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Determinants of Mongolian Economic Growth
Published May 2, 2018

Mongolia is the second largest landlocked country, which has unique economic condition. This paper aims to examine Mongolian economic growth from 2000 until 2016 and identify its determinants. The growth was studied based on the growth rate of National Domestic Product. Initially, 20 macroeconomic variables are chosen and tested for the economi...c growth determinators such as; unemployment rate, human capital index, import growth, inflation rate, export growth, and interest rate, etc. The results showed that the growth rate of dollar exchange, inflation rate, and the growth rate of export were the main factors (81.4%). Mongolian GDP per capita and poverty rate were compared with other Asian lower-middle-economies, which are classified in the same classification as Mongolia. An increment of average salary was adjusted by the inflation rate, which showed the purchasing power declined in 2015. Statistics of Central Bank of Mongolia, Central Intelligence Agency, World Bank’s statistics, and the statistics from National Statistics Office of Mongolia are used for the research.

JEL Classification: H0, H30, H6, H70

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Determinants of credit access of cocoa farmers
Published December 31, 2019

Access to credit is one of the critical areas that are of prime interest to development practitioners, agribusiness entrepreneurs and agricultural economists, mainly access to credit by farmers in order to increase their production and also reduce poverty. This study sought to analyze the determinants of credit access among cocoa farmers in the... Asunafo North of the Ahafo Region of Ghana. The multistage sampling procedure was used to collect data from 100 cocoa farmers with the aid of a questionnaire. Sources of credit, factors influencing access to credit, and constraints to credit were analyzed with the aid of descriptive statistics, multiple linear regression, and Kendall’s coefficient of concordance respectively. The results of multiple linear regression revealed that, age, marital status, education, experience, and family size were significant factors that influenced access to credit. The constraints analysis with the aid of Kendall’s coefficient of concordance showed that, high interest rate was highly ranked with a mean score of 1.93 whilst the need for a guarantor was least ranked with a mean score of 7.40. Based on the results, the study recommended that a policy aimed at expanding formal and semi-formal financial institutions credit portfolio to embrace cocoa farmers by finding alternative to collaterals and also reducing the interest rate will improve credit access with a positive externality effect of poverty reduction among cocoa farmers in the study area.

JEL Classification: Q14

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Systematic risk factors and stock return volatility
Published June 30, 2017

This study analyzes the transmission of systematic risk exhaling from macroeconomic fundamentals to volatility of stock market by using auto regressive generalized auto regressive conditional heteroskedastic (AR-GARCH) and vector auto regressive (VAR) models. Systematic risk factors used in this study are industrial production, real interest ra...te, inflation, money supply and exchange rate from 2000-2014. Results indicate that there exists relationship among the volatility of macroeconomic factors and that of stock returns in Pakistan. The relationship among the volatility of macroeconomic variables and that of stock returns is bidirectional; both affect each other in different dynamics.

JEL code: C32, C58, G11, G12

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Commodity Indices Risk and Return Analysis Against Libor Benchmark
Published December 13, 2018

This study analyze the risk and return characteristics of commodity index investments against the LIBOR benchmark. Commodity-based asset allocation strategies can be optimized by benchmarking the risk and return characteristics of commodity indices with LIBOR index rate. In this study, we have considered agriculture, energy, and precious metals... commodity indices and LIBOR index to determine the risk and return characteristics using estimation techniques in terms of expected return, standard deviation, and geometric mean. We analyzed the publicly available daily market data from 10/9/2001 to 12/30/2016 for benchmarking commodity indices against LIBOR. S&P GSCI Agriculture Index (SGK), S&P GSCI Energy Index (SGJ), and S&P GSCI Precious Metals Index (SGP) are taken to represent each category of widely traded commodities in the regression analysis. Our study uses time series data based on daily prices. Alternative forecasting methodologies for time series analysis are used to cross-check the results. The forecasting techniques used are Holt-Winters Exponential Smoothing and ARIMA. This methodology predicts forecasts using smoothening parameters. The empirical research has shown that the risk of each of the commodity index that represents agriculture, energy, and precious metals sector is smaller compared to its return, whereas LIBOR based interest rate benchmark shows higher risk compared to its return in recession, non-recession and overall periods.

JEL Classification: C43, G13, G15

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