Abstract
In the financial analyses the fact of predicting future states of the instruments subjected to investments is extremely important. It allows reducing risk and maximizing potential profits. That is why any ways which enable to predict the further negative results of taking decisions are very important and the knowledge about the measures and their efficiency are an additional advantage. Thus, the paper in which value at risk and an assessment of this measure are discussed seems to be of interest. Thus, in the paper Value at Risk (VaR) is presented as an instrument which reduces the risk and defines its scale. The analysis is made among twenty companies of the Warsaw Stock Exchange currently included in the WIG20. Taking into account the multitude of varieties of VaR calculation methods, some of them are proposed to carry out some kind of confrontation. Such an approach was designed to assess the effectiveness of simulation methods for more complex parametric methods. As a part of the resultant, which combines both of these groups, also a method which belongs to semi-parametric EKT (Note 1) group is proposed. At the same time the Monte Carlo and historical simulation as non-parametric methods are taken into consideration. The methods developed by the Risk Metrics with different approaches to the modeling of random noise (normal distribution, the Student's t distribution and GED (Note 2) are representatives of the second group). In order to evaluate the full effectiveness of these methods three levels of significance of 0,01, 0,05 and 0,10 are taken into account. The research horizon covers the period from 01.01.2010 to 31.12.2012. In the analyses 151 historical observations are used as the information necessary to estimate these models. In order to estimate the quantity there was used the dependence developed by Risk Metrics™, namely:
Highlights
The financial world has its own rules and all decisions result in financial consequences
The relevant parameters of the model are determined by the maximum probability method. Another model recommended by Risk MetricsTM is a Risk Metrics t-Student model with random noise modeled by t-Student distribution
While drawing conclusions it can be argued that the best estimates and the most efficient forecasts, irrespective of the level of significance, provide models of random noise modeled by Student's t-distribution
Summary
The financial world has its own rules and all decisions result in financial consequences. Any measure, including the value at risk, may allow for a full exploration of knowledge about the mechanisms that create the financial markets but, more importantly can be used as a tool to fight over the negative consequences of our decisions. Taking into account the multitude of varieties of VaR calculation methods, some of them are proposed to carry out some kind of confrontation. Such an approach was designed to assess the effectiveness of simulation methods for more complex parametric methods. Where, as it is easy to see the required amount of historical observations is dependent on the specified level of tolerance and the adopted smoothing constant.
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