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The Optimal Behavior Model of the Modern Russian Banking System

Author

Listed:
  • Nikolay Pilnik

    (National Research University Higher School of Economics, Moscow, Russia)

  • Stanislav Radionov

    (National Research University Higher School of Economics, Moscow, Russia)

  • Artem Yazykov

    (National Research University Higher School of Economics, Moscow, Russia)

Abstract

The paper describes the new version of the model of the Russian banking system, which successfully reproduces a wide set of parameters characterizing its performance: loans and deposits of firms and households, liquidity nominated both in rubles and in foreign currency, mandatory reserves. We describe the technique of derivation of model relations, which includes the statement of the problem of macroeconomic agent “bank”. This problem is based on the maximization of discounted flow of profit subject to budget constraint, balance of loans and deposits, liquidity constraints and reserve sufficiency requirements. The paper contains the system of equations which describes the solution of the problem. We provide a detailed description of transition of continuous to discrete time and the new approach to the relaxation of complementary slackness conditions based on the assumption that the model exhibits a turnpike property. Apart from the standard approach to the parameter estimation for this class of models, we apply a method of multi-step forecasting. We show that the standard method of estimations allows to closely reproduce the historic series but leads to the poor quality of forecasts. The method of multi-step forecasting, on the other hand, successfully reproduces historic series and also leads to rather accurate forecasts. We compared it with standard econometric techniques and show that the model with parameters obtained via multi-step forecast method provides somewhat better forecasts than ARIXAM and much better ones than AR, ARIMA, VAR and VARX. We also show that then we use multi-step forecasting method, optimal values of parameters are about the same for different intervals of estimation and different lengths of forecasts (from one to six months). Such a stability of parameters makes us think that the model reproduces long-term relations of variables and can be used for forecasting and scenario analysis. The model can be used for the evaluation of reaction of the banking system on the monetary policy, external constraints of different kind and the general condition of the economy. The model can be used as a block of a bigger general equilibrium model of the Russian economy.

Suggested Citation

  • Nikolay Pilnik & Stanislav Radionov & Artem Yazykov, 2018. "The Optimal Behavior Model of the Modern Russian Banking System," HSE Economic Journal, National Research University Higher School of Economics, vol. 22(3), pages 418-447.
  • Handle: RePEc:hig:ecohse:2018:3:5
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    References listed on IDEAS

    as
    1. Markus K. Brunnermeier & Yuliy Sannikov, 2014. "A Macroeconomic Model with a Financial Sector," American Economic Review, American Economic Association, vol. 104(2), pages 379-421, February.
    2. 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.
    3. Carlstrom, Charles T & Fuerst, Timothy S, 1997. "Agency Costs, Net Worth, and Business Fluctuations: A Computable General Equilibrium Analysis," American Economic Review, American Economic Association, vol. 87(5), pages 893-910, December.
    4. Jaromir Benes & Michael Kumhof, 2011. "Risky Bank Lending and Optimal Capital Adequacy Regulation," IMF Working Papers 11/130, International Monetary Fund.
    5. Zhigu He & Arvind Krishnamurthy, 2012. "A Model of Capital and Crises," Review of Economic Studies, Oxford University Press, vol. 79(2), pages 735-777.
    6. Bernanke, Ben & Gertler, Mark, 1989. "Agency Costs, Net Worth, and Business Fluctuations," American Economic Review, American Economic Association, vol. 79(1), pages 14-31, March.
    7. Mamonov, Mikhail & Vernikov, Andrei, 2017. "Bank ownership and cost efficiency: New empirical evidence from Russia," Economic Systems, Elsevier, vol. 41(2), pages 305-319.
    8. repec:spr:pharme:v:4:y:2014:i:1:p:81-98 is not listed on IDEAS
    9. Belousova, Veronika & Karminsky, Alexander & Kozyr, Ilya, 2018. "The macroeconomic and institutional determinants of the profit efficiency frontier for Russian banks," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 49, pages 91-114.
    10. Gertler, Mark & Karadi, Peter, 2011. "A model of unconventional monetary policy," Journal of Monetary Economics, Elsevier, vol. 58(1), pages 17-34, January.
    11. Alexander Karminsky & Alexander Kostrov, 2014. "The probability of default in Russian banking," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 4(1), pages 81-98, June.
    12. Gertler, Mark & Kiyotaki, Nobuhiro & Queralto, Albert, 2012. "Financial crises, bank risk exposure and government financial policy," Journal of Monetary Economics, Elsevier, vol. 59(S), pages 17-34.
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    Cited by:

    1. N. P. Pilnik & I. G. Pospelov & S. A. Radionov, 2020. "On Limits of the Influence of the Bank of Russia Key Rate on Indicators of the Russian Banking System," Studies on Russian Economic Development, Springer, vol. 31(2), pages 229-237, March.

    More about this item

    Keywords

    banking system; loans; deposits; liquidity; settlement accounts; forecasting; mandatory reserves;

    JEL classification:

    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models

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