Parametric Methods for Estimating the Level of Risk in Finance

The present paper deals with quantifying a wide range of risks through techniques known as Value at Risk. A given group of methods is discussed, whether human of financial practice or financial theorists from academia, more than twenty years, and it is therefore logical that there are currently exists a lot of approaches and algorithms risk quantification. We will be invented to a group called parametric methods for estimating the level of risk. This is a procedure where the theoretical probability distribution of losses is known, but we do not know its parameters. We must then be suitably approximated with some theoretical probability distributions and thus used for the distribution of individual risk rate. In our contribution we real data to approximate Normal, Student's and Laplace distribution. Percentage approximation we checked on the basis Likelihood logarithmic functions. Theoretical aspects of selected theoretical distributions will be applied to real data. Specifically, the shares of Oracle, Coca Cola and Pfizer term in January 2012 to February 2015. The price development is on a daily basis.