This work has been prepared for the purpose of presenting the methodology and uses of the Monte Carlo simulation technique as applied in the evaluation of investment projects to analyze and assess risk. In the deterministic appraisal the basic decision rule for a project is simply to accept or reject the project depending on whether its net present value (NPV) is positive or negative, respectively. Similarly, when choosing among alternative (mutually exclusive) projects, the decision rule is to select the one with the highest NPV, provided that it is positive. Recognizing the fact that the key project variables (as: volume of sales, sales price, costs) are not certain, an appraisal report is usually supplemented to include sensitivity and scenario analysis tests. Both tests are static and rather arbitrary in their nature. During the simulation process, random scenarios are built up using input values for the project's key uncertain variables, which are selected from appropriate probability distributions. The results are collected and analyzed statistically so as to arrive at a probability distribution of the potential outcomes of the project and to estimate various measures of project risk. PACS. 02.70.Lq Monte Carlo and statistical methods The risk analysis processThe risk analysis process consists of the following steps [1]:• Developing a conceptual model of the problem under study. This involves the creation of a forecasting model, which defines the mathematical relationships between numerical variables that relate to forecasting the future. For example we can consider simple cash flow model: Cash flow = (revenues -costs-deprecation) × (1-tax rate)+deprecation. • Building the simulation model.This includes selection of key project variables and determining their probability distributions. • Verification and validation of the model.Verification refers to the process of ensuring that the model is free from logical errors. Data validity includes ensuring that all input data and probability distributions are truly representative of the system being modeled. • Performing the experiments -generation of random scenarios based on assumption set.• Analysis of the results.The output of the risk analysis process is a probability distribution of all possible project returns. The basic decision rule for a project appraisal using certainty equivalent values as inputs and discounted at a rate adjusted for risk is simply: to accept or reject the project depending on whether its NPV is positive or negative, respectively. Investment criteria for a distribution of NPVs are not always as clear as this. In deterministic case risk is usually accounted for by including a risk premium in the discount rate. Brealy and Mayers [2] have argued that the most appropriate discount rate to use in a project appraisal based on the Monte Carlo simulation is the risk free interest rate because any other discount rate would "pre-judge risk" in a project. Another school of thought maintains that the discount rate should include a premium for syste...
W artykule skupiono się na badaniu elastyczności polskiego rynku pracy w latach 2002-2012. Na wstępie omówione zostały pojęcie i rola deregulacji rynku pracy we współczesnej gospodarce. Przedmiotem zainteresowania było zbadanie skali oraz dynamiki elastyczności polskiego rynku pracy, którą opisywano za pomocą 10 wskaźników cząstkowych. Badanie przeprowadzono za pomocą trzech miar syntetycznych i porówny-wano zmiany ich wartości w czasie.Słowa kluczowe: rynek pracy, elastyczność rynku pracy, zmienna syntetyczna, syntetyczne miary rozwoju.
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