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

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  • 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
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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

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
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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;
    All these keywords.

    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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