Saving plays an important role in economics. Private Pension System (PPS) helps individuals make savings and contribute to the nation's economy. Therefore, it is important to know the performance of Private Pension Companies (PPCs) that manage the pension funds. Data Envelopment Analysis (DEA) is one of the useful nonparametric techniques to measure the relative efficiency of Decisions Making Units (DMUs) for a specific time or process. In this study, relative efficiency scores of PPCs are investigated for a time interval with Dynamic DEA and compared with the traditional DEA. Inputs of the model are considered as the number of workers, total assets; and the outputs of the model are the number of contracts, total contribution and the market share of each PPC. Also, to ensure the dynamic procedure, Shareholders' Equity is used as a quasi-fixed input data to link consecutive periods in the time interval. Our results demonstrate that the efficiency score can be improved by considering the effects of the inter-relations of the consecutive periods. The implications arising from the results of the study are important for the companies' policies.

The paper compares portfolio optimization with the Second-Order Stochastic Dominance (SSD) constraints with mean-variance and minimum variance portfolio optimization. As a distribution-free decision rule, stochastic dominance takes into account the entire distribution of return rather than some specific characteristic, such as variance. The paper is focused on practical applications of the portfolio optimization and uses the Portfolio Safeguard (PSG) package, which has precoded modules for optimization with SSD constraints, mean-variance and minimum variance portfolio optimization. We have done in-sample and out-of-sample simulations for portfolios of stocks from the Dow Jones, S&P 100 and DAX indices. The considered portfolios’ SSD dominate the Dow Jones, S&P 100 and DAX indices. Simulation demonstrated a superior performance of portfolios with SD constraints, versus mean-variance and minimum variance portfolios.

A stock market index gives some illustrative information regarding the financial market. In this study, we are interested in stock indices efficiency of OECD member countries. We use Data Envelopment Analysis (DEA) methodology and Second Order Stochastic Dominance (SSD) Criteria as an efficiency metrics. DEA is a linear programming based technique for measuring the relative efficiency of homogenous decision making units by their input-output rates. In the Risk-Based DEA, traditional and modern risk measures are used as inputs of the model and the mean return as an output. We consider Conditional Value at Risk (CVaR) as a modern risk measure of financial asset returns. Another approach for the efficiency is Stochastic Dominance (SD) rule that takes into account the entire distribution of return, rather than the return distribution characteristics. There are several papers show that SSD constraints related to the CVaR constraints in an optimization model. Therefore, we compare Risk-Based DEA results with optimization problem with SSD constraints in the empirical study. We also test SSD efficiency of stock index pairs. The results are valuable for the asset managers who need to evaluate the performance of a stock index among others.

Finansal kurumların yatırım getirilerine ait piyasa riskini uluslararası platformda Riske Maruz Değer (VaR) ile raporlamaları beklenmektedir. VaR belirli bir güven düzeyinde bir getiri dağılımında kayıpları içeren kuyruğa kesme noktası olarak yaklaşırken, tutarlı bir risk ölçüsü olma özelliklerini sağlayan Koşullu VaR (CVaR) ortalama değer alarak, dağılımın kuyruğuna odaklanmaktadır. Bu çalışmada, beş farklı kriptoparaya ait getirilerin piyasa riskleri daha önceden çoğunlukla hisse senedi piyasasında başvurulduğu görülen VaR ve CVaR ölçülerinin parametrik ve parametrik olmayan yaklaşımlarıyla ölçülmeye çalışılmıştır. Analizden elde edilen sonuçlarda, kriptopara birimleri arasında genel olarak Ripple’ın en yüksek piyasa riskine sahip olduğu, Bitcoin’in ise en az riskli kriptopara birimi olduğu görülmektedir. Geriye dönük testlerle sınanan sonuçlara göre, parametrik olmayan CVaR yaklaşımının VaR’a ve parametrik yaklaşıma göre öngörülebilir bir risk ölçümü sağlandığı görülmektedir. Farklı yatırım araçları kullanılarak oluşturulan minimum riskli portföylerde kriptoparaların portföy ağırlığı arttıkça portföy getirisinin de arttığı gözlenmektedir. Bu sonuç kriptoparaların portföy çeşitlendirmesinde kullanılabileceğini göstermektedir. Sonuçlarımız yatırımcılar ve araştırmacılara, bir yatırım aracı olarak kriptopara birimlerinin kayıplara karşı duyarlılık seviyelerine ilişkin nicel bilgi sağlamaktadır.