Research background: Risk is an integral part of the world of financial markets today. One of the best known and widespread methods of quantifying the risk of a securities portfolio is the concept of value at risk (VaR). The method quantifies the maximum possible loss of a securities portfolio for specific variables. We used the work of Carol Alexander as a basis for our contribution, whence we borrowed mathematical formulas and derivatives of normal linear VaR and VaR scaling.
Purpose of the article: The aim of this study is to design our own method of using the VaR calculation in the trading process and to practically verify the explanatory power of such calculation. To meet this goal, we used our own designed and adjusted formulas to calculate normal linear VaR and scaling VaR.
Methods: The purpose of these adjusted formulas is to calculae specific levels of significance of specific scenarios of the course of trading positions, which represent the probability of their occurrence. Subsequently, we used regression analysis and constructed two regression models to verify that the significance levels themselves were significant variables, and that they could explain the variability of the explanatory variable to such an extent that they could be considered as strong predictors in the trading process.
Findings & Value added: Based on such research, we find that the resulting levels of significance of our proposed VaR calculation formulas are significant. Based on the compiled regression models, we also find that the dependence we identified is a strong one and can therefore be considered as systematic. Nevertheless, the materiality levels could explain only a small proportion of the variability of the variable being explained, and therefore could not be considered as strong predictors and thus involved in the trading process itself