IDEAS home Printed from https://ideas.repec.org/a/bkr/journl/v81y2022i1p23-45.html

Comparison of Models for Growth-at-Risk Forecasting

Author

Listed:
  • Aleksei Kipriyanov

    (HSE University, International College of Economics and Finance)

Abstract

During the past several decades, the importance of assessing the risk of GDP growth downturns has increased tremendously. The financial crisis of 2008-2009 and the global lockdown caused by the COVID-19 pandemic demonstrated the vulnerability of the modern economy. As a result, a new framework (Growth-at-Risk) has been developed which allows the estimation of the size of the potential downfall of future GDP growth. However, most of the research focuses on the performance of quantile regression. I apply different approaches to forecasting growth-at-risk, including quantile regression, quantile random forests, and generalised autoregressive conditional heteroscedastic (GARCH) models, using the US economy for the analysis. I find that GARCH-type models perform worse at 5% and 10% coverage levels, but that quantile random forests and quantile regressions seem to have equal predictive ability.

Suggested Citation

  • Aleksei Kipriyanov, 2022. "Comparison of Models for Growth-at-Risk Forecasting," Russian Journal of Money and Finance, Bank of Russia, vol. 81(1), pages 23-45, March.
    • Handle: RePEc:bkr:journl:v:81:y:2022:i:1:p:23-45
    DOI: 10.31477/rjmf.202201.23
    as

    Download full text from publisher

    File URL: https://rjmf.econs.online/upload/iblock/73c/Comparison-of-Models-for-Growth-at-Risk-Forecasting.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.31477/rjmf.202201.23?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    2. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Stijn Claessens & M. Ayhan Kose & Marco E. Terrones, 2009. "What happens during recessions, crunches and busts? [Business cycles for G-7 and European countries]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 24(60), pages 653-700.
    5. Brownlees, Christian & Souza, André B.M., 2021. "Backtesting global Growth-at-Risk," Journal of Monetary Economics, Elsevier, vol. 118(C), pages 312-330.
    6. David Aikman & Jonathan Bridges & Sinem Hacioglu Hoke & Cian O’Neill & Akash Raja, 2019. "Credit, capital and crises: a GDP-at-Risk approach," Bank of England working papers 824, Bank of England.
    7. Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2019. "Vulnerable Growth," American Economic Review, American Economic Association, vol. 109(4), pages 1263-1289, April.
    8. Philip Lowe & Claudio Borio, 2002. "Asset prices, financial and monetary stability: exploring the nexus," BIS Working Papers 114, Bank for International Settlements.
    9. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2018. "An evaluation of early warning models for systemic banking crises: Does machine learning improve predictions?," Discussion Papers 48/2018, Deutsche Bundesbank.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lorena Skufi & Adam Geršl, 2023. "Using Macrofinancial Models to Simulate Macroeconomic Developments During the COVID-19 Pandemic: The Case of Albania," Eastern European Economics, Taylor & Francis Journals, vol. 61(5), pages 517-553, September.

    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. Ngozi G. Emenogu & Monday Osagie Adenomon & Nwaze Obini Nweze, 2020. "On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtesting," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-25, December.
    2. Marcelo Cunha Medeiros & Alvaro Veiga, 2004. "Modelling multiple regimes in financial volatility with a flexible coefficient GARCH model," Textos para discussão 486, Department of Economics PUC-Rio (Brazil).
    3. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2009. "Asymmetric multivariate normal mixture GARCH," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2129-2154, April.
    4. Meriem Rjiba & Michail Tsagris & Hedi Mhalla, 2015. "Bootstrap for Value at Risk Prediction," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 4(6), pages 362-371.
    5. repec:wyi:journl:002190 is not listed on IDEAS
    6. João Caldeira & Guilherme Moura & André Santos, 2015. "Measuring Risk in Fixed Income Portfolios using Yield Curve Models," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 65-82, June.
    7. Paul Bui Quang & Tony Klein & Nam H. Nguyen & Thomas Walther, 2018. "Value-at-Risk for South-East Asian Stock Markets: Stochastic Volatility vs. GARCH," JRFM, MDPI, vol. 11(2), pages 1-20, April.
    8. Daniel Velásquez-Gaviria & Andrés Mora-Valencia & Javier Perote, 2020. "A Comparison of the Risk Quantification in Traditional and Renewable Energy Markets," Energies, MDPI, vol. 13(11), pages 1-42, June.
    9. Karmakar, Madhusudan & Paul, Samit, 2016. "Intraday risk management in International stock markets: A conditional EVT approach," International Review of Financial Analysis, Elsevier, vol. 44(C), pages 34-55.
    10. Stavroyiannis, S. & Makris, I. & Nikolaidis, V. & Zarangas, L., 2012. "Econometric modeling and value-at-risk using the Pearson type-IV distribution," International Review of Financial Analysis, Elsevier, vol. 22(C), pages 10-17.
    11. Díaz-Hernández, Adán & Constantinou, Nick, 2019. "A multiple regime extension to the Heston–Nandi GARCH(1,1) model," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 162-180.
    12. Van Cauwenberge, Annelies & Vancauteren, Mark & Braekers, Roel & Vandemaele, Sigrid, 2019. "International trade, foreign direct investments, and firms’ systemic risk : Evidence from the Netherlands," Economic Modelling, Elsevier, vol. 81(C), pages 361-386.
    13. Timotheos Angelidis & Stavros Degiannakis, 2007. "Backtesting VaR Models: An Expected Shortfall Approach," Working Papers 0701, University of Crete, Department of Economics.
    14. Kulp-Tåg, Sofie, 2007. "An Empirical Investigation of Value-at-Risk in Long and Short Trading Positions," Working Papers 526, Hanken School of Economics.
    15. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc, 2006. "Accurate value-at-risk forecasting based on the normal-GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2295-2312, December.
    16. Li, Muyi & Li, Wai Keung & Li, Guodong, 2015. "A new hyperbolic GARCH model," Journal of Econometrics, Elsevier, vol. 189(2), pages 428-436.
    17. Karmakar, Madhusudan & Shukla, Girja K., 2015. "Managing extreme risk in some major stock markets: An extreme value approach," International Review of Economics & Finance, Elsevier, vol. 35(C), pages 1-25.
    18. Carol Alexander & Emese Lazar, 2009. "Modelling Regime‐Specific Stock Price Volatility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(6), pages 761-797, December.
    19. Kris Boudt & Hong Anh Luu, 2022. "Estimation and decomposition of food price inflation risk," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 295-319, June.
    20. Bali, Turan G. & Mo, Hengyong & Tang, Yi, 2008. "The role of autoregressive conditional skewness and kurtosis in the estimation of conditional VaR," Journal of Banking & Finance, Elsevier, vol. 32(2), pages 269-282, February.
    21. Degiannakis, Stavros, 2004. "Forecasting Realized Intra-day Volatility and Value at Risk: Evidence from a Fractional Integrated Asymmetric Power ARCH Skewed-t Model," MPRA Paper 80488, University Library of Munich, Germany.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

    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:bkr:journl:v:81:y:2022:i:1:p:23-45. 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 Kuvshinova (email available below). General contact details of provider: https://edirc.repec.org/data/cbrgvru.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.