nep-cis New Economics Papers
on Confederation of Independent States
Issue of 2019‒05‒06
six papers chosen by



  1. Style consistency and mutual fund returns: the case of Russia By Adiya Bayarmaa; Guglielmo Maria Caporale
  2. Russian energy and ICT MNEs in global value chains: Shift of location advantages under the sanctions By Garanina, Olga; Abramova, Abramova
  3. Movements in International Bond Markets: The Role of Oil Prices By Saban Nazlioglu; Rangan Gupta; Elie Bouri
  4. Volatility forecasts for the RTS stock index: option-implied volatility versus alternative methods By Guglielmo Maria Caporale; Daria Teterkina
  5. Forecasting Realized Volatility of Russian stocks using Google Trends and Implied Volatility By Bazhenov, Timofey; Fantazzini, Dean
  6. Building an Innovation Ecosystem as an Alternative of Oil Sector Exports in Azerbaijan (on the basis of the study of Israeli practice) By Babayev, Bahruz

  1. By: Adiya Bayarmaa; Guglielmo Maria Caporale
    Abstract: This paper carries out style analysis for Russian mutual funds using monthly data from the National Managers’ Association over the period January 2008-December 2017; specifically, it applies the RSBA method developed by Sharpe (1992) for evaluating the impact of style on returns, and uses the Style Drift Score (SDS) introduced by Idzorek (2004) as a measure of a fund’s style drifting activity. The main findings can be summarised as follows. In the Russian case there is a significant positive relationship between style consistency and profitability of funds. Further, Russian funds are characterised by a high level of style drift, namely deviations from the investment strategy declared at the time of registration as required by Russian law.
    Keywords: mutual funds, style consistency, performance, Russia
    JEL: C23 G14 G19
    Date: 2019
    URL: http://d.repec.org/n?u=RePEc:ces:ceswps:_7605&r=all
  2. By: Garanina, Olga; Abramova, Abramova
    Abstract: The paper is focused on detailed analysis of the expansion challenges of Russian MNEs under the sanctions. The present research aims to understand how the shifts in global governance affect Russian multinationals (MNEs) inclusion into GVCs. We focus on energy and ICT industries. The research is based on multiple case study. Cases from energy and ICT sectors are examined in order to demonstrate the challenges for Russian MNEs inclusion in GVCs in context of sanctions and opportunities connected to the emergence of new governance institutions supporting Russian MNEs expansion towards Asia. Expected results are the following: a structured overview of external policy constraints and opportunities for Russian MNCs inclusion into GVCs; analysis of possible options for expansion of Russian MNEs in GVCs in Europe and in Asia.
    Date: 2018
    URL: http://d.repec.org/n?u=RePEc:sps:cpaper:15472&r=all
  3. By: Saban Nazlioglu (Department of International Trade and Finance, Faculty of Economics and Administrative Sciences, Pamukkale University, Denizli, Turkey); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); Elie Bouri (USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon)
    Abstract: In this paper, we analyze daily data-based price transmission and volatility spillovers between crude oil and bond markets of major oil exporters and importers, by accounting for structural shifts as a smooth process in causality and volatility spillover estimations. In general, we find that, oil prices tend to predict bond prices in majority of oil exporting countries, and for the two major oil importers of India and China. But, the feedback from bond to oil prices is weak, and is detected for China and USA. Regarding volatility spillovers, oil volatility affects the bond market volatility of some major oil exporters (Kuwait, Norway and Russia), and an importer (France). However, the most prominent volatility spillovers are from bond to oil, except for Kuwait and Saudi Arabia. We also reveal that taking into account for smooth structural shifts - accounting for structural breaks - strengthens our findings and particularly is important for volatility spillover analysis. Our results have important implications for academics, investors, and policy makers.
    Keywords: Bond and oil markets, price and volatility spillovers, major oil exporters and importers, structural changes
    JEL: C32 G12 Q02
    Date: 2019–04
    URL: http://d.repec.org/n?u=RePEc:pre:wpaper:201935&r=all
  4. By: Guglielmo Maria Caporale; Daria Teterkina
    Abstract: This paper compares volatility forecasts for the RTS Index (the main index for the Russian stock market) generated by alternative models, specifically option-implied volatility forecasts based on the Black-Scholes model, ARCH/GARCH-type model forecasts, and forecasts combining those two using a mixing strategy based either on a simple average or a weighted average with the weights being determined according to two different criteria (either minimizing the errors or maximizing the information content). Various forecasting performance tests are carried out which suggest that both implied volatility and combination methods using a simple average outperform ARCH/GARCH-type models in terms of forecasting accuracy.
    Keywords: option-implied volatility, ARCH-type models, mixed strategies
    JEL: C22 G12
    Date: 2019
    URL: http://d.repec.org/n?u=RePEc:ces:ceswps:_7612&r=all
  5. By: Bazhenov, Timofey; Fantazzini, Dean
    Abstract: This work proposes to forecast the Realized Volatility (RV) and the Value-at-Risk (VaR) of the most liquid Russian stocks using GARCH, ARFIMA and HAR models, including both the implied volatility computed from options prices and Google Trends data. The in-sample analysis showed that only the implied volatility had a significant effect on the realized volatility across most stocks and estimated models, whereas Google Trends did not have any significant effect. The out-of-sample analysis highlighted that models including the implied volatility improved their forecasting performances, whereas models including internet search activity worsened their performances in several cases. Moreover, simple HAR and ARFIMA models without additional regressors often reported the best forecasts for the daily realized volatility and for the daily Value-at-Risk at the 1% probability level, thus showing that efficiency gains more than compensate any possible model misspecifications and parameters biases. Our empirical evidence shows that, in the case of Russian stocks, Google Trends does not capture any additional information already included in the implied volatility.
    Keywords: Forecasting; Realized Volatility; Value-at-Risk; Implied Volatility; Google Trends; GARCH; ARFIMA; HAR;
    JEL: C22 C51 C53 G17 G32
    Date: 2019–04
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:93544&r=all
  6. By: Babayev, Bahruz
    Abstract: Building a successful innovation ecosystem is a key factor in innovation, growth, and development, and it can be an alternative to reducing Azerbaijan’s dependency on oil exports. Formation of a favorable innovation ecosystem remains an essential policy priority for the government of Azerbaijan too. President of the Republic of Azerbaijan signed a decree on the establishment of the Innovation Agency under the Ministry of Transport, Communications and High Technologies of the Republic of Azerbaijan on November 6th, 2018. The Innovation Agency to be established in 2019 will be a coordinating body to draft and implement an innovation roadmap of an Azerbaijani ecosystem. This paper reviews world practice, including an Israeli practice of success to deduct results and models to build an ecosystem in Azerbaijan. The aim is to determine factors that made the Israeli ecosystem successful and study if these factors can be applied to the development and implementation of similar benchmarks in Azerbaijan. The methodology that is used for this research is the case study from Israel. Through systematic analysis and logical generalization, the paper analytically discusses and deducts conclusions from Israel’s experience to spell out some key public policy lessons.
    Keywords: Business model, Ecosystem, Innovation, Innovation ecosystem, non-oil economy
    JEL: O3 O38 O4
    Date: 2019–04–30
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:93600&r=all

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