Practitioners of Forecast-based Action (FbA) argue that a humanitarian response able to utilize forecasts to accurately predict disaster, secure funding, and take action before the onset of a crisis will benefit donors and beneficiaries alike. In search of effective and efficient early-action regimes, a number of major humanitarian actors are developing FbA projects of various designs, predominantly in response to natural disaster and famine. While numerous organizations and institutions have expressed interest in developing FbA mechanisms, the tool has only been applied in a limited capacity to the humanitarian needs generated by armed conflict. This research seeks to understand whether a scalable FbA approach can be developed to stage principled, anticipatory humanitarian action in response to situations in which rigorous evaluations predict the likelihood of imminent armed conflict. The hypothesis is that the application of FbA to armed conflict is possible, but due to the complex political nature of conflict, implementing organizations should try to focus on creating mechanisms managed by humanitarian actors and, in so far as possible, be insulated from outside influence. This research is the first academic work to specifically investigate the application of FbA to armed conflict. Following an extensive review of current FbA mechanisms and conflict early warning practices, this research concludes that a conflict-centered FbA system akin to the automated FbA systems in use today to respond to natural disaster and famine is possible, but that the endeavor presents many practical and conceptual barriers to implementation. In particular, diffuse models such as the Start Fund offer a hopeful glimpse at a type of horizontal, member-driven FbA mechanism that is both highly context-sensitive and relatively insulated from outside influence. Such a design, however, features notable and inherent limitations in its ability to reliably and accurately predict the outbreak of conflict and respond in a manner that minimizes regretful actions.

Connus sous le nom de Systèmes d’Alerte Avancés, ou Early Warning Systems (EWS), les modèles de prévision des crises financières sont appelés à jouer un rôle déterminant dans l’orientation des politiques économiques tant au niveau microéconomique qu’au niveau macroéconomique et international. Or,dans le sillage de la crise financière mondiale, des questions majeures se posent sur leur réelle capacité prédictive. Deux principales problématiques émergent dans le cadre de cette littérature : comment évaluer les capacités prédictives des EWS et comment les améliorer ?Cette thèse d’économétrie appliquée vise à proposer (i) une méthode d’évaluation systématique des capacités prédictives des EWS et (ii) de nouvelles spécifications d’EWS visant à améliorer leurs performances. Ce travail comporte quatre chapitres. Le premier propose un test original d’évaluation des prévisions par intervalles de confiance fondé sur l’hypothèse de distribution binomiale du processus de violations. Le deuxième chapitre propose une stratégie d’évaluation économétrique des capacités prédictives des EWS. Nous montrons que cette évaluation doit être fondée sur la détermination d’un seuil optimal sur les probabilités prévues d’apparition des crises ainsi que sur la comparaison des modèles.Le troisième chapitre révèle que la dynamique des crises (la persistance) est un élément essentiel de la spécification économétrique des EWS. Les résultats montrent en particulier que les modèles de type logit dynamiques présentent de bien meilleurs capacités prédictives que les modèles statiques et que les modèles de type Markoviens. Enfin, dans le quatrième chapitre nous proposons un modèle original de type probit dynamique multivarié qui permet d’analyser les schémas de causalité intervenant entre différents types crises (bancaires, de change et de dette). L’illustration empirique montre clairement que le passage à une modélisation trivariée améliore sensiblement les prévisions pour les pays qui connaissent les trois types de crises. / Known as Early Warning Systems (EWS), financial crises forecasting models play a key role in definingeconomic policies at microeconomic, macroeconomic and international level. However, in the wake ofthe global financial crisis, numerous questions with respect to their forecasting abilities have been raised,as very few signals were drawn prior to the starting of the turmoil. Two questions arise in this context:how to evaluate EWS forecasting abilities and how to improve them?The broad goal of this applied econometrics dissertation is hence (i) to propose a systematic model-free evaluation methodology for the forecasting abilities of EWS as well as (ii) to introduce new EWSspecifications with improved out-of-sample performance. This work has been concretized in four chapters.The first chapter introduces a new approach to evaluate interval forecasts which relies on the binomialdistributional assumption of the violations series. The second chapter proposes an econometric evaluationmethodology of the forecasting abilities of an EWS. We show that adequate evaluation must take intoaccount the cut-off both in the optimal crisis forecast step and in the model comparison step. The thirdchapter points out that crisis dynamics (persistence) is essential for the econometric specification of anEWS. Indeed, dynamic logit models lead to better out-of-sample forecasting probabilities than those oftheir main competitors (static model and Markov-switching one). Finally, a multivariate dynamic probitEWS is proposed in the fourth chapter to take into account the causality between different types of crises(banking, currency, sovereign debt). The empirical application shows that the trivariate model improvesforecasts for countries that underwent the three types of crises.

L’objectif de ce travail est d’analyser les implications du choix de régime de change dans les pays émergents et en développement, ainsi que d’apporter des éclaircissements sur les facteurs jouant un rôle important dans le déclenchement des crises (de change, bancaires, financières…) dans ces pays. Pour cela, l’analyse se tourne, dans un premier temps, vers la question du choix de régime de change optimal. Cette partie du travail s’appuie principalement sur un grand nombre de travaux théoriques et empiriques traitant de cette question, pour mettre en lumière les implications de ce choix, tout en tenant compte des particularités du groupe de pays qui font l’objet de cette étude. Dans une deuxième partie nous nous intéressons aux crises et les facteurs qui jouent un rôle majeur dans leur incidence. Ainsi, après une revue des différents modèles de crises afin d’identifier les variables d’intérêt, nous construisons deux modèles de prédiction des crises, ou « d’alarme précoce ». Enfin, la troisième partie du travail rassemble les enseignements tirés des deux parties précédentes pour traiter d’une question qui prend une ampleur croissante dans ces pays : étant donné la logique d’intégration financière mondiale et les avantages présentés par un régime de changes flottants dans un tel contexte, de quelle manière un pays envisageant un sortie vers ce régime de change peut-il la planifier, et à quel moment doit-il l’entreprendre, pour réussir une sortie sans crise majeure, que nous qualifions de sortie « ordonnée » ? Pour répondre à cette question, nous nous appuyons sur des expériences passées qui nous permettent de construire un modèle identifiant les variables susceptibles d’accroître la probabilité d’une sortie ordonnée. Nous complétons ce modèle par quelques considérations supplémentaires qui constituent des conditions importantes à la réussite d’une sortie ordonnée. L’objectif est d’apporter des recommandations susceptibles de faciliter cette transition. / The aim of this work is to analyze the implications of exchange rate regime choice in developing and emerging market economies, as well as highlight the factors that play a major role in the incidence of crises (currency, banking, financial…) in these countries. With this aim in mind, we start our analysis by turning to the question of the choice of the optimal exchange rate regime. This part of our work draws on a large number of both theoretical and empirical works evoking this question in order to determine the implications of this choice, all the while keeping in mind the fact that this particular group of countries present certain characteristics that are usually absent in their industrial counterparts. The second part of our work concentrates more specifically on crises and the factors that play a major role in their occurrence. Therefore, following a brief overview of different crisis models in order to identify the variables of interest, we propose two models for crisis prediction, or “Early Warning Systems”. Finally, the third and final part of our work brings together the conclusions of the earlier parts in order to address an issue that is becoming increasingly important in developing and emerging market economies: given their greater integration in international financial and capital markets, as well as the incontestable advantages of a floating exchange rate regime in such a context, how can a country wishing to exit to a more flexible exchange rate arrangement undertake such a transition, and when, in order to achieve an “orderly” exit, that is, an exit that is not accompanied by a crisis? To answer this question we draw on past experiences to construct a model indentifying the economic variables that might increase the probability of an orderly exit. We complete this model with a number of additional considerations that have recently emerged as important preconditions for an orderly exit, in order to provide some useful policy recommendations facilitating this transition.

La récente crise financière qui a débuté aux États-Unis en 2007 a révélé les faiblesses du système bancaire international se traduisant par l’effondrement de nombreuses institutions financières aux États-Unis et aussi par l’augmentation de la part des prêts non performants dans les bilans des banques européennes. Dans ce cadre, nous proposons d’abord d’estimer et de tester l’efficacité des modèles de prévisions des défaillances bancaires. L’objectif étant d’établir un système d’alerte précoce (EWS) de difficultés bancaires basées sur des variables financières selon la typologie CAMEL (Capital adequacy, Asset quality, Management quality, Earnings ability, Liquidity). Dans la première étude, nous avons comparé la classification et la prédiction de l’analyse discriminante canonique (CDA) et de la régression logistique (LR) avec et sans coûts de classification en combinant ces deux modèles paramétriques avec le modèle descriptif d’analyse en composantes principales (ACP). Les résultats montrent que les modèles (LR et CDA) peuvent prédire la faillite des banques avec précision. De plus, les résultats de l’ACP montrent l’importance de la qualité des actifs, de l’adéquation des fonds propres et de la liquidité en tant qu’indicateurs des conditions financières de la banque. Nous avons aussi comparé la performance de deux méthodes non paramétriques, les arbres de classification et de régression (CART) et le nouveau modèle régression multivariée par spline adaptative (MARS), dans la prévision de la défaillance. Un modèle hybride associant ’K-means clustering’ et MARS est également testé. Nous cherchons à modéliser la relation entre dix variables financières et le défaut d’une banque américaine. L’approche comparative a mis en évidence la suprématie du modèle hybride en termes de classification. De plus, les résultats ont montré que les variables d’adéquation du capital sont les plus importantes pour la prévision de la faillite d’une banque. Enfin, nous avons étudié les facteurs déterminants des prêts non performants des banques de l’Union Européenne durant la période 2012-2015 en estimant un modèle à effets fixe sur données de panel. Selon la disponibilité des données nous avons choisi un ensemble de variables qui se réfèrent à la situation macroéconomique du pays de la banque et d’autres variables propres à chaque banque. Les résultats ont prouvé que la dette publique, les provisions pour pertes sur prêts, la marge nette d’intérêt et la rentabilité des capitaux propres affectent positivement les prêts non performants, par contre la taille de la banque et l’adéquation du capital (EQTA et CAR) ont un impact négatif sur les créances douteuses. / The recent financial crisis that began in the United States in 2007 revealed the weaknesses of the international banking system resulting in the collapse of many financial institutions in the United States and also the increase in the share of non-performing loans in the balance sheets of European banks. In this framework, we first propose to estimate and test the effectiveness of banking default forecasting models. The objective is to establish an early warning system (EWS) of banking difficulties based on financial variables according to CAMEL’s ratios (Capital adequacy, Asset quality, Management quality, Earnings ability, Liquidity). In the first study, we compared the classification and the prediction of the canonical discriminant analysis (CDA) and the logistic regression (LR) with and without classification costs by combining these two parametric models with the descriptive model of principal components analysis (PCA). The results show that the LR and the CDA can predict bank failure accurately. In addition, the results of the PCA show the importance of asset quality, capital adequacy and liquidity as indicators of the bank’s financial conditions. We also compared the performance of two non-parametric methods, the classification and regression trees (CART) and the newly multivariate adaptive regression splines (MARS) models, in the prediction of failure. A hybrid model combining ’K-means clustering’ and MARS is also tested. We seek to model the relationship between ten financial variables (CAMEL’s ratios) and the default of a US bank. The comparative approach has highlighted the supremacy of the hybrid model in terms of classification. In addition, the results showed that the capital adequacy variables are the most important for predicting the bankruptcy of a bank. Finally, we studied the determinants of non-performing loans from European Union banks during the period 2012-2015 by estimating a fixed effects model on panel data. Depending on the availability of data we have chosen a set of variables that refer to the macroeconomic situation of the country of the bank and other variables specific to each bank. The results showed that public debt, loan loss provisions, net interest margin and return on equity positively affect non performing loans, while the size of the bank and the adequacy of capital (EQTA and CAR) have a negative impact on bad debts.

PhD (Geography) / Department of Geography and Geo-Information Sciences / Malaria is a climate-change concatenated biological hazard that may, like any other natural hazard, can lead to a disaster if there is a failure in handling emergencies or risks. A holistic solution for malaria mitigation can be provided when indigenous knowledge is complemented with scientific knowledge. Malaria remains a challenge in South Africa and Limpopo province is the highest burdened malaria-endemic region. Specifically, Vhembe District is the highest burdened followed by Mopani District (Raman et al., 2016). This research sought to mitigate malaria transmissions in Mopani District through the integration of indigenous and scientific knowledge. The study was carried out in Mopani District of South Africa and 4 municipalities were involved. These are Ba-Phalaborwa, Greater Tzaneen, Greater Letaba, and Maruleng. A pragmatism philosophy was adopted hence the study took a mixed approach (sequential multiphase design). Data was collected from 381 selected participants through in-depth interviews, a survey and a focus group discussion. Participants for the in-depth interviews were obtained through snowballing and selected randomly for the survey, while for the focus group discussion purposive sampling was used. The study applied constructivist grounded theory to analyze qualitative data and to generate theory. Statistical Package for Social Sciences version 23.0 was used for quantitative data. Based on empirical findings, it was concluded that temperature and rainfall among other various factors exacerbate malaria transmission in the study area. Results of the study also show that people in Mopani District predict the malaria season onset by forecasting rainfall using various indigenous knowledge based indicators. The rainfall indicators mentioned by participants in the study were used in the developed early warning system. An Early warning system is an essential tool that builds the capacities of communities so that they can reduce their vulnerability to hazards or disasters. In the design of the system, Apache Cordova, JDK 1.8, Node JS, and XAMPP software were used. The study recommends malaria management and control key stakeholders to adopt the developed early warning system as a further mitigation strategy to the problem of malaria transmission in Mopani District. / NRF

本研究分析我國49家本國銀行、55家信用合作社、287家農會信用部及27家漁會信用部等四類金融機構之經營現況，並參照各國金融預警制度運作方式，選取適合的財務比率，運用SAS統計軟體及Z-score、Logistic等模型，分別找出造成各類金融機構經營失敗之顯著相關財務比率，評估各類金融機構之經營效率、失敗機率與模型之正確區別率，以建立預測金融機構失敗機率之預警模型。研究之樣本資料分別為：本國銀行49家、2001年第2季~2003年底共計11季25項財務比率，信用合作社55家、1998年底~2003年底共計21季26項財務比率，農會信用部287家1998年底~2003年底共計21季25項財務比率，漁會信用部27家1998年底~2003年底共計21季25項財務比率。 本研究之結論為： 一、彙整Z-Score模型對各類金融機構具有顯著性之財務變數，本國銀行有6項、信用合作社有7項、農會信用部有6項，漁會信用部有4項。 二、彙整Logistic模型對各類金融機構具有顯著性之財務變數，本國銀行、信用合作社各有6項，農會信用部有5項，漁會信用部有4項。 三、金融預警模型中，Logistic模型較Z-Score模型有較高的正確區別率。 / This research analyzes 49 domestic banks, 55 credit cooperative unions, 287 credit department of farmer associations and 27 credit department of fisherman associations above four kind of financial institution´s management situation, and refers the operation ways of various countries financial early warning system, selects suitable financial ratios , utilizes SAS statistics software and Z-score, Logistic models, it identifies the root cause of bankruptcy thus reveals finance of ratio the correlation, appraises management efficiency, the defeat probability each kind of financial institution if the correct difference rate. It appraises each kind of financial institution´s management efficiency, defeats probability and correct difference rate. It establishes early warning model that forecasts financial institutions failure rate. The research model and period: used 49 domestic banks from 2001 in 2nd season to the end of 2003 total 11 seasons and 25 items of finance ratio、55 credit cooperative associations from the end of 1998 to the end of 2003 total 21 seasons and 26 items of finance ratio、287 credit department of farmer associations and 27 credit department of fisherman associations from the end of 1998 to the end of 2003 total 21 seasons which used respectively 25 items of finance ratio. The conclusion of this research are: Firstly, it collects the entire Z-Score model to have significant financial indicator to each kind of financial institution, the domestic banks have 6 items, the credit cooperative associations have 7 items, the credit department of farmer associations have 6 items, and the credit department of fisherman associations have 4 items. Secondly, it collects the entire Logistic model to have significant financial indicator to each kind of financial institution, the domestic banks and the credit cooperative associations have 6 items respectively, the credit department of farmer associations have 5 items, and the credit department of fisherman associations have 4 items. Thirdly, in the financial early warning model, when comparing Z-Score with Logistic model , the latter appears to have a higher correct difference rate.