Journal of Money & Economy
Volume:9 Issue: 1, Winter 2014

This paper presents an optimal portfolio selection approach based on value at risk (VaR), conditional value at risk (CVaR), worst-case value at risk (WVaR) and partitioned value at risk (PVaR) measures as well as calculating these risk measures. Mathematical solution methods for solving these optimization problems are inadequate and very complex for a portfolio with high number of assets. For these reasons, a combination of particle swarm optimization (PSO) and genetic algorithm (GA) is used to determine optimized weights of assets. Stocks’ Optimized weight results show that proposed algorithm gives more accurate outcomes in comparison with GA algorithm. According to back-testing analysis, PVaR and WVaR overestimate risk value while VaR and CVaR give a rather accurate estimation. A set of companies in Tehran Stock Exchange are considered as a case study for empirical analysis.

Quality Growth Index (QGI) is affected by two sets of combined-structural and social indicators. Structural indicator contributes to achieve the main target of sound-sustainable-competitive output growth. By the way, the sound output growth should enhance social-public services and living standards. Although QGIs are weightedly computed based on different scenarios, the trend of the QGIs and coefficient of variation of the QGIs indicate the robustness of results. The correlation among QGI and social sub-components highlights a positive relationship between QGI and school enrolment, per capita income and public spending on education and health. The result of co-integration model indicates that higher government size and devaluation of local currency have evidently exacerbated QGI. Meanwhile, openness and inflation underscore the positive long-run impact over QGI. Vector error correction equation outlines that about 84 percent of a short-term shock to the co-integrating vector will be absorbed in the first period. In this context, the impulse response of the QGI to the exchange rate and government size shocks are diminishingly and negatively permanent while the response of the QGI to the openness shock is significantly and positively permanent.

Asset-liability mismatch in balance sheet of banks shows serious challenges in banks because of the traditional methods of recording assets and liabilities at book value in Iran. The Central Bank of the country motivated and advised banks to take concrete steps in minimizing the mismatch in the asset-liability composition. This paper attempts to suggest a micro funded framework that can evaluate the role of asset and liability management in banking sector in business cycles through a DSGE model. In this paper, we use Bayesian method to estimate parameters and use national account and balance sheet data from 1981 to 2013. Results show that tightening monetary policy decreases the cost of ALM. On the other hand, raising required reserve requirement increases the cost of asset and liability management; technology shock leads to decrease of asset and liability management cost, and the costs of ALM affects interest rate. Then, the increase of the cost of ALM leads to increase of interest rate.

There are two sources for governments to raise their revenues. The first is the direct taxation levied on output, and the second is seigniorage. Seigniorage is also known as printing new money and is defined as the value of real resources acquired by the government through its power of sovereignty on its monopoly of printing money. The purpose of this paper is to examine the Laffer curve for Seigniorage in the economy of Iran through data-set collected from the statistical books of the central bank of Iran related to the time period 1979-2010. For this purpose, we use a methodology that is based on the Fuzzy C-Means algorithm that is widely used in the context of pattern recognition, and the Takagi-Sugeno approach which is proper for modeling fuzzy systems. This methodology is exceptionally flexible and provides a computationally tractable method of dealing with non-linear models in high dimensions. Our findings support a standard Laffer curve shape in Iran. In other words, it will be concluded through empirical results that there is a nonlinear relationship between seigniorage and inflation for the economy of Iran in the time period studied in this paper.

The purpose of this study is to find the relationship between lending to Small and Medium-size Exporter Enterprises (E-SMEs) and the use of Basel II Capital Accord for the first time in the banking system of Iran. Results showed that 96.69 percent of small firms were in the very low risk category of credit portfolio. This proof explains a consistent and balanced relationship between risk- weighted assets distribution system (RWA) in Basel II Capital Accord and firms’ size. In other words, the smaller the size of the firm, the smaller their risk-share in the credit portfolio would be. Furthermore, according to the results found by Probit regression with an endogenous covariate, the higher ratings the firm recovers, the less risk-share in bank’s portfolio the firm will enjoy. Thus,it is indispensable that banks, chiefly specialized banks, should take action towards the allocation of parts of the credit portfolio to SME exporter financing. Accordingly, it is essential to design particular credit scoring models for these firms.

One of the most important methods employed to measure the market risk is value at risk calculation method. In this study, the value at risk of banks listed on the Tehran Stock Exchange and Over-the-counter (OTC) are calculated using parametric model, Monte Carlo simulation, historical simulation and Two-Sided Power (TSP) Distribution. The sample includes all listed banks in Iran. The results showed that the value at risk estimated by TSP and historical models is more accurate than the VaR estimated by Monte Carlo and GARCH models. TSP model and then historical model are more accurate than the other ones. Moreover, GARCH is the least accurate model. So far, no research has been conducted to investigate all four models of value at risk assessment.