IDEAS home Printed from https://ideas.repec.org/a/gai/ruserr/r2241.html
   My bibliography  Save this article

Использование Многорежимных Моделей Для Моделирования Динамики Финансовых Временных Рядов

Author

Listed:
  • Vadim Ye. Zyamalov

    (Russian Presidential Academy of National Economy and Public Administration)

Abstract

Однорежимные эконометрические модели широко применяются в целях моделирования динамики фондовых индексов. Они справедливы при неизменности взаимосвязи между рассматриваемыми переменными, однако подобное допущение может стать неверным, если в силу каких-либо экономических причин переменные меняются. Для разрешения этих вопросов были предложены многорежимные модели, позволяющие в явном виде учитывать такие изменения. В настоящей работе представлены результаты моделирования влияния макроэкономических показателей на динамику индекса РТС в зависимости от внешнеэкономической конъюнктуры, для определения которой была выбрана цена нефти как одного из основных экспортных товаров РФ. Было показано, что в зависимости от экономического режима наблюдается различие в характере импульсных откликов индекса РТС на инновации в объясняющих макроэкономических показателях.

Suggested Citation

  • Vadim Ye. Zyamalov, 2022. "Использование Многорежимных Моделей Для Моделирования Динамики Финансовых Временных Рядов," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 5, pages 13-19, May.
  • Handle: RePEc:gai:ruserr:r2241
    as

    Download full text from publisher

    File URL: http://www.iep.ru/files/RePEc/gai/ruserr/r2241.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Basher, Syed Abul & Haug, Alfred A. & Sadorsky, Perry, 2012. "Oil prices, exchange rates and emerging stock markets," Energy Economics, Elsevier, vol. 34(1), pages 227-240.
    2. Vadim Ye. Zyamalov, 2017. "Comparison of the Predictive Ability of Single and Multi-Regime Models of Stock Market Dynamics," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 2, pages 64-75, April.
    3. Faia, Ester & Monacelli, Tommaso, 2007. "Optimal interest rate rules, asset prices, and credit frictions," Journal of Economic Dynamics and Control, Elsevier, vol. 31(10), pages 3228-3254, October.
    4. Ubilava, David, 2019. "On The Relationship Between Financial Instability And Economic Performance: Stressing The Business Of Nonlinear Modeling," Macroeconomic Dynamics, Cambridge University Press, vol. 23(1), pages 80-100, January.
    5. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
    6. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    7. S. Smirnov, 2001. "The System of Leading Indicators for Russia," Voprosy Ekonomiki, NP Voprosy Ekonomiki, vol. 3.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vadim Ye. Zyamalov, 2022. "Applying the Multi Regime Models to the Modelling the Dynamics of Financial Time Series [Использование Многорежимных Моделей Для Моделирования Динамики Финансовых Временных Рядов]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 5, pages 13-19, May.
    2. Fasanya, Ismail O. & Adekoya, Oluwasegun B. & Adetokunbo, Abiodun M., 2021. "On the connection between oil and global foreign exchange markets: The role of economic policy uncertainty," Resources Policy, Elsevier, vol. 72(C).
    3. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Filis, George, 2017. "Oil shocks and stock markets: Dynamic connectedness under the prism of recent geopolitical and economic unrest," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 1-26.
    4. Yang Liu & Tongshuai Qiao & Liyan Han, 2022. "Does clean energy matter? Revisiting the spillovers between energy and foreign exchange markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(11), pages 2068-2083, November.
    5. Jozef Baruník & Evžen KoÄ enda, 2019. "Total, Asymmetric and Frequency Connectedness between Oil and Forex Markets," The Energy Journal, , vol. 40(2_suppl), pages 157-174, December.
    6. Ibrahim Turhan & Erk Hacihasanoglu & Ugur Soytas, 2013. "Oil Prices and Emerging Market Exchange Rates," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 49(S1), pages 21-36, January.
    7. Turuntseva, M. & Zyamalov, V., 2016. "Stock Markets under the Changing Terms of Trade," Journal of the New Economic Association, New Economic Association, vol. 31(3), pages 93-109.
    8. Dong, Minyi & Chang, Chun-Ping & Gong, Qiang & Chu, Yin, 2019. "Revisiting global economic activity and crude oil prices: A wavelet analysis," Economic Modelling, Elsevier, vol. 78(C), pages 134-149.
    9. Le, Thai-Ha & Chang, Youngho, 2015. "Effects of oil price shocks on the stock market performance: Do nature of shocks and economies matter?," Energy Economics, Elsevier, vol. 51(C), pages 261-274.
    10. Chatziantoniou, Ioannis & Filippidis, Michail & Filis, George & Gabauer, David, 2021. "A closer look into the global determinants of oil price volatility," Energy Economics, Elsevier, vol. 95(C).
    11. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Filis, George, 2014. "Spillovers between oil and stock markets at times of geopolitical unrest and economic turbulence," MPRA Paper 59760, University Library of Munich, Germany.
    12. Ngene, Geoffrey M. & Tah, Kenneth A., 2023. "How are policy uncertainty, real economy, and financial sector connected?," Economic Modelling, Elsevier, vol. 123(C).
    13. Chatziantoniou, Ioannis & Elsayed, Ahmed H. & Gabauer, David & Gozgor, Giray, 2023. "Oil price shocks and exchange rate dynamics: Evidence from decomposed and partial connectedness measures for oil importing and exporting economies," Energy Economics, Elsevier, vol. 120(C).
    14. Aviral Kumar Tiwari & Samia Nasreen & Subhan Ullah & Muhammad Shahbaz, 2021. "Analysing spillover between returns and volatility series of oil across major stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2458-2490, April.
    15. Balcilar, Mehmet & Roubaud, David & Usman, Ojonugwa & Wohar, Mark E., 2021. "Moving out of the linear rut: A period-specific and regime-dependent exchange rate and oil price pass-through in the BRICS countries," Energy Economics, Elsevier, vol. 98(C).
    16. Demirer, Rıza & Ferrer, Román & Shahzad, Syed Jawad Hussain, 2020. "Oil price shocks, global financial markets and their connectedness," Energy Economics, Elsevier, vol. 88(C).
    17. Singh, Vipul Kumar & Nishant, Shreyank & Kumar, Pawan, 2018. "Dynamic and directional network connectedness of crude oil and currencies: Evidence from implied volatility," Energy Economics, Elsevier, vol. 76(C), pages 48-63.
    18. Seiler, Volker, 2024. "The relationship between Chinese and FOB prices of rare earth elements – Evidence in the time and frequency domain," The Quarterly Review of Economics and Finance, Elsevier, vol. 95(C), pages 160-179.
    19. Miklesh Yadav & Nandita Mishra & Shruti Ashok, 2023. "Dynamic connectedness of green bond with financial markets of European countries under OECD economies," Economic Change and Restructuring, Springer, vol. 56(1), pages 609-631, February.
    20. Hasan, Mudassar & Arif, Muhammad & Naeem, Muhammad Abubakr & Ngo, Quang-Thanh & Taghizadeh–Hesary, Farhad, 2021. "Time-frequency connectedness between Asian electricity sectors," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 208-224.

    More about this item

    Keywords

    финансовые индексы; многорежимные модели; STVECM; импульсные отклики;
    All these keywords.

    JEL classification:

    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gai:ruserr:r2241. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Olga Beloborodova (email available below). General contact details of provider: https://edirc.repec.org/data/gaidaru.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.