IDEAS home Printed from https://ideas.repec.org/a/scn/00rbes/y2015i2p67-68.html
   My bibliography  Save this article

Infographics: Patterns Of Information Flows Sharing And Volatility Spillovers

Author

Listed:
  • V. Barmin

    (LLC Commercial Bank "Ergobank")

Abstract

It’s commonplace, that information drives prices. Can we infer the impact of information by just observing prices? Can we observe regime changes during crises, when markets are overwhelmed with waves of fear and greed? What happens in the aftermath? We estimate information flows on the world marketsby modeling volatility of regional stock indexes. Then we estimate VAR models for volatilities and use the capabilities of ‘circlize’ package from statistical environment ‘R project’ to visualize patterns of exposure and auto-determinism of information processes in global stock markets. Общеизвестно, что ценой движет информация. Можем ли мы оценить эффект информационного воздействия, наблюдая цены? Меняется ли это воздействие в кризисы и бумы, когда рынки захлестывают эмоциональные волны? Что происходит после? Мы оцениваем информационный процесс на мировых рынках, моделируя волатильность региональных фондовых индексов, строим модели векторной авторегрессии и используем возможности пакета circlize статистической среды R project для визуализации информационных процессов на мировых фондовых рынках.

Suggested Citation

  • V. Barmin, 2015. "Infographics: Patterns Of Information Flows Sharing And Volatility Spillovers," Review of Business and Economics Studies // Review of Business and Economics Studies, Финансовый Университет // Financial University, vol. 3(2), pages 67-68.
    • Handle: RePEc:scn:00rbes:y:2015:i:2:p:67-68
    as

    Download full text from publisher

    File URL: https://rbes.fa.ru/jour/article/viewFile/17/17.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pfaff, Bernhard, 2008. "VAR, SVAR and SVEC Models: Implementation Within R Package vars," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i04).
    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. Niels Haldrup & Carsten P. T. Rosenskjold, 2019. "A Parametric Factor Model of the Term Structure of Mortality," Econometrics, MDPI, vol. 7(1), pages 1-22, March.
    2. Deborah Bentivoglio & Adele Finco & Mirian Rumenos Piedade Bacchi, 2016. "Interdependencies between Biofuel, Fuel and Food Prices: The Case of the Brazilian Ethanol Market," Energies, MDPI, vol. 9(6), pages 1-16, June.
    3. Thierry Moudiki & Frédéric Planchet & Areski Cousin, 2018. "Multiple Time Series Forecasting Using Quasi-Randomized Functional Link Neural Networks," Risks, MDPI, vol. 6(1), pages 1-20, March.
    4. Bruns, Stephan B. & König, Johannes & Stern, David I., 2019. "Replication and robustness analysis of ‘energy and economic growth in the USA: A multivariate approach’," Energy Economics, Elsevier, vol. 82(C), pages 100-113.
    5. Raja Ben Hajria & Salah Khardani & Hamdi Raïssi, 2017. "Testing the lag length of vector autoregressive models: A power comparison between portmanteau and Lagrange multiplier tests," Working Papers 2017-03, Escuela de Negocios y Economía, Pontificia Universidad Católica de Valparaíso.
    6. Raimund M. Kovacevic, 2019. "Valuation and pricing of electricity delivery contracts: the producer’s view," Annals of Operations Research, Springer, vol. 275(2), pages 421-460, April.
    7. João Sousa Andrade & Adelaide Duarte & Marta Simões, 2011. "Inequality and Growth in Portugal: a time series analysis," GEMF Working Papers 2011-11, GEMF, Faculty of Economics, University of Coimbra.
    8. Accolley, Delali, 2018. "Accounting for Busines Cycles in Canada: II. The Role of Money," MPRA Paper 85481, University Library of Munich, Germany.
    9. Shakina, Elena & Parshakov, Petr & Alsufiev, Artem, 2021. "Rethinking the corporate digital divide: The complementarity of technologies and the demand for digital skills," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    10. Mirosław Bełej, 2022. "Does Google Trends Show the Strength of Social Interest as a Predictor of Housing Price Dynamics?," Sustainability, MDPI, vol. 14(9), pages 1-14, May.
    11. Hazar Altınbaş & Vincenzo Pacelli & Edgardo Sica, 2022. "An Empirical Assessment of the Contagion Determinants in the Euro Area in a Period of Sovereign Debt Risk," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 8(2), pages 339-371, July.
    12. Yeager, Elizabeth A. & Featherstone, Allen M., 2011. "Impact of Changes in Energy Input Prices on Ethanol Importation and Prices," Journal of Agribusiness, Agricultural Economics Association of Georgia, vol. 29(2).
    13. Julio Barzola-Monteses & Mónica Mite-León & Mayken Espinoza-Andaluz & Juan Gómez-Romero & Waldo Fajardo, 2019. "Time Series Analysis for Predicting Hydroelectric Power Production: The Ecuador Case," Sustainability, MDPI, vol. 11(23), pages 1-19, November.
    14. Nargiza Alymkulova & Nurlan Atabaev & Junus Ganiev, 2016. "Var — analysis of global financial economic crisis impact on public budget and unemployment: evidence from the economy of the Kyrgyz republic," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(4), pages 1090-1101.
    15. Zeileis, Achim & Koenker, Roger, 2008. "Econometrics in R: Past, Present, and Future," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i01).
    16. Bełej Mirosław, 2022. "Analysis of the Relationship Between COVID-19 Infections and Web-Based Housing Searches," Real Estate Management and Valuation, Sciendo, vol. 30(4), pages 89-102, December.
    17. Lyócsa, Štefan & Todorova, Neda, 2021. "What drives volatility of the U.S. oil and gas firms?," Energy Economics, Elsevier, vol. 100(C).
    18. Anh Tru Nguyen, 2018. "The Relationship among Economic Growth, Trade, Unemployment, and Inflation in South Asia: A Vector Autoregressive Model Approach," Asian Journal of Economics and Empirical Research, Asian Online Journal Publishing Group, vol. 5(2), pages 165-172.
    19. Matteo Farn'e & Angela Montanari, 2018. "A bootstrap test to detect prominent Granger-causalities across frequencies," Papers 1803.00374, arXiv.org, revised Oct 2018.
    20. Chris Heaton & Natalia Ponomareva & Qin Zhang, 2020. "Forecasting models for the Chinese macroeconomy: the simpler the better?," Empirical Economics, Springer, vol. 58(1), pages 139-167, January.

    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:scn:00rbes:y:2015:i:2:p:67-68. 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: Алексей Скалабан (email available below). General contact details of provider: http://rbes.fa.ru/ .

    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.