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Editorial for the Special Issue on 'Computational Methods for Russian Economic and Financial Modelling'

Author

Listed:
  • Fantazzini, Dean

Abstract

This double-issue contains 11 papers invited for the first special issue on “Computational methods for Russian economic and financial modelling”. It was an attempt to explore and bring together practical, state-of-the-art applications of computational techniques with a particular focus on Russia and the Commonwealth of Independent States. The response was beyond expectations and managed to cover a wide range of issues, so that a double-issue was considered: the first dealing with Finance and the second with Economics.

Suggested Citation

  • Fantazzini, Dean, 2014. "Editorial for the Special Issue on 'Computational Methods for Russian Economic and Financial Modelling'," MPRA Paper 55430, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:55430
    as

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    File URL: https://mpra.ub.uni-muenchen.de/55430/1/MPRA_paper_55430.pdf
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    References listed on IDEAS

    as
    1. Alexey I. Balaev, 2014. "Modelling financial returns and portfolio construction for the Russian stock market," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 4(1/2), pages 32-81.
    2. Dean Fantazzini & Nikita Fomichev, 2014. "Forecasting the real price of oil using online search data," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 4(1/2), pages 4-31.
    3. Anna Sokolova, 2014. "Are inflation expectations in Russia forward-looking?," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 4(1/2), pages 254-268.
    4. Oxana Malakhovskaya & Alexey Minabutdinov, 2014. "Are commodity price shocks important? A Bayesian estimation of a DSGE model for Russia," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 4(1/2), pages 148-180.
    5. N.P. Pilnik & I.G. Pospelov & S.A. Radionov & A.A. Zhukova, 2014. "The intertemporal general equilibrium model of the economy with the product, money and stock markets," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 4(1/2), pages 207-233.
    6. Anatoly Peresetsky, 2014. "What drives the Russian stock market: world market and political shocks," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 4(1/2), pages 82-95.
    7. Olga Demidova, 2014. "How Russian and Ukrainian citizens perceive the role of immigrants in their country: a comparison with European residents," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 4(1/2), pages 181-206.
    8. M.A. Khokhlov & I.G. Pospelov & L.Ya. Pospelova, 2014. "Technology of development and implementation of realistic (country-specific) models of intertemporal equilibrium," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 4(1/2), pages 234-253.
    9. Vladimir I. Malugin & Natalia V. Hryn & Aleksandr Y. Novopoltsev, 2014. "Statistical analysis and econometric modelling of the creditworthiness of non-financial companies," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 4(1/2), pages 130-147.
    10. Henry Penikas & Anastasia Petrova, 2014. "An empirical analysis of growth and consolidation in banking: a Markovian approach for the case of Russia," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 4(1/2), pages 112-129.
    11. Evgeny A. Ivin & Alexey N. Kurbatskiy & Alexandr V. Slovesnov, 2014. "Time aspects of a fund manager appraisal," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 4(1/2), pages 96-111.
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    More about this item

    Keywords

    Forecasting; oil price; Google; Russian stock market; T-distribution with vector degrees of freedom; portfolio management; Fund manager; Russian banking sector; Credit Risk; DSGE; Russia; Immigrants; Intertemporal general equilibrium model; Intertemporal equilibrium; Inflation; Inflation expectations;

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit
    • G1 - Financial Economics - - General Financial Markets
    • G2 - Financial Economics - - Financial Institutions and Services
    • J0 - Labor and Demographic Economics - - General
    • J1 - Labor and Demographic Economics - - Demographic Economics

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