Криптоэкономика: методика экспертной оценки ICO стартапов в процессах управления финансовыми инновациями
Objective: to develop a new methodological approach to the evaluation of innovative projects and technological startups which organize ICO.
Methods: method of comparative analysis, method of measurement, inductive method of cognition, logical methods of cognition.
Results: the paper discusses the events taking place in the market of alternative finances and demonstrates a new methodological approach to assessing the investment and innovation potential of startups implementing the ICO procedure. The methodology of evaluating the ICO startups is presented, which enables to make a choice and selection of target projects groups in a particular field of activity (for example, in fintech, insurtech). The stages of this assessment are considered. The developed method has been applied in practice for the evaluation (rating) of ICO projects in the insurance sector, and showed its efficiency. Its use allowed the authors to identify the fraudulent project and choose the “best” one using the rating results.
Scientific novelty: a methodology was created which enables to conduct expert evaluation of ICO projects.
Practical significance: the main provisions of the article can be used in the evaluation of start-up projects, in the work on ratings development. Also, the method of expert evaluation can be used in academic and pedagogical activities on the subject of alternative finances – ICO.
The paper studies a problem of optimal insurer’s choice of a risk-sharing policy in a dynamic risk model, so-called Cramer-Lundberg process, over infinite time interval. Additional constraints are imposed on residual risks of insureds: on mean value or with probability one. An optimal control problem of minimizing a functional of the form of variation coefficient is solved. We show that: in the first case the optimum is achieved at stop loss insurance policies, in the second case the optimal insurance is a combination of stop loss and deductible policies. It is proved that the obtained results can be easily applied to problems with other optimization criteria: maximization of long-run utility and minimization of probability of a deviation from mean trajectory.
This article conducts a study of multiplying the credit rating agencies efforts. These opportunities are practically important in connection with implementation of the IRB approach. The author considers Russian commercial banks as one of the main examples of using proposal methods, so in addition to literature overview the paper includes review of the Russian banking system and rating activities.
Firstly, the author discussed the rating scales mapping for comparison of rating estimations of different agencies. Then, he proposed the distance method with the connected extremum problem to find compatible mapping functions for rating scale correspondence.
Secondly, the paper considered the possibility of rating model system creation for financial institutions. The bank rating models in order logit interpretation are discussed simultaneously for resident (Russian) and non-resident institutions. In addition, the specification of bank models’ characteristics and their quality were considered for the three largest international rating agencies also as econometrical models for corporates and sovereign were presented.
The results reviewed can help to apply basic instruments for practical applications of such models to the risk management problems, which are based on the public information and remote estimation of ratings. Commercial banks and government financial regulators may be perspective consumers of the proposed methods.
The chapter studies a dynamic risk model defined on infinite time interval, where both insurance and per-claim reinsurance policies are chosen by the insurer in order to minimize a functional of the form of variation coefficient under constraints imposed with probability one on insured's and reinsurer's risks. We show that the optimum is achieved at constant policies, the optimal reinsurance is a partial stop loss reinsurance and the optimal insurance is a combination of stop loss and deductible policies. The results are illustrated by a numerical example involving uniformly distributed claim sizes.