model than ad hoc procedure previously used in the literature. Finally selection of optimal copula for modeling of joint weather/yield distribution should be crop and county specific, while in general Clayton and Frank copula of Archimedean copula family provide the best out-of-sample metric results.

This thesis overviews and discusses the issue of nonperforming loans in general and in China, and financial sector development from the beginning of Deng Xiaoping's reform era in 1978. The focus is put on the underlying institutional framework as it is found to be one of key contributing factors of NPLs in China. Utilizing a sample of 23 countries over the period 2000-2009, we estimate the impact of macroeconomic determinants on nonperforming loans. One of the regressors, output gap, is estimated via Kalman filtering technique based on the multivariate unobserved component (MUC) method. Consequently, out-of-sample method forecasts for five subgroups of countries, thus providing different benchmarks for China, are compared with official data. The conclusion argues the NPLs ratio to be understated in official statistics, especially towards the end of the series, which might be a sign of increasing macroeconomic instability in China.

Submitted by Vinicius Vale Pereira (viniciusvale@gmail.com) on 2016-03-01T18:04:06Z No. of bitstreams: 1 tese.v4.1.FINAL.pdf: 1143339 bytes, checksum: e3167f8ba5e577bce7f0c40605716c5d (MD5) / Rejected by Renata de Souza Nascimento (renata.souza@fgv.br), reason: Vinicius, boa noite Para que possamos aceitar seu trabalho deverá realizar os ajustes: O título deve estar em letras maiúsculas. A ficha catalográfica após a contra capa, na parte inferior. Não deve constar números romanos. As páginas anteriores não podem estar numeradas. Centralizar os títulos Agradecimentos, Resumo e Abstract. Após ajustes, submeter novamente o trabalho. Att on 2016-03-02T00:43:38Z (GMT) / Submitted by Vinicius Vale Pereira (viniciusvale@gmail.com) on 2016-03-02T13:49:51Z No. of bitstreams: 1 tese.v4.2.pdf: 1141368 bytes, checksum: 31f0646f0c15281795a6b96bd647ef42 (MD5) / Rejected by Renata de Souza Nascimento (renata.souza@fgv.br), reason: Vinicius, boa tarde Por gentileza, colocar o código CDU no canto do lado direito, da ficha catalográfica. Att on 2016-03-02T16:18:05Z (GMT) / Submitted by Vinicius Vale Pereira (viniciusvale@gmail.com) on 2016-03-02T16:48:39Z No. of bitstreams: 1 tese.v4.2.pdf: 1141278 bytes, checksum: 9f554d94d8c1ab631bb8127fadbbe43a (MD5) / Approved for entry into archive by Renata de Souza Nascimento (renata.souza@fgv.br) on 2016-03-02T17:07:51Z (GMT) No. of bitstreams: 1 tese.v4.2.pdf: 1141278 bytes, checksum: 9f554d94d8c1ab631bb8127fadbbe43a (MD5) / Made available in DSpace on 2016-03-02T17:27:52Z (GMT). No. of bitstreams: 1 tese.v4.2.pdf: 1141278 bytes, checksum: 9f554d94d8c1ab631bb8127fadbbe43a (MD5) Previous issue date: 2016-02-03 / O presente trabalho propõe um modelo de previsão simultânea de taxas de câmbio de vários países utilizando a abordagem GVAR e analisa a qualidade destas previsões. Para isso foram utilizados dados de 10 países ou regiões de taxa de câmbio, taxas de juros e nível de preços com frequência mensal entre 2003 e 2015. As previsões foram feitas utilizando janela móvel de 60 meses e avaliadas através da comparação dos erros quadráticos médios contra o benchmark padrão, o random walk, e dos testes de Pesaran e Timmermann e de Diebold e Mariano. Foram feitas previsões out-of-sample para horizontes de 1, 3, 12 e 18 meses. Os resultados mostram que o modelo proposto não consegue superar sistematicamente o random walk, contudo apresenta algum poder de previsão em alguns casos específicos / This paper proposes a model that simultaneously forecasts foreign exchange rate for several countries using the GVAR framework and analyzes the quality of these forecasts. For this purpose, data from 10 countries or regions regarding exchange rates, interest rates and price levels on a monthly basis between 2003 and 2015 was used. The forecasting was performed using a 60 months moving window and the evaluation of these was performed comparing the root mean square errors against the standard benchmark, the random walk, and by Pesaran-Timmermann and Diebold-Mariano Tests. Out-of-sample forecasts were estimated for horizons of 1, 3, 12 and 18 months. The results show that the model cannot systematically outperform the random walk, although it has some predictive power in some specific cases.

This diploma thesis evaluates out-of-sample predictive ability of exchange rate models. The first part of the thesis summarizes existing empirical findings about exchange rate predictability and describes exchange rate models chosen to be evaluated. The second part of the thesis evaluates predictive ability of purchasing power parity, uncovered interest parity, monetary model and Taylor rule model. The exchange rate models are evaluated on CZK/EUR and CZK/USD currency pairs. The analysis is made using quarterly data from 1999 to 2013, while 2009 to 2013 period is reserved for forecast evaluation. The predictive ability of exchange rate models is evaluated in one quarter, one year and three years horizons. The exchange rate models are specified in first differences and estimated by ordinary least squares method. The forecasts are made using rolling regression. The exchange rate models are evaluated using RMSE, Theil's U, CW test and direction of change criterion. The diploma thesis concludes with description of own empirical findings.

We compare two time series models: an ARMA(1,1)-ACD(1,1)-NIG model against an ARMA(1,1)-GARCH(1,1)-NIG model. Their out-of-sample performance is of interest rather than their in-sample properties. The models produce one-day ahead forecasts which are evaluated using three statistical tests: VaR-test, VaRdur-test and Berkowitz-test. All three tests are concerned with the the tail events, since our time series models are often used to estimate downside risk. When the two models are applied to data on Canadian stock market returns, our three statistical tests point in the direction that the ACD model and the GARCH model perform similarly. The difference between the models is small. We finish with comments on the model uncertainty inherit in the comparison.

This study employs Extreme Value Theory and several univariate methods to compare their Value-at-Risk and Expected Shortfall predictive performance. We conduct several out-of-sample backtesting procedures, such as uncondi- tional coverage, independence and conditional coverage tests. The dataset in- cludes five different stock markets, PX50 (Prague, Czech Republic), BIST100 (Istanbul, Turkey), ATHEX (Athens, Greece), PSI20 (Lisbon, Portugal) and IBEX35 (Madrid, Spain). These markets have different financial histories and data span over twenty years. We analyze the global financial crisis period sep- arately to inspect the performance of these methods during the high volatility period. Our results support the most common findings that Extreme Value Theory is one of the most appropriate risk measurement tools. In addition, we find that GARCH family of methods, after accounting for asymmetry and fat tail phenomena, can be equally useful and sometimes even better than Extreme Value Theory based method in terms of risk estimation. Keywords Extreme Value Theory, Value-at-Risk, Expected Shortfall, Out-of-Sample Backtesting Author's e-mail ozturkdurmus@windowslive.com Supervisor's e-mail ies.avdulaj@gmail.com

It has been noticed that throughout the years, immigrant’s skills, knowledge, and experience have declined. In fact, researchers have noticed the presence of cohort effects, where there are differences in quality and skills across the immigrants. Using data from the German Socio-Economic Panel and through an out-of-sample forecast of the employment of recent cohorts based on how earlier newcomers performed, I can confirm that, over time, immigrants see their probability of being employed decrease. For instance, employment decreased from 99% for immigrants that arrived in Germany in 2010 to 92% for those that came in 2015. The linear probability model also highlights that not only human capital influences directly employment levels of immigrants. Undeniably, the region of origin and the immigrants’ duration of residence in Germany also impact the likelihood of finding a paid job. Therefore, cohort effects cannot only be justified by the fact that newly arrived immigrants are very different from those who arrived some years ago.

This paper studies the role of fluctuation of the aggregate consumption-wealth ratio(cay) for predicting Taiwan stock return. The effect of cay on U.S. stock return has been recently confirmed by Lettau and Ludvigson (2001) with a two stage method. In the first step, estimate the ratio used a dynamic least square(DLS) technique. Second, to investigate the performance of cay, they use in-sample and out-of-sample test. In this paper, we follow the method which Lettau and Ludvigson(2001) use to examine the predictability of cay. Using quarterly market data from 1998 to 2010, we find cay is strong predictors of excess return in out-of sample test. We also find that this ratio is a better forecaster of future returns at intermediate horizons compared to short time.