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This paper builds a dynamic computable general equilibrium model of the Moroccan economy that investigates the linkages that transmit the influence of remittances to households and economic sectors. The model shows that the linkage between the real estate sector and other sectors in a general equilibrium context gives interesting results. Given that the real estate sector uses intermediary inputs from other sectors, a drop of remittances will negatively affect the overall economy in contrast to the little impact such a drop will have in partial equilibrium. Hence, the channeling of investment from real estate to productive sectors is unexpectedly harmful. Positive effects arise only from an improvement in the country risk premium and from a reduction of international transfer costs.

Inefficient, or weak banks, are continuously pointed out as threatening financial stability as they are more likely to fail than efficient ones and thus to weaken the whole banking sector (BIS, 2002). Consequently, banks' efficiency analysis, a measure of bank's management efficiency, should be a useful tool for policy makers to predict bank failure. This paper tests the usefulness of efficiency analysis in predicting bank failure by using the 2001 Turkish banking crisis as a case study. It implements in a first step both parametric, Stochastic Frontier Analysis (SFA), and non-parametric, Data Envelopment Analysis (DEA), to predict Turkish banks' efficiency scores over 1996 to 2001. During this period, 19 out of 55 were taken over by the Turkish Saving and Deposit Insurance Fund (SDIF). In a second step, efficiency scores are tested against the standard CAMELS model to check their capacity in predicting banks' probability to fail, i.e. SDIF taken-over. The findings indicate that banks taken-over were less cost efficient than others. Moreover, banks which were lending to connected parties were also less cost efficient than others. On the methodology side, our results using a parametric approach are found to be more significant than the non-parametric ones.

Recent turmoil in financial markets endorses the need for rigorous handling and integration of asset liquidity risk into Value-at-Risk (VaR) models. In this work we develop and test measures of certain kinds of asset liquidity risk that is useful for completing the definition of market risk and for predicting liquidity-adjusted VaR under adverse market conditions. This paper presents a practical simulation framework for the modeling of asset liquidity risk for portfolios that consist of multiple long and short trading assets. We put forward a method whereby the holding periods are adjusted according to the specific needs of each trading portfolio by explicitly modeling a linearly-distributed liquidation scheme by means of a pertinent scaling multiplier. The empirical testing is achieved using daily return data of emerging Gulf-Cooperation-Council (GCC) stock markets. We simulate and analyze different relevant portfolios (of both long and short-sales trading positions) and determine the risk-capital exposure under varied illiquid and adverse market conditions.

We investigate the relationship between oil prices and manufacturing sector of a small open economy, Turkey. We take into account exogeneity of oil prices, extreme oil-reliance and import-dependence, as well as asymmetric responses of oil product prices to world crude oil price changes. We also control for the global liquidity and domestic finance conditions, along with real exchange rate dynamics in our VAR estimations. We report that while oil price increases do not significantly affect the manufacturing sector in aggregate terms, some sub-sectors are adversely affected.