Since Bitcoin introduction in 2008, the cryptocurrency market has grown into hundreds-of-billion-dollar market. The cryptocurrency market is well known as very volatile, mainly for the fact that the cryptocurrencies have not the price to fall back upon and that anybody can join the trading (no license or approval is required). Since empirical literature suggests that GARCH-type models dominate as VaR estimators the overall objective of this paper is to perform comprehensive volatility and VaR estimation for three major digital assets and conclude which method gives the best results in terms of risk management. The methods we used are parametric (GARCH and EWMA model), non-parametric (historical VaR) and Monte Carlo simulation (given by Geometric Brownian Motion). We conclude that the best method for value-at-risk estimation for cryptocurrencies is the Monte Carlo simulation due to the heavy diffusion (stochastic) process and robustness of the results.

The COVID-19 pandemic outbreak has upended the educational system worldwide, possibly with severe long-term consequences as most training institutions were forced to move to an online environment. Given the sudden transition to remote education, the main objective of this contribution is to evaluate the impact of distance education on examination results. We investigated the examination results of tax related subjects collected at the Czech University of Life Sciences in Prague during the period from 2014 to 2020. The sample consists of examination results of 120 different classes within 6 years with a total amount of 7268 observations. Firstly, we pivoted the data into the long format and performed binary logistic regression. Our findings suggest that the odds that student successfully passes the exam increases if the student was examined online compared to in-person. Additionally, we used KNN regression which enables us to predict success rate for an upcoming semester. According to our analysis, it is expected that on average 82 students out of 100 will successfully pass the exam. The model was calibrated using cross-validation to choose optimal K.

Frequent price discounts have become one of the features of retail chains in the Czech Republic. Discounts are most often provided for products popular with customers. One of the products that is subject to frequent discounts is milk. Several expert opinions estimate that up to 50% of milk is sold with frequent price discounts. Price pressure on farmers and food producers owing to frequent price promotions is supported by the purchasing power of retailers and, in extreme cases, leads to sub-cost purchasing prices. These facts set up the framework to assess the impact of frequent price discounts on policymaking, including tax administration. One of the effects of selling goods at discounts is lower VAT collection from shops, and thus lower revenue to the state budget. This paper attempts to estimate one of the potential impacts of frequent price discounts, namely the loss of VAT for the state budget due to low retail prices of milk. Theoretical estimation of the effect of frequent price discounts on VAT can help to assess policy that touches on mark-ups in specific markets, such as food. The estimation is based on data obtained from Czech Statistical Office, FADN, and Orbis databases and employs Monte Carlo simulation to capture the stochastic element of retail markups. Sub-cost prices of producers of milk have a more significant negative effect on VAT revenue than prices of other supply chain participants. The theoretical effect on VAT revenue is estimated to be in the range from a negative effect of 14.9 billion CZK to a positive effect of 7.4 billion CZK. Values of zero VAT effect points (mark-ups of producers, dairies, and retailers) are shown, as well as the critical value of price elasticity of demand (−0.1715), at which the effect of frequent price discounts on VAT revenue is zero.

The objective of this study is to identify the regions of the Czech Republic with the economically non-effective agriculture industry. The methodology is based on an orginal approach as the economically weak regions are identified on the LAU 1 level, comparing to the existing studies using only NUTS III level. The input data describe the economic results of 6,031 agricultural entities from 75 different regions LAU 1. The data covering the period between 2006 and 2014 were gained from the database Amadeus. The study deploys the methods of Principal Component Analysis, Kaiser-Meier-Olkin test, Bartlett’s test and hierarchical cluster analysis. The study determinates two key components: the Company Size and the Company Profit. These key components are used as the input variables for the cluster analysis. The cluster analysis identifies four clusters of regions from the agricultural entities economical results point of view. Subsequently the Standardized Variable Method is used to determinate the mutual order of the regions. The results of LAU 1 regions analyses show that the agrucultural entities, located in the border regions, reach economical results below average of the Czech Republic regions.

The aim of the paper was to identify areas of possible abuse of social security support in the Czech Republic, with a specific focus on the Ústí nad Labem Region and the Litoměřice district, and to estimate the volume of social security support drawn in conflict with the legal regulations of the Czech Republic. In the Ústí nad Labem Region, spending on social security increased by 34.9% in the years 2016–2020. Upon a detailed focus on the Litoměřice district in the monitored period of 2018–2020, there were 69 fraud cases in the Litoměřice district within the scope of the social security system and illness benefit. Over 60% of this fraud and loss was deferred. The biggest shortcoming of the Czech Republic’s social system is the fact that individual institutions have little or no chance of finding out whether applicants are entering truthful information in the forms. A change of legal regulations and mandatory checks on applicants is the path towards reducing the amount of fraud and loss in social security and illness benefit.