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Managing risk using real options in company’s valuation
125-132Views:186The valuation of company is very important because provides information about the current value/situation of company, and through this, provide the opportunity of choosing the best company’s growth alternatives. The future strategic decisions are characterized by lack of knowledge, information, so all measures of company’s growth are closely linked with uncertainty and risk. The company’s valuation process is also related with uncertainty and risk. The risk may result both from the assessed assets and the technique used. In literature, we could find three approaches for risk management: capital budgeting based method, methods based on portfolio analysis and real options approach of risk management. Among them, the real options based methods is the most revolutionary approach for risk management. The advantages of the method, consists in the fact, that the process of establishing strategic decisions integrates the possibility of reversibility, delay and rejections, which isn’t it possible at two previous methods. The method also takes into account the total risk of company, so both the company-specific and systematic risk. In this study, I have used one of the best-known real option based method, the Black-Scholes model, for determining the option’s value. Determination of option value is based on the data of enterprise, which was tested Monte Carlo simulation. One of the basic assumptions of the Black-Scholes model is that the value of option is influenced by several factors. The sensitivity of option’s value could be carried out with so-called “Greeks”.. In the study the sensitivity analysis, was carried out with indicators Delta (Δ), Gamma (Γ) and Vega (ν). The real options based risk management determinations were performed in the R-statistics software system, and the used modules are 'fPortofio' and 'mc2d'. By using of real options method, I have calculated the average value of company capital equal with 38.79 million. By using simulation was carried out 1000 runs. The results of this show a relatively low standard deviation, small interquartile range and normal distribution. In the calculation of indicator Delta, could be concluded the value of company moves in 0.831 proportion to the price of options, the standard deviations of index is low, so the real option based method could be used with success in company’s value estimation. The Gamma index shows the enterprise value is sensitive just for large changes. The result of Vega reflects the value of option, so the company’s value volatility, which is small in this case, but this means a volatility of value. In summary, we can conclude that the call options pricing model, well suited for the determination of company’s value.
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Possibilities of mass valuation in land use in Hungary
59-68Views:176Technological development makes it possible to simplify and accelerate decision-making processes by adequately processing and evaluating large volumes of data. Sub-data obtained from large data sets have a very important practical role in asset valuation, forecasting and valuing delineated or difficult-to-map areas, or in the context of portfolio management. Land valuation is a separate segment within asset valuation and it requires a specific methodological approach on behalf of evaluators. In this study, the authors compared the transaction data of arable land and the value of other land use categories. Based on empirical assessments, the authors developed proposals for the fast and cost-effective determination of the value of land use categories other than arable land - mainly meadows and pastures.
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The Examination of the Effects of Value Modifying Factors on Dairy Farms
36-40Views:76We wish to present a method to quantify the value modifying effects when comparing animal farms. To achieve our objective, multi-variable statistical methods were needed. We used a principal component analysis to originate three separate principal components from nine variables that determine the value of farms. A cluster analysis was carried out in order to classify farms as poor, average and excellent. The question may arise as to which principal components and which variables determine this classification.
After pointing out the significance of variables and principal components in determining the quality of farms, we analysed the relationships between principal components and market prices. Some farms did not show the expected results by the discriminant analysis, so we supposed that the third principal component plays a great role in calculating prices. To prove this supposition, we applied the logistic regression method. This method shows how great a role the principal components play in classifying farms on the basis of price categories.