Project selection is a complex multi-criteria decision-making process that is influenced by multiple and often conflicting goals. In world practice, the problem of project selection is mainly associated with a large number of projects which company should include in projects portfolio. In practice of Russian machine-building enterprises, the problem is further complicated by such factors as: the lack of linkage of the project portfolio with the company's strategy; filling all sections of the company's strategy with projects and programs; irregular update (revision) of projects included in portfolio; non-regular updating of program documents, as well as the lack of a set of project performance indicators for different types of selected projects.
This study is devoted to review of portfolio management modern theory of, an analysis of the situation in Russian machine-building industry, using the case of a particular enterprise and developing recommendations concerning the selecting criteria for development projects.
Almost a quarter of Russian government domestic bonds (OFZ) are issued as floating rate bonds. Due to the peculiarities of cash flow, yields of such bonds are calculated differently than of fixed coupon bonds and have different interpretation. However, in some trading platforms (for example, MICEX Trade SE) and other information sources yield measures for Russian government bonds with fixed coupon (OFZ–PD) and floating coupon (OFZ–PK) are presented in identical way which may mislead user. The aim of this paper is to highlight differences between yield measures applicable for OFZ with fixed and floating coupon and prevent any confusion that may arise in practice. We also study different credit spread measures of OFZ–PK that might be found in Bloomberg.
A comparative analysis is presented in the paper of asymptotic efficiencies derived on the basis of the Kaplan-Meier and the maximum likelihood methods which are widely used for censored data problems. A random variable is considered which is distributed according to the truncated normal distribution and is right-censored by another random variable distributed according to one of the following distributions: truncated normal, exponential and uniform one. It is demonstrated that under the correct assumption on the parametric family of distributions the maximum likelihood method yields higher asymptotic efficiency than the Kaplan-Meier method. This advantage of the maximum likelihood method becomes more significant for higher censoring percentage of the observed data sample and for the survival function close to 0 and 1. Besides it is obtained that the relative asymptotic efficiency of both methods under consideration is depended on the type of censored distribution. All the calculations are produced in computer mathematical environment Wolfram Mathematica.
Nowadays investors are facing changing conditions of global financial markets and should evaluate risks correctly. The most crucial factor is market risk that defines financial stability and investment results of professional participants at financial market and its clients. One of the characteristics of American stocks are higher volatility during financial report announcements. Common VaR methodology doesn’t take this into consideration as it lowers volatility during such periods and lowers it in other cases. Thereby a more flexible HVD-VaR model is proposed that allows risk estimation for each period separately. This can be possible due to the fact that announcement days are predefined. The proposed methodology is effective for a half of S&P500 stocks, so it’s useful for several financial instruments AS a result a more precise risk estimation method is proposed that considers extreme price movements caused by earnings announcement.
The article is devoted to the study of factors affecting the financial stability of banks. The study is based on a database covering the period from July 2008 to January 2017. In the course of the work, the specification of the logistic regression model, including nine explanatory variables, was evaluated. The model correctly predicts license revocation in 91% of cases. On the basis of the selected specification, it was possible to determine which factors have the strongest impact on the probability that the Bank will have the license revoked.
The crisis of 2007-2009 has shown that the evaluating credit risk is a crucial task in the financial market. This paper presents a methodology to assess credit risk for the companies of the Russian steel industry. The model is based on principals usually used by rating agents. The methodology shows what key qualitative and quantitative risk characteristics are likely to affect rating outcomes. It includes three groups of factors: business profile, market development and financial characteristics .The results reveal that this methodology is able to measure credit risk of the companies and analyze positive and negative sides of their activity.
Nowadays there are few researches which investigate the pricing methods for structured products which depend on several underlying assets and no researches devoted to this topic in the case of Russian market. The aim of this article is to estimate fair value of first-to-default structured notes based on Russian issuers CDS and to conclude is it possible to construct this type of notes under the conditions of Russian market. We construct 715 first-to-default notes and calculate its fair value. Besides, we estimate the return of notes compared to its risk using modified Sharp ratio. The article demonstrates the analysis of Russian CDS market and its perspectives as a source of underlying assets for first-to-default notes.
The paper presents modified Galors’ model designed to investigation of land inequality influence on the emergence of state educational system. Non-renewable natural resources are considered instead of land. It is show that a main hurdle for human capital accumulation and transition to economic growth is the concentration of natural resources ownership and its amount.