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Le marché du crédit représente un des marchés financiers les plus implorants au monde. Trois éléments permettent de déterminer le niveau du risque de crédit : la probabilité de défaut, la valeur de la créance et la proportion de la créance qui ne sera pas recouvrée en cas de défaut. La réglementation prudentielle impose aux acteurs du marché de sévères restrictions en termes de gestion des risques et d'allocation de capital. Par conséquent, la question de l'évaluation du risque de crédit revêt une importance cruciale pour les banques, qui sont tenues d'effectuer une analyse approfondie du risque de crédit.

crédit --- risque de credit --- institution financière --- Sciences économiques & de gestion > Finance

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This thesis aims to contribute to the existing literature around the impact of political uncertainty on stock markets. The goal of this study is to identify the impact of political uncertainty due to Brexit during the post-referendum period and negotiations on the UK stock market’s both implied and conditional volatility. An analysis of the sectorial impact is also conducted. To do so, the approach consists of applying econometrics models on financial time series, which allows us to define whether an impact occurred during the period under analysis.
First, we analyze the general impact of political uncertainty due to Brexit on the implied volatility of the UK stock market, represented by the VFTSE index being the implied volatility of the FTSE 100 index. In order to represent political uncertainty in a quantitative way, positive and negative “event days” are identified by both looking at the number of news published in financial newspapers and their “color”. This helps us obtain two series of binary variables: one representing the increase of political uncertainty due to Brexit through unfavorable news and favorable news contributing to its decrease. The study of the impact is based on the cointegration theory and on the error correction model (ECM). Our model uses the cointegrated variables VFTSE and VSTOXX®, index of the implied volatility of the EURO STOXX® 50, as well as an exogenous variable representing political uncertainty caused by Brexit. The model is replicated as many times as there are binary variables accounting for this uncertainty, which permits to test for robustness of the model.
Secondly, in order to verify the impact on conditional volatility, we test the FTSE 350 index in a GARCH-X(1,1) model in which we integrate our series of political uncertainty. It is also through this type of model that we test the sectorial impact using the supersector indices of the FTSE 350. Once again, favorable and unfavorable news are analyzed separately to isolate the impact of each of the two categories of news.
The results of these analyses show a significant impact of political uncertainty due to Brexit on the UK stock market’s volatility during the post-referendum period. These analyses also confirm a different impact depending on the sector and on the “color” of the news.

Brexit --- Stock market --- Cointegration --- ECM --- Political uncertainty --- GARCH --- Volatility --- Sciences économiques & de gestion > Finance

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La microfinance congolaise a connu une ascension fulgurante en début des années 2000. Beaucoup plus rependue à l’Est du pays, et notamment dans le Nord-Kivu, elle connaît une chute brutale que d’aucuns ont très vite qualifié de « crise de la microfinance en RDC », la liant, par concomitance de survenance, à la grande crise financière qui a secoué les marchés financiers entre 2007 et 2008, voire 2009.
Au fond, les institutions de microfinance étaient en déphasage des époques et des pratiques. Nombreuses d’entre elles avaient – par effet de concurrence, de course obstinée à la rentabilité, d’inexpérience en matière de gestion des risques – multiplié les agences et des volumes massifs d’octrois de crédits ; sans en avoir préalablement mesuré l’impact, en terme de risque, sur leur portefeuille. La boite à pandore a vite explosé, entraînant des dizaines d’entre elles à disparaitre. Outre le faible encrage en terme de gestion des risques propres aux institutions de microfinance, l’épine dans l’œil de cette autre forme de business de la misère au Nord-Kivu aura aussi été son décalage d’avec le cadre règlementaire et une forte concentration en plein « économie de la guerre », octroyant des crédits à des microentreprises et individus, parfois très peu maîtrisés. Elles n’ont pas tardé à faire défaut, par effet de contagion ou tout simplement par déphasage vis-à-vis des normes prudentielles et volumes importants d’encours irrécouvrables.
Pour comprendre les faits et pouvoir suggérer une des voies de sortie, cette étude aborde une double échelle : une description analytique des causes internes de la crise dite de microfinance et propose un modèle de prédiction de défaut de crédit sur base des scores générés par la régression logistique. Les résultats montrent que les vraies causes de la crise ont été plutôt internes et surtout liées à la distorsion réglementaire. Congolese microfinance experienced a spectacular rise in the early 2000s. Much more prevalent in the east of the country, and particularly in North Kivu, it experienced a sharp drop that some quickly described as a "microfinance crisis in DRC ", linking it, by coincidence of occurrence, to the great financial crisis that shook the financial markets between 2007 and 2008, even 2009.
In essence, microfinance institutions were out of step with times and practices. Many of them faced – through competition, stubborn profitability, inexperience in risk management – multiplication of agencies and massive volumes of loans; without having previously measured the risk impact on their portfolio. The Pandora box quickly exploded, causing dozens of them to disappear. In addition to their weak risk management specific to microfinance institutions, the spine in the eye of this other form of misery’s business in North Kivu has also been its shift from the regulatory framework and a strong concentration in the "war economy", granting credits to micro-enterprises and individuals, sometimes very little controlled. They were soon defaults, due to contagion or simply out of step with prudential standards and large volumes of uncollectible loans.
To understand the facts and to be able to suggest one of the ways out, this study addresses a double scale: an analytical description of the internal causes of the so-called microfinance crisis and proposes a credit default prediction model based on logistic regression-generated scores. The results show that the real causes of the crisis were rather internal and especially related to regulatory distortion.

Crise financière --- Risque de défaut. --- Régression Logistique --- Scoring --- Microfinance --- Sciences économiques & de gestion > Finance

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Since time immemorial, humans have been playing games of exchanging goods and services, either to satisfy their needs, or to acquire what they do not have or to get rid of what they have in excess. These exchange games gave birth to the currency as we know today. There are a multitude of currencies which are most often associated with a state or geographical area. 
The development of trade between different states and geographical areas has resulted in the creation of a huge market, called foreign exchange market, where various currencies can be traded at a defined rate, referred as the exchange rate.
The exchange rate is a rate at which the currency of one state is exchanged for the currency of another. The exchange rate is considerably important for each country, as it has a direct impact on imports and exports between countries. A country with a strong exchange rate will tend to increase his import, because the price of foreign goods is cheaper. On the contrary, a low exchange rate will boost the country's exports.
The exchange rate is influenced by countless factors such as the current balance, monetary policy, etc. These factors will be one of the main subjects of our work. Indeed, it will mainly consist of a comparative study of the euro/dollar and yuan/dollar rate in order to determine whether these two rates are influenced by the same factors. In conjunction with the study of factors, we will study the predictability of these two rates, first through the factors influencing these rates, and then only through their past values. The study period extends from 2005, the year in which the Chinese Central Bank adopted a variable exchange rate policy against the dollar, to 2015, and the data collected are quarterly. This work will attempt to answer the questions formulated below:
1.Are the euro/dollar and yuan/dollar rates affected by the same factors?
2.Is it possible to accurately predict the future values of an exchange rate?
3.In an attempt to forecast an exchange rate, what is the best forecasting model between the model taking into account the factors significantly influencing this rate and the one accounting only past values?

exchange rate determinants --- currency --- exchange rate forecasting --- euro/dollar --- yuan/dollar --- Sciences économiques & de gestion > Finance

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In order to explain mainly the instantaneous and cumulative impulse responses of bank profitability on the negotiable debt securities market to innovation shocks related to profitability itself, the inflation rate and the key rate between 2008 and 2019 in the DRC, it was appropriate to apply econometric analyses via non-structural VAR modelling. Indeed, after estimating the model, the empirical results obtained confirm that inflation and key interest rates significantly cause, in the Granger sense, bank profitability for the period under study. In the same vein, the instantaneous impulse response function (IRF) analysis showed that profitability reacted first negatively and significantly and then positively to innovations in the inflation rate when the innovation shock is projected over time: the maximum negative impact being reached 2 weeks after the shock and the maximum positive impact 3 weeks after the shock before returning to balance after about 6 weeks. The (IRF) first shows a positive profitability response to the key rate before turning negative when the shock is projected over time: the maximum negative impact being reached 3 weeks after the shock and the maximum positive impact 2 weeks after the shock before returning to equilibrium after about 5 weeks. The (IRF) reveals that profitability reacts negatively to its own innovations. On the other hand, we can realize that the accumulated impulse response (AIR) shows a positive and significant profitability response to its own innovations and those of the key rate, whereas it reacted negatively to an innovation shock on the inflation rate before returning to balance.
In addition, the analysis of the decomposition of the variance of the VAR model's forecast error variance showed that the variance of the profitability forecast error was due to its own innovations averaging 80.1% and those of the key rate averaging 19.6%, as well as to innovations in the inflation rate averaging 0.32% over 10 periods, all in proportion. Finally, these analyses covered 18 banks operating in the DRC and a size of 516 observations at weekly frequency from April 2008 to September 2019.

Bank Profitability, DRC, Inflation rate, Key rate, Innovation, TCN, and VAR. --- Sciences économiques & de gestion > Finance