This paper reviews the theory of Credit Default Swaps (CDS), the main characteristics of the CDS market, and how to estimate the non-default component of the yield spreads as the basis between the actual CDS premium and the hypothetical CDS premium implied by bond yields. We then analyze the most liquid CDS on Russian companies and compute the relative CDS-Bond basis from 2005 till 2010, paying particular attention to the period when a short selling ban was into effect in Russian financial markets from September 18, 2008 till June 15, 2009. We found that, while the basis was mainly negative before the ban, it then became largely positive during the period the ban was enforced. After the ban was lifted, the basis has started to decrease but still remains positive for all companies examined. This evidence therefore seems to support the hypothesis that a positive basis can be justified by the difficulty of arbitrage caused by short selling costs
In this paper the methodology for disaggregated macroeconomic model of the Russian economy of 1990-2010s is given. In this model the following main sectors of the Russian economy are considered: the real sector (subsectors EOM (export-oriented markets), DOM (domestic oriented markets), EM (natural monopolies)), the financial sector, the population. We try to explain why the real understanding of trends and tendencies of the Russian economy is achieved only via interactions between these sectors. The choice of predictors of the macroeconometric model is carried out on the basis of conclusions of the theoretical disaggregated model: the main factors of long-term dynamics in co-integrated equations are considered and the functional form of these equations is chosen. The main conclusion of the authors: the theoretical description of the Russian economy is possible on the basis of the structural disaggregated model which can be used for the macroeconometric modeling.
Many of business tasks requires revealing of consumer preferences. There are two main sources of preferences: actual sales data and consumer survey data. Both of these sources have substantial drawbacks. That is the reason why they are often combined into one dataset. In this paper we propose a methodology to estimate heterogeneous preferences on a dataset combined from transactional sales database and result of discrete choice experiment. We show that collection of experimental data allows to increase an efficiency of preferences estimates and improve a model quality in terms of actual choice prediction.
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This paper proposes a modification of an industry-wide banking competition indicator, namely the Boone indicator, which does not require information about the interest rates on loans, as opposed to the more popular Lerner index. In previous research, the Boone indicator was estimated as homogeneous effect of the banks marginal costs on their market shares or profitability. We show that such effect is heterogeneous and that the main sources of bank-level heterogeneity of that effect for Russian banks include differences in business models pursued (retail vs. corporate) and credit risk exposures. Based on these differences we identified more risky and less risky niches on credit market. Bank-level estimates of the Boone indicator showed that government-owned banks hold leading positions in every such niche. Next, based on these estimates we, first, confirm monopolistic nature of credit market with Sberbank being the dominant player. Second, we show that banks compete in this market mainly on quality rather than on quantity. Finally, we show that Boone indicator and the Lerner index provide similar predictions about the market power of Russian banks, despite of their differences.
The article deals with the issue of copula use in the program of price risk hedging. Copula-models performance is compared to the OLS-based ones. Fully parametric and semi-parametric approaches to copula-modeling are compared. The copula-based models efficiency is illustrated by the fact of decreasing the daily profit-and-loss volatility of the hedged portfolio by simultaneously augmenting its total yield compared to the OLS-based hedge ratio computation during the back-testing period. Nevertheless, it is shown that copula-based approaches are able to outperform OLS-based ones only for direct hedging programs, while for cross-hedging ones OLS do better.
In the paper we analyze the reasons of Russian bank license withdrawal, formulated in orders of CB RF at the period 2005.2–2008.4. During this period, after establishing deposit insurance system in Russia, two main reasons were «money laundering» and «financial insolvency». We design binary choice logit models and multinomial logit models to model probability of license withdrawal one year ahead of the event. We use in model macro indicators to control for the varying economic environment and bank-specific financial indicators taken one year before the observation of the bank status. The models reveal factors important for the prediction of the license withdrawal, which are found to be different for the two reasons. Also we investigate if multinomial model outperform binary model in the bank license withdrawal forecast. We consider dynamics of impact of unaccounted factors, including human factor.
Does the relationship between economic well-being of citizens and support for the welfare state institutions vary across the European Union member countries? To test a hypothesis about the differentiated effect of economic well-being we use multilevel regression modeling. Peculiarities of social cleavages and welfare models in the EU countries explain differences both in type and in degree of the relationship.
Paper is devoted to modeling risks of mortgage default and prepayment using data from large Russian mortgage agency. Various techniques of survival analysis are applied to estimate corresponding hazard functions and their relation to loan characteristics. Along with traditional, single equation regression models, split population approach is used. Special attention is paid to model selection issues.
In this paper, the four-parameter generalized beta distribution of the second kind (GB2) is applied to simulate the distribution of the income of Russian population based on the quarterly micro-data of household income for the period from 2003 to 2015. The distribution parameters were estimated via the maximum weighted-likelihood method, and the distributional parameter estimates were aggregated into quarterly time series. The time series have undergone the decomposition by the STL method. ARIMA and exponential smoothing models were applied to the trend component of the time series, and the distributional parameter forecasts were produced. Based on the predicted values of the distribution parameters, several inequality measures was estimated, such as at-risk-of-poverty rate, relative median poverty gap, quintile share ratio and Gini index. Thus, the robust estimates of inequality measures were obtained, the prediction accuracy of which was about 5%. An analysis of the dynamics of distributional parameters yielded an interesting conclusion that during the crisis periods the nominal level of income inequality decreases, in contrast to common apparent belief that negative macroeconomic shocks induce higher inequality.
How seriously does the degree of trust in basic social and political institutions for people from different countries depend on their individual characteristics? To answer this question, three types of models have been estimated using the data of the fifth wave of the World Value Survey: the first one based on the assumption about a generalized relationship for all countries, the second one taking into account heterogeneity of countries (using introduction of the country-level variables), the third type applying a preliminary subdivision of countries into five clusters. The obtained results have been used for suggestion of possible actions to increase public confidence in the basic institutions.
The paper identifies the factors affecting the investment in the production of new knowledge in the Russian regions. R&D and innovation expenditures are considered separately. The dataset includes 74 subjects of the Russian Federation for 2010–2014. We apply some panel regression models and estimation techniques. Based on their results we proposed a set of recommendations to promote innovation.
The work is dedicated to VaR models, estimated on the equities quotes of the six European countries. The time series cover three economic periods - pre crisis, crisis and post crisis, where the crisis period is the financial crunch of the 2008 year. The volatility estimation is based on the four APARCH(1,1) models and six distribution functions. The results of the investigation show the connection of the model with country's economic development and its financial condition at the different periods of time.
Using the Rosstat panel data for the 2001-2008 period we estimate the gravity model of migration between Russian regions. We show that though the migration flows have been quite stable, their determinants have changed substantially. Special attention is drawn to the role of distance between the regions. So far we have found out that social and economic factors are affecting migration between nearby regions. Yet our attempts to model the flows between distant (>500 km) places have lead to very poor goodness of fit.