Theoretical Channels Of International Transmission During The Subprime Crisis To OECD Countries: A FAVAR Model Under Bayesian Framework

This paper studies whether and how U.S. shocks are transmitted to other OECD economies in the case of the Subprime Crisis. Using a large data set of 119 major financial and macroeconomic variables in 17 OECD countries from 1980:Q1 to 2006:Q2, we characterize the transmission channels by the interpretable factors and make a structural analysis using FAVAR models under a Bayesian approach. Our main findings suggest that differences exist in the contagion effects. This implies that no generalizations can be made for OECD countries even of equal economic size and in the same geographic region. Our results show that a large portion of the variance of domestic economic variables is explained by global factors and that the interest rate shock appears to play an important role in the spillover mechanism from the U.S to the OECD countries

Determinants Of Foreign Direct Investment In MENA Region: Panel Co-Integration Analysis

This paper aims to investigate the relationship between Determinants of Foreign Direct Investment (FDI) inflows and their determinants in MENA (Middle East and North Africa) region during the period 1970- 2010. Using panel data techniques, we take into account the both hypothesis economic dependencies and structural breaks. We find that the macro determinants like openness, growth rate, exchange rate, and economic instability have a long-run impact on FDI inflows in our panel

Dynamic integration and transmission channels among interest rates and oil price shocks

This paper examines the short term dynamic integration among oil price shocks and interest rates for the U.S.A, Euro area and twelve Asian economies from August 1999 to January 2018 using a Time-Varying Parameter Vector Autoregression (TVP-VAR) with stochastic volatility. First, we found convincing evidence of time variation in the co-movement of interest rates and oil shocks and that the integration levels were highest towards the 2001 financial crisis whereas there is an evidence of decoupling as shown by notable drop in the the level of integration during the 2007-2009 economic and Euro-debt crises. In descending order, Singapore, crude oil, Hong Kong, Philippines and the United States are the net-transmitters of shocks while India, Japan and Vietnam are net-receivers. Results from a sub-sample containing highly industrializing economies in Asia, the United States, Euro area and crude oil market suggest that Singapore, Hong Kong and the United States remained top transmitters of shocks whereas the Euro-area, Taiwan, Korea and crude oil market become net receivers of shocks. Results from the analysis of transmission channels suggest that higher integration for the full sample tend to be driven by increasing levels of external exposure through trade and financial linkages, information asymmetry and political stability while financial crisis reduces the level of integration. Lastly, among the highly industrialized markets, time varying integration is also driven by the degree of external exposure as well as both political and financial stability. We also document important policy implications of our findings

Modeling of business failure prediction by statistic and dynamic approaches : neural networks, Bayesian networks, duration and dichotomous models

L'objectif de cette thèse est d’étudier différentes méthodes de prévision de la défaillance d'entreprises aussi bien en statique qu'en dynamique. Plus précisément, dans l'approche statique, nous avons recouru aux méthodes de sélection des variables discriminantes en utilisant les réseaux de neurones. Nous avons ainsi proposé deux nouvelles procédures relevant de ces méthodes.La première, fondée sur le critère HVS, intitulée HVS-AUC, nous a permis i) de construire un modèle plus parcimonieux par rapport à l’ADL ; ii) de dégager un ensemble de variables stables à la fois non conjoncturelles et avec un fort pouvoir explicatif. A l'inverse, la seconde technique est basée sur la procédure forward ou plus exactement sur forward-AUC. Cette méthode fait apparaître des résultats comparables à l'ADL mais avec moins de variables explicatives. Elle permet notamment de détecter les ratios jugés les plus pertinents selon ADL et HVS-AUC.Nous avons de plus utilisé des méthodes d'apprentissage de structure de réseaux bayésiens pour essayer d'améliorer la performance de classification des entreprises. Nous avons mobilisé une technique intitulée "Max-Min Hill-Climbing" ou MMHC. Nous avons analysé les performances de classification d'un algorithme combiné entre MMHC et le modèle de base d'un réseau bayésien naïf (BN). Cette nouvelle méthode a été nommée BN-MMHC (Bayes naïf augmentée par MMHC). Les résultats obtenus confirment néanmoins l'opinion dominante : pour ce qui est du pouvoir discriminant, aucune structure ne semble à même de concurrencer BN de manière significative.Dans la deuxième approche dynamique, nous avons mis plus l'accent sur les facteurs non mesurables a priori et sur des facteurs explicatifs impossibles à appréhender dans un cadre statique. Nous avons mobilisé dans un premier volet les variables macroéconomiques pour mieux estimer le risque de défaut. Dans un second volet, nous avons utilisé une modélisation alternative permettant d'appréhender correctement les chocs que peuvent subir les entreprises au cours du temps. De ce fait, nous avons évalué ainsi l'effet de la propagation de ces chocs.The objective of this thesis is to study bankruptcy prediction models from both static and dynamic viewpoints. More precisely, in the static approach, we used the methods of selecting discriminating variables using the neural networks. We thus proposed two new procedures relating to these methods. The first one is based on the criterion HVS called HVS-AUC and allowed to 1) build a more parsimonious model compared to the LDA, 2) identify a set of variables both static and non-cyclical with a strong explanatory power. Conversely, the second technique is based on the forward procedure, more precisely on forward-AUC. This method shows results comparable to the LDA but with fewer variables. It allows the detection of ratios considered as the most relevant according to LDA and HVS-AUC. We have also used methods of structure learning of Bayesian networks to improve the performance of classification of firms. We have mobilized a technique called "Max-Min Hill-Climbing" or MMHC. Specifically, we plan to analyze the performance of classification of an algorithm that mixes both MMHC and the canonical model of a naive Bayes network (NB). This new method could be called NB-MMHC (naive Bayes augmented by MMH C). The results confirm the prevailing view: as for the discriminatory power, no structure seems to be able to significantly compete with NB. In the second dynamic approach, we put more emphasis on factors not measurable a priori and also on explanatory factors impossible to capture within a static framework. In the first phase, we used the macroeconomic variables to better estimate the risk of default. In the second part, we used an alternative model to better estimate the shocks that firms could undergo over time. We therefore evaluate the propagation effects of theses shock

Detecting Contagion Effects during the Subprime Crisis Using Different VAR Size Models: Comparison between OLS and Bayesian Shrinkage

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ASSESSING THE KNOWLEDGE OF SMALLPOX AND MONKEYPOX VIRUS AMONG THE UNIVERSITY OF TLEMCEN MEMBERS IN THE WAKE OF COVID-19: A 2023 CROSS-SECTIONAL STUDY.: a Cross-sectional Study

Background: A recently-surfaced virus called Monkeypox virus (MPXV) has gained widespread attention as it dominates the news, creating a sense of panic among people due to the potential threat it poses to their health. Materials and Methods: To evaluate knowledge about this virus and its disease, and to raise consciousness among the members of the Faculty of Natural and Life Sciences and Earth and Universe Sciences at the University of Tlemcen, we launched an online web-based survey for a twenty days’ period that contained sociodemographic and perceptiveness questions about the emergent virus, its disease, and vaccination. Results: Our findings showed that the majority of the respondents of our study have a satisfactory level of knowledge about this emerging virus and its disease. Moreover, most participants showed a positive attitude towards the vaccine, considering it the best preventive means to fight against Monkeypox disease. Conclusion: Although the MPXV may not become a pandemic, but knowing the various ways that contribute to its spread is essential to avoid any possibility of a new one, especially in Algeria

Stock Market Integration and Risk Premium: Empirical Evidence for Emerging Economies of South Asia

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Effect of 3,4-diaminopyridine and tetraethylammonium on the presynaptic blockade caused by beta-bungarotoxin.

In this paper we test for the existence of equity market contagion, originating from oil price fluctuations, to regional and domestic stock markets. The data are collected over the period from April 1993 to April 2015. We apply an empirical multifactor asset pricing model with three-factor setting to capture the unexpected return and disentangle simple correlation due to fundamentals and contagion. We investigate four regions: the European Monetary Union (EMU), Asia-Pacific (AP), the Non-European Monetary Union (NEMU) and North America (NA). We define contagion as the excess correlation that is not explained by fundamental factors. Oil price risk is shown to be a factor as important as contagion. In addition, oil price fluctuations amplify contagion in the context of regional markets strongly interlinked with the USA

Contagion and bond pricing: The case of the ASEAN region

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