5 research outputs found

    The Impacts Of Interest Expenditure On Income Distribution and An Application On The Factors Distorting Income Distribution: An Empirical Analysis For Turkey

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    Income inequality and poverty have emerged as the most important socio-economic problems for these times. Turkey, since the 1980s, meets public sector deficits by internal borrowing. To meet interest costs incurred for borrowing with tax revenues, which is an important source of the government, and gradually increase the share of the indirect taxes in tax revenues is an accurate indicator of an income transfer from public to the owners of capital. This study will focus on internal borrowing interest payments. The distribution of the recipients of internal borrowing, the share of interest payments in domestic debt stock of the state are evaluated, and by examining the course of interest payments over the years, will be focused on the effect on income distribution. In addition, the effect of the distribution of tax burden on income distribution will also be analyzed. And finally, in this study, the factors distorting the income distribution will be analyzed and the Topsis Model which is conducted over the period 2002-2015. Findings are in accordance with the Gini Coefficient results. Accordingly, income distribution indicated a more equitable distribution during the period when the domestic debt interest payments, unemployment rates, the share of indirect taxes, inflation and interest rates were decreasing

    Large portfolio optimisation approaches

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    This paper makes an empirical comparison of prominent methods in portfolio optimisation, such as nodewise regression, the sample covariance matrix, observable factor model-based covariance matrices, linear and nonlinear shrinkage methods, and principal orthogonal complement thresholding (POET) estimators. Empirically, we find that the nodewise regression approach that uses a direct estimator of the sparse inverse covariance matrix improves portfolio performance among existing methods in mean-variance portfolio optimisation when the number of stocks is greater than the number of observations
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