IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v45y2022ics1544612321002154.html
   My bibliography  Save this article

A Value-at-Risk forecastability indicator in the framework of a Generalized Autoregressive Score with “Asymmetric Laplace Distribution”

Author

Listed:
  • Bogdan, Dima
  • Ştefana Maria, Dima
  • Roxana, Ioan

Abstract

In the present paper we discuss the forecasting ability of the VaR model, within the context of a Generalized Autoregressive Score (GAS). The proposed method considers an Asymmetric Laplace Distribution (GAS-ALD) to describe the daily log-returns of the analyzed data. A forecastability indicator for a certain probability of VaR is proposed. The approach uses back-testing and several unconditional and conditional tests, to see whether or not the GAS-ALD model accurately describes the specificity of log-returns’ distribution. Our results show a strong connection between this indicator and the informational efficiency of financial markets, as it indirectly reflects shifts in market efficiency.

Suggested Citation

  • Bogdan, Dima & Ştefana Maria, Dima & Roxana, Ioan, 2022. "A Value-at-Risk forecastability indicator in the framework of a Generalized Autoregressive Score with “Asymmetric Laplace Distribution”," Finance Research Letters, Elsevier, vol. 45(C).
  • Handle: RePEc:eee:finlet:v:45:y:2022:i:c:s1544612321002154
    DOI: 10.1016/j.frl.2021.102134
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612321002154
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.frl.2021.102134?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Peter C. B. Phillips & Shuping Shi & Jun Yu, 2015. "Testing For Multiple Bubbles: Historical Episodes Of Exuberance And Collapse In The S&P 500," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(4), pages 1043-1078, November.
    2. Giot, Pierre & Laurent, Sebastien, 2004. "Modelling daily Value-at-Risk using realized volatility and ARCH type models," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 379-398, June.
    3. Mario Gruppe & Tobias Basse & Meik Friedrich & Carsten Lange, 2017. "Interest rate convergence, sovereign credit risk and the European debt crisis: a survey," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 18(4), pages 432-442, August.
    4. Peter C. B. Phillips & Yangru Wu & Jun Yu, 2011. "EXPLOSIVE BEHAVIOR IN THE 1990s NASDAQ: WHEN DID EXUBERANCE ESCALATE ASSET VALUES?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 52(1), pages 201-226, February.
    5. Pan, Jun, 2002. "The jump-risk premia implicit in options: evidence from an integrated time-series study," Journal of Financial Economics, Elsevier, vol. 63(1), pages 3-50, January.
    6. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    7. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    8. Michael McAleer & Bernardo da Veiga, 2008. "Single-index and portfolio models for forecasting value-at-risk thresholds," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 217-235.
    9. Zhang, Dayong & Hu, Min & Ji, Qiang, 2020. "Financial markets under the global pandemic of COVID-19," Finance Research Letters, Elsevier, vol. 36(C).
    10. Garland Durham & Yang-Ho Park, 2013. "Beyond Stochastic Volatility and Jumps in Returns and Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 107-121, January.
    11. Batten, Jonathan A. & Kinateder, Harald & Wagner, Niklas, 2014. "Multifractality and value-at-risk forecasting of exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 71-81.
    12. Angelidis, Timotheos & Degiannakis, Stavros, 2008. "Volatility forecasting: Intra-day versus inter-day models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(5), pages 449-465, December.
    13. Christopher Mayer, 2011. "Housing Bubbles: A Survey," Annual Review of Economics, Annual Reviews, vol. 3(1), pages 559-577, September.
    14. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    15. Peter C. B. Phillips & Shuping Shi & Jun Yu, 2015. "Testing For Multiple Bubbles: Limit Theory Of Real‐Time Detectors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56, pages 1079-1134, November.
    16. Phillips, Peter C.B. & Shi, Shu-Ping, 2018. "Financial Bubble Implosion And Reverse Regression," Econometric Theory, Cambridge University Press, vol. 34(4), pages 705-753, August.
    17. Peter C. B. Phillips & Shuping Shi & Jun Yu, 2015. "Testing For Multiple Bubbles: Historical Episodes Of Exuberance And Collapse In The S&P 500," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56, pages 1043-1078, November.
    18. 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.
    19. Gonzalez-Rivera, Gloria & Lee, Tae-Hwy & Mishra, Santosh, 2004. "Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood," International Journal of Forecasting, Elsevier, vol. 20(4), pages 629-645.
    20. Brian Boyer & Todd Mitton & Keith Vorkink, 2010. "Expected Idiosyncratic Skewness," Review of Financial Studies, Society for Financial Studies, vol. 23(1), pages 169-202, January.
    21. Peter C. B. Phillips & Shuping Shi & Jun Yu, 2015. "Testing For Multiple Bubbles: Limit Theory Of Real‐Time Detectors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(4), pages 1079-1134, November.
    22. Peter C. B. Phillips & Shuping Shi, 2019. "Detecting Financial Collapse and Ballooning Sovereign Risk," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(6), pages 1336-1361, December.
    23. Sanders, Anthony, 2008. "The subprime crisis and its role in the financial crisis," Journal of Housing Economics, Elsevier, vol. 17(4), pages 254-261, December.
    24. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    25. Lee, Suzanne S. & Hannig, Jan, 2010. "Detecting jumps from Lévy jump diffusion processes," Journal of Financial Economics, Elsevier, vol. 96(2), pages 271-290, May.
    26. Basse, Tobias & Klein, Tony & Vigne, Samuel A. & Wegener, Christoph, 2021. "U.S. stock prices and the dot.com-bubble: Can dividend policy rescue the efficient market hypothesis?," Journal of Corporate Finance, Elsevier, vol. 67(C).
    27. Tholl, Johannes & Schwarzbach, Christoph & Pittalis, Sandro & von Mettenheim, Hans-Jörg, 2020. "Bank funding and the recent political development in Italy: What about redenomination risk?," International Review of Law and Economics, Elsevier, vol. 64(C).
    28. Satchell, Stephen, 2007. "Forecasting Expected Returns in the Financial Markets," Elsevier Monographs, Elsevier, edition 1, number 9780750683210.
    29. Drew Creal & Siem Jan Koopman & André Lucas, 2013. "Generalized Autoregressive Score Models With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 777-795, August.
    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. Esteve, Vicente & Prats, María A., 2023. "Testing explosive bubbles with time-varying volatility: the case of Spanish public debt," LSE Research Online Documents on Economics 116980, London School of Economics and Political Science, LSE Library.
    2. Yang, Hui & Ferrer, Román, 2023. "Explosive behavior in the Chinese stock market: A sectoral analysis," Pacific-Basin Finance Journal, Elsevier, vol. 81(C).
    3. Esteve, Vicente & Prats, María A., 2023. "Testing explosive bubbles with time-varying volatility: The case of Spanish public debt," Finance Research Letters, Elsevier, vol. 51(C).
    4. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    5. Aktham Maghyereh & Hussein Abdoh, 2022. "Bubble contagion effect between the main precious metals," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 40(1), pages 43-63, March.
    6. Gomis-Porqueras, Pedro & Shi, Shuping & Tan, David, 2022. "Gold as a financial instrument," Journal of Commodity Markets, Elsevier, vol. 27(C).
    7. Leopoldo Catania & Nima Nonejad, 2016. "Density Forecasts and the Leverage Effect: Some Evidence from Observation and Parameter-Driven Volatility Models," Papers 1605.00230, arXiv.org, revised Nov 2016.
    8. Erik Kole & Thijs Markwat & Anne Opschoor & Dick van Dijk, 2017. "Forecasting Value-at-Risk under Temporal and Portfolio Aggregation," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 649-677.
    9. Blasques, Francisco & Koopman, Siem Jan & Nientker, Marc, 2022. "A time-varying parameter model for local explosions," Journal of Econometrics, Elsevier, vol. 227(1), pages 65-84.
    10. Wang, Xichen & Yan, Ji (Karena) & Yan, Cheng & Gozgor, Giray, 2021. "Emerging stock market exuberance and international short-term flows," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    11. Gharib, Cheima & Mefteh-Wali, Salma & Serret, Vanessa & Ben Jabeur, Sami, 2021. "Impact of COVID-19 pandemic on crude oil prices: Evidence from Econophysics approach," Resources Policy, Elsevier, vol. 74(C).
    12. Yang, Bingduo & Long, Wei & Yang, Zihui, 2022. "Testing predictability of stock returns under possible bubbles," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 246-260.
    13. Francisco Blasques & Siem Jan Koopman & Gabriele Mingoli, 2023. "Observation-Driven filters for Time-Series with Stochastic Trends and Mixed Causal Non-Causal Dynamics," Tinbergen Institute Discussion Papers 23-065/III, Tinbergen Institute.
    14. Jalan, Akanksha & Matkovskyy, Roman & Potì, Valerio, 2022. "Shall the winning last? A study of recent bubbles and persistence," Finance Research Letters, Elsevier, vol. 45(C).
    15. Yu, Lu & Li, Yanglin, 2023. "Testing factor models when asset bubbles occur: A time-varying perspective," Economic Modelling, Elsevier, vol. 124(C).
    16. Donggyu Kim & Minseog Oh & Yazhen Wang, 2022. "Conditional quantile analysis for realized GARCH models," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 640-665, July.
    17. Vincenzo Candila & Giampiero M. Gallo & Lea Petrella, 2020. "Mixed--frequency quantile regressions to forecast Value--at--Risk and Expected Shortfall," Papers 2011.00552, arXiv.org, revised Mar 2023.
    18. František Čech & Jozef Baruník, 2019. "Panel quantile regressions for estimating and predicting the value‐at‐risk of commodities," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(9), pages 1167-1189, September.
    19. Chen, Cathy W.S. & Gerlach, Richard & Hwang, Bruce B.K. & McAleer, Michael, 2012. "Forecasting Value-at-Risk using nonlinear regression quantiles and the intra-day range," International Journal of Forecasting, Elsevier, vol. 28(3), pages 557-574.
    20. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.

    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:eee:finlet:v:45:y:2022:i:c:s1544612321002154. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    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.