Issue #1 (Volume 8 2017)

The article analyzes the regulatory framework in the insurance market in connection with the advent of Big Data, such as information collected from different sources that can be manipulated by new technologies. The use of Big Data offers significant opportunities to the insurance companies in terms of digitization of the distribution channels and greater knowledge of the customers, which is instrumental to a more effective identification of the individual’s risk profile, as well as improvement of the competitiveness. However, regulatory measures are needed for a proper use of Big Data in terms of respect of the individual privacy, potential discrimination and constraint on competition.

Prior literature suggests that diversified property-liability (P/L) insurers underperform their focused counterparts. While most studies focus on insurers’ overall performance, there is an absence of evidence regarding whether the underperformance is driven by underwriting or investment profitability. The authors develop and test hypotheses of diversification’s separate effect on underwriting and investing in the U.S. property-liability (P/L) insurance industry. It is found that diversified insurers outperform their focused counterparts in terms of investment return, but that they underperform in terms of underwriting profitability. The results are robust to corrections for endogeneity bias and a matched sample analysis.

In the process of evaluating the premium of an insurance plan, one considers the risk arising from various uncertainties. The authors suppose for a plan whose net premium is p and the standard deviation is σ the premium including the risk factor will be p + 3σ for a given member, and 3σ reflects the risk. For a group of n members with the same premium p and with standard deviation σ, the premium including the risk factor will be p + 3σ/√n where 3σ/√n reflects the risk for each member of the group. The authors study the emerging profit in case of n insured groups each with its own premium and its own risk when all the n insured groups merge into a single group uniting all insured members. They prove that there emerge a profit due to joining the n groups into a single one due to a reduced total risk of the n separate insured groups when merging into a single group. The emerging profit between the various groups may be divided using the Shapley values method or using utility functions for each group. The auhors discuss various reasonable ways to split the emerging profit between the n groups and show that the split of the profit depends on the chosen method. The main tools are techniques of game theory, in particular those of cooperative games.

The purpose of the article is to determine the degree of financial interaction between the stock and insurance market, or, in other words, to determine the potential capacity of the stock market’s risk insurance sector for the Ukrainian insurance market. The authors examine the insurance not of all possible risks on the stock market, but only the most potentially important for the development of the stock market at this stage of economic development: insurance of professional risks of depositories and insurance of individual investments of individuals – participants of the stock market. In order to calculate the capacity of the stock market’s risk insurance sector in the context of the two above mentioned types, the authors apply the models that are widely used in the economic-mathematical analysis. For mathematical calculations we used 31 absolute indicators of the characteristics of the state of the stock and insurance markets, as well as some macroeconomic indicators. When forming an array of input data for mathematical calculations we used annual values of absolute indicators for the period 2005–2015 were used. For the adequacy of the received calculations the normalization of the selected indicators was carried out. All indicators were divided into two groups: stimulators and de-stimulators. The normalization of stimulator indicators was carried out by the method of natural normalization, and of de-stimulator indicators – according to the Savage formula. The capacity of the segment of the new type of insurance was determined by the authors as the maximum possible amount of insurance premiums that insurers can get in the process of implementing a new insurance product based on the current state of development of the insurance market. The capacity of the sector of the new type of insurance was presented as a function of the main component (an indicator that directly characterizes the created segment) and the corrective component (a set of indicators characterizing the segments created indirectly). The weight coefficients of the corrective component were determined by using the Fischer’s formula. As a result of the calculations, the authors obtained the data on the prospects of simultaneous introduction for the stock and insurance markets of such types of insurance as a professional liability insurance of depositories and an insurance of individual investors on the stock market.

The article considers main historical stages of the establishment and development of insurance supervision system in Russia and Ukraine. The objective necessity and the essence of state regulation of insurance business, its basic directions and methods providing a combination of interests of policyholders, the state and insurance companies are revealed. Attention is paid to the tendencies of content convergence and insurance supervision in many countries as well as regulation of the insurance markets development. The changes and the implementation of the control and supervision function in the insurance field in recent history, the risks of tightening the regulatory regime are considered. The need to introduce effective regulatory and supervisory practices in insurance by the Russian megaregulator (Bank of Russia) and the National Financial Service of Ukraine is shown.

The recent period has experienced many instances when market volatility suddenly increased even when there were no well-known fundamental catalysts, as illustrated by the short-lived but sharp transitions from low volatility to high volatility, as many in the last six years as we have had in the prior two decades ‒ increasing evidence that we are in a new volatility-of-volatility regime. Fundamentally, market impact is an illustration of market inefficiency: theories of efficient markets typically expect that investors buy and sell assets based on assessments of their intrinsic value, in contrast with large derivative players who often act based on market price movements which may not be linked to fundamentals. Market impact risk refers to the degree to which large size transactions can be carried out in a timely fashion with a minimal impact on prices. As a result, managing investment and liquidity risks for large players requires introducing an explicit market impact function, and applying to derivatives significantly depends on whether there is or not significant delta hedging activity: in case of no significant delta hedging activity, the risk appetite has significant influence on the optimal execution strategy, while in case of significant delta hedging activity the optimal trading involves feedback hedging effects translating into a modified Black ‒ Scholes hedging strategy.