Value-at-Risk: The Method of Studying and Managing Risks


The term Value-at-Risk (VaR) is understood as a particular measure of risk, demonstrating an amount not exceeding the portfolio’s losses. This cost measure, as a rule, is characterized by three parameters: time horizon, confidence level, and base currency. In brief, the Value-at-Risk calculation is carried out to anticipate and predict the expected consequences. VaR is one of the best, most effective, practical, and productive methods of studying and managing risks in terms of several excellent examples.

Synthesizing the Articles with Studied Information

In order to assess the quality and usefulness of the VaR mechanism in the framework of risk assessment and control, one should refer to the two good, relevant, and reliable articles. For example, according to the data from the first article, VaR is great for studying cryptocurrencies, especially bitcoin, its value, and further development for a certain time (Stavroyiannis, 2018). In this case, Value-at-Risk allows people to estimate losses from owning the currency with a specific probability. This may be done quite briefly so that a person can relatively easily imagine the amount of threat. Such information is also clarified in the assigned textbook readings. In addition, using the tool, the researchers found out that bitcoin has a high range of risk exposure, unlike other assets, and investors in this currency are subject to special requirements for capital and its distribution (Stavroyiannis, 2018). The knowledge obtained permits individuals to see a holistic picture of the situation, draw informed conclusions, and accept the best offers.

Furthermore, Value-at-Risk fits perfectly into the concept of studying risks and market losses for companies producing modern products. According to Yie et al. (2021), such significant players on the world stage as Apple, Google, and Microsoft have shares that are subject to a unique, thorough, detailed estimation and examination. VaR showed a low indicator for Microsoft but higher for other competitors (Yie et al., 2021). Referring to the facts from the assigned textbook readings, VaR modeling determines the potential losses of the assessed organization and the probability of certain losses. Accordingly, one can assume that Microsoft’s entrepreneurial activity should be more stable and reliable. Based on the data from the book, using the data provided by VaR, financial institutions of these companies can determine whether they have sufficient capital reserves to cover losses or whether risks exceeding permissible require them to reduce concentrated assets.

Opinions and Analysis on the Research

In my opinion, despite the criticism of Value-at-Risk, this tool is still relevant and essential in assessing financial risks and undesirable consequences for protection from troubles and preservation of capital. Based on the research results, I should note that it would be challenging to predict the expected outcome of events without the instrument. In particular, the knowledge acquired using Value-at-Risk is vital in the field of decision-making and the application of radical actions.


Summarizing the information mentioned above, it should be assumed that Value-at-Risk plays a crucial role in assessing risks and adverse consequences for some companies or owners of valuables. In general, VaR has the broadest range of capabilities and scope applied to specific positions, entire portfolios, or to measure the risk exposure of a whole firm. VaR modeling demonstrates an excellent understanding, for example, a drop in the value of a cryptocurrency or shares on the phones of a particular firm. Such information is especially important because it allows you to track the probability of a specific scenario taking into account the allowable time and making adjustments in time.


Stavroyiannis, S. (2018). Value-at-risk and related measures for the Bitcoin. The Journal of Risk Finance, 19(2), pp. 127-136.

Yie, W. L. S., Chan, K. Y., & Lim, F. P. (2021). Estimation of value at risk for stock prices in mobile phone industry. Data Analytics and Applied Mathematics (DAAM), 2(2), pp. 14-26.

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