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Найдено 5 публикаций
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Статья
Tamara V. TEPLOVA, Tatiana V. SOKOLOVA. Journal of Applied Economic Sciences. 2015. Vol. X. No. 6(36). P. 897-913.

  The rapidly growing Russian national currency bond market is demonstrating attractive yield levels after global crisis 2008-2009. A significant share of ruble bond issues has relatively low trading volume, so liquidity risk is of particular importance for potential investors.   This article provides an analysis of theoretical approaches to the construction of bond liquidity integral indices andreviews existing practice in the Russian market. First, it compares methodologies of Russian investment banks (Trust, Gazprombank, Zenith and others) and a new cyclic algorithm introduced by Thomson Reuters Agency (TRLI 2015). In empirical part of our research Thomson Reuters’ integral indices of bond liquidity (weighted and non-weighted) are tested in the context of explaining the difference in yields of 1118 Russian national currency bonds outstanding (including government,municipal and corporate bonds). The multi-factor cross-sectional regression analysis results show that the influence of both Thomson Reuters liquidity indices on Russian bond yields is fairly stable. Duration and S&P rating also exert stable influence on bond yields. The non-weighted liquidity index has better explanatory power than the weighted one.

Добавлено: 30 ноября 2015
Статья
Lyapuntsova E. V., Tatuev A., Kutsuri G. et al. Journal of Applied Economic Sciences. 2017. Vol. 12. No. 3 (49). P. 726-735.
Добавлено: 30 октября 2019
Статья
Yasnitsky L., Yasnitsky V. L. Journal of Applied Economic Sciences. 2016. Vol. XI. No. 8(46). P. 1519-1530.
Добавлено: 15 февраля 2017
Статья
Asaturov K. G. Journal of Applied Economic Sciences. 2016. Vol. XI. No. 8(46). P. 1624-1642.

This paper examines the dynamic beta of Russian companies within the framework of the market model. The closing weekly prices of 29 Russian stocks, six Russian sector indices and the MICEX Index as a market index during the period from January 2009 to June 2015 are used to estimate time-varying beta using various econometric techniques. According to the results for the analyzed period, semiparametric regressions are confirmed to be the most effective model. As regards the forecast period, multivariate GARCH models surprisingly outperform all the other methods. An analysis of beta dynamics shows that most of time-varying betas are non-stationary.

Добавлено: 22 марта 2017
Статья
Konstantin ASATUROV, Tamara TEPLOVA, HARTWELL C. A. Journal of Applied Economic Sciences. 2015. Vol. X. No. 6(36). P. 929-945.

What is the relationship between the two largest emerging financial markets of Eastern Europe, Russia and Poland, and how do they impact the region’s stock markets? The purpose of this paper is to examine the role of these two countries in regional volatility by examining their effect on two separate phenomena: financial volatility, defined here as long-term interrelations, and contagion, a more short-term phenomenon. Utilizing bivariate DCC-GARCH modeling, this paper estimates long-term volatility spillover effects and short-term contagion effects and their origins during several periods of financial crisis in the Central and Eastern European region. Our results show that the long-term impact of volatility in the Russian market is much more substantial than that of Poland in Central and Eastern Europe, with this disparate impact corresponding to each country’s level of market capitalization. Additionally, our results show that Russia served as a source of short-term contagion for neighboring countries during its banking crisis in 2004 and during the Russian stock market fall in 2008. Poland had comparatively less effect on the region during the Global Financial Crisis. Moreover, the entrance of Poland into the European Union in May 2004 had no impact on stock markets in the region in terms of enhancing contagion.

Добавлено: 30 ноября 2015