Search

PurposeThe purpose of this paper is to explore the effect of monetary policy on bank risk in the banking system in some MENA countries. It explores how some economic and credit indicators affect the level of risk in the banking sector. It combines many factors that could affect banks’ risk appetite such as macroeconomic conditions, banks’ credit size and lending growth. The authors use nonperforming loans as a proxy for banking sector risks. At first, the authors have analyzed the linear relationship between monetary policy and credit risk. As mentioned above, nonlinearity is expected in the underlying relationship, and, thus, they have investigated the nonlinear relationship to deeply analyse the relationship using the dynamic panel threshold model, as stimulated by Kremeret al.(2013). Threshold models have gained a great importance in economics and finance for modelling nonlinear behaviour. Threshold models are useful in showing the turning points in the behaviour of financial and economic indicators. This technique has been applied in this study to study the effect of monetary policy on credit risk.Design/methodology/approachThis paper is divided into the following sections: Section 2 which previews the recent literature; Section 3 which includes some stylized facts about the relationship between credit risk and monetary policy; Section 4 which deals with the model and methodology; Section 5 which handles the data sources and discusses the results, and finally Section 6 which is the conclusion. The paper adopts dynamic panel threshold model of Kremeret al.(2013).FindingsThe results show that the relationship between monetary policy and credit risk is positive and significant to a certain threshold, 6.3. If the lending interest rate is higher than 6.3, this increases the credit risk in the banking sector, because increasing the lending interest rate imposes huge burdens on the borrowers, and, therefore, the bad loans and nonperforming loans become more likely. Thus, the MENA countries need to decrease the lending interest rate to be less than 6.3 to reduce the effect of monetary policy on credit risk. Further, these results are qualitatively robust regarding the inclusion of additional control variables, using alternative threshold variables and further endogeneity checks of the credit risk, such as Risk premium and the squared term of the lending interest rate. The results of taking the risk premium and the squared term of the lending interest rate as a threshold served the analysis and confirmed the positive relationship between monetary policy and credit risk above a certain threshold. As for the risk premium, the relationship below the threshold was negative and significant. Other related research points might be a good avenue for the future research such as applying this approach to micro data of banks from different MENA countries. Also, more sophisticated approaches like time-varying panel approach to assess the relationship over the time can be applied.Originality/valueThe importance of this paper lies in the fact that it does not only study the effect of time, but it also focuses on the panel data about some economic and credit indicators in the MENA region for the first time. This is because central banks in the MENA region have common characteristics and congruous level of economic growth. Therefore, to study how the monetary policy affects those countries’ credit risks in their lending policies, this requires careful analysis of how the central banks in this region might behave to control default risks.

Purpose This paper aims to explore the extreme effect of crude oil price fluctuations and its volatility on the economic growth of Middle East and North Africa (MENA) countries. It also investigates the asymmetric and dynamic relationship between oil price and economic growth. Further, a separate analysis for each MENA oil-export and oil-import countries is conducted. Furthermore, it studies to what extent the quality of institutions will change the effect of oil price fluctuations on economic growth. Design/methodology/approach As the effect of oil price fluctuations is not the same over different business cycles or oil price levels, the paper uses a panel quantile regression approach with other linear models such as fixed effects, random effects and panel generalized method of moments. The panel quantile methodology is an extension of traditional linear models and it has the advantage of exploring the relationship over the different quantiles of the whole distribution. Findings The paper can summarize results as following: changes in oil price and its volatility have an opposite effect for each oil-export and oil-import countries; for the former, changes in oil prices have a positive impact but the volatility a negative effect. While for the latter, changes in oil prices have a negative effect but volatility a positive effect. Further, the impact of oil price changes and their uncertainty are different across different quantiles. Furthermore, there is evidence about the asymmetric effect of the oil price changes on economic growth. Finally, accounting for institutional quality led to a reduction in the impact of oil price changes on economic growth. Originality/value The study concludes more detailed results on the impact of oil prices on gross domestic product growth. Thus, it can be used as a decision-support tool for policymakers.

This paper assesses several early warning (EWS) models of financial crises to propose a model that can predict the incidence of a currency crisis in developing countries. For this purpose, we employ the equal weighting (EW) and dynamic model averaging (DMA) approaches to combine forecast from individual models allowing for time-varying weights. Taking Egypt as a case study and focusing only on currency crises, our findings show that combined forecast (EW- and DMA-based EWS), to account for uncertainty, perform better than other competing models in both in-sample and out-of-sample forecasts.

Inflation forecasts are highly important in the actual management of monetary policy implying that central banks must acquire accurate inflation forecasts. Given the high risks involved in inflation, models that account for uncertainty are expected to do well in terms of forecasting power. The current paper focuses on estimating 12 different specifications including 10 univariate models and 2 multivariate models. The univariate models are generalized autoregressive conditional heteroskedasticity in mean (GARCH-M) and threshold autoregressive generalized conditional heteroskedasticity in mean (TARCH-M) models assuming three different distributions for the error term as well as an extension of the GARCH-M model that allows for time-varying higher order moments. Furthermore, we employed three models that take into consideration the possibility of structural shifts, namely, Markov switch, threshold autoregressive and time-varying coefficients (TVCs) models. The multivariate models are the vector- half operato...

The aim of this study is to explore the asymmetric effect of the unanticipated monetary policy shocks on inflation and real production. This is done by utilizing the non-linear autoregressive distributed lag (ARDL) model. Nonlinear ARDL allows us to check for the asymmetric effect of the policy in terms of the size of the shocks: large or small, and the stance of the shocks: loose or tight. Additionally, Wald test is employed to confirm the underlying relationship. The results provided a sufficient evidence for the idea of the asymmetric effect of monetary policy in Egypt. Firstly, regarding the size of the policy, only small shocks have a considerable effect on both inflation and real production. Secondly, regarding the direction of the policy, only positive shocks have a considerable influence on both variables. Wald test confirmed the abovementioned results. Therefore, based on these results, the monetary policy in Egypt is only effective under some circumstances and thus other economic policies such as fiscal policy might be more effective in these cases.

The paper aims at examining an augmented version of Fisher hypothesis that include inflation instability. According to this hypothesis, there is a positive relation between interest rates and expected inflation. In contrast, there is a debate regarding the impact of inflation uncertainty on interest rate. According to the portfolio theory and models of asset pricing, inflation instability positively affects the interest rate. The reason is that risk-averse investors must be compensated with higher returns for higher risks. In contrast, the loanable funds theory implies a negative impact of inflation instability and interest rates since high uncertainty leads consumers to protect themselves against inflation by raising their savings which lowers consumption and interest rates. To compute inflation volatility, we applied different Autoregressive Conditional Heteroscedasticity models. The simple and augmented versions of Fisher hypothesis are examined using Markov Switch Model to account for possible regime shift in that relationship. For the original Fisher hypothesis, there is an evidence of supporting it in the first regime while that hypothesis does not hold in the second one. In the augmented version of Fisher hypothesis, portfolio theory hypothesis is verified in the first regime whereas the loanable funds hypothesis is confirmed in the second one.

This paper aims at improving the prediction accuracy through using combining forecasts approaches. In forecast combination, the crucial issue is the selection of the weights to be assigned to each model. In addition to traditional methods, we propose, also, two sophisticated approaches. These suggested methods are modified Bayesian Moving Average (BMA) and Extended Time-varying coefficient (ETVC). The first technique is based on merging the traditional BMA with other frequentist combination schemes to avoid the subjective prior inside the traditional Bayesian technique. The suggested ETVC approach provides consistent time-varying parameters even if there are some measurement errors, omitted variables bias and if the true functional form is unknown. Concerning the included models, we consider both linear and nonlinear models in order to calculate the forecasts of quarterly Egyptian CPI inflation. We find that our proposed scheme ETVC is superior to the best model and all other static combination schemes including the time-varying scheme based on the random walk coefficients updated (TVR) approach. Additionally, the suggested modified Bayesian approach improves the traditional BMA and overcomes the problem of depending on the arbitrary choice for the initial priors.