CEUR-WS Proceedings of the Workshop on Computer Modelling in Decision Making (CMDM 2017)
Students’ perception of the labor market makes a great deal in students’ decisions concerning effort to study, work during university studies, etc. The aim of the research is to define whether students identify significant returns on effort with respect to wage after graduation. Moreover, it seems reasonable to single out other factors that students expect to influence their wage significantly. With the use of the data of Russian students’ questionnaire conducted in 2012 within the framework of the Monitor of Economics of Education project the regressions with the use of instrumental variables and stochastic frontier approach are estimated. The results suggest effort is considered as an influential factor in determining wage by Russian students if students’ incomplete awareness about labor market is taken into account. Besides, university quality, abilities, wage received by working students, region, specialty, family’s income and gender make the difference in the amount of wage expected by students. For additional analysis the 20% and 80% quantile regressions are built. According to the results, persons having the highest wage forecasts base them on the amount of wage offered to working students on the labor market and do not correct them subject to their effort, university quality and abilities. At the same time another group of students, keeping similar basis for expectation formation as a previously analyzed group, expect significant contribution of effort and abilities.
The paper studies the problem of dynamic hedge ratio calculation for the portfolio consisted of two assets – futures and the underlying stock. We apply the utility based approach to account for the degree of risk aversion in the hedging strategy. Seventeen portfolios, consisted of Russian blue-chip stocks and futures, are estimated in the paper. In order to estimate the conditional covariances of hedged portfolio returns, such multivariate volatility models as GO-GARCH, copula-GARCH, asymmetric DCC and parsimonious stochastic volatility model are applied. The hedging efficiency is estimated on the out-of-sample period using the maximum attainable risk reduction, the financial result and the investor’s utility. It’s shown that for 60% of portfolios ADCC surpasses the other models in hedging. Including the degree of risk aversion in the investor’s utility function together with above-mentioned volatility models allows to reach hedging efficiency of 88%.