Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1708.08622.html
   My bibliography  Save this paper

Measurement of Common Risk Factors: A Panel Quantile Regression Model for Returns

Author

Listed:
  • Frantisek Cech
  • Jozef Barunik
Abstract
This paper investigates how to measure common market risk factors using newly proposed Panel Quantile Regression Model for Returns. By exploring the fact that volatility crosses all quantiles of the return distribution and using penalized fixed effects estimator we are able to control for otherwise unobserved heterogeneity among financial assets. Direct benefits of the proposed approach are revealed in the portfolio Value-at-Risk forecasting application, where our modeling strategy performs significantly better than several benchmark models according to both statistical and economic comparison. In particular Panel Quantile Regression Model for Returns consistently outperforms all the competitors in the 5\% and 10\% quantiles. Sound statistical performance translates directly into economic gains which is demonstrated in the Global Minimum Value-at-Risk Portfolio and Markowitz-like comparison. Overall results of our research are important for correct identification of the sources of systemic risk, and are particularly attractive for high dimensional applications.

Suggested Citation

  • Frantisek Cech & Jozef Barunik, 2017. "Measurement of Common Risk Factors: A Panel Quantile Regression Model for Returns," Papers 1708.08622, arXiv.org.
    • Handle: RePEc:arx:papers:1708.08622
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1708.08622
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Giacomini, Raffaella & Komunjer, Ivana, 2005. "Evaluation and Combination of Conditional Quantile Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 416-431, October.
    2. Manski, Charles F., 1986. "Ordinal Utility Models Of Decision Making Under Uncertainty," SSRI Workshop Series 292682, University of Wisconsin-Madison, Social Systems Research Institute.
    3. Neil Foster-McGregor & Anders Isaksson & Florian Kaulich, 2014. "Importing, exporting and performance in sub-Saharan African manufacturing firms," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 150(2), pages 309-336, May.
    4. Gilbert W. Bassett, 2004. "Pessimistic Portfolio Allocation and Choquet Expected Utility," Journal of Financial Econometrics, Oxford University Press, vol. 2(4), pages 477-492.
    5. Kato, Kengo & F. Galvao, Antonio & Montes-Rojas, Gabriel V., 2012. "Asymptotics for panel quantile regression models with individual effects," Journal of Econometrics, Elsevier, vol. 170(1), pages 76-91.
    6. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    7. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    8. Ole E. Barndorff-Nielsen & Neil Shephard, 2006. "Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation," Journal of Financial Econometrics, Oxford University Press, vol. 4(1), pages 1-30.
    9. David Powell & Joachim Wagner, 2021. "The Exporter Productivity Premium Along the Productivity Distribution: Evidence from Quantile Regression with Nonadditive Firm Fixed Effects," World Scientific Book Chapters, in: Joachim Wagner (ed.), MICROECONOMETRIC STUDIES OF FIRMS’ IMPORTS AND EXPORTS Advanced Methods of Analysis and Evidence from German Enterprises, chapter 9, pages 121-149, World Scientific Publishing Co. Pte. Ltd..
    10. Baur, Dirk G. & Dimpfl, Thomas & Jung, Robert C., 2012. "Stock return autocorrelations revisited: A quantile regression approach," Journal of Empirical Finance, Elsevier, vol. 19(2), pages 254-265.
    11. Zhang, Yue-Jun & Peng, Hua-Rong & Liu, Zhao & Tan, Weiping, 2015. "Direct energy rebound effect for road passenger transport in China: A dynamic panel quantile regression approach," Energy Policy, Elsevier, vol. 87(C), pages 303-313.
    12. Clements, Michael P. & Galvão, Ana Beatriz & Kim, Jae H., 2008. "Quantile forecasts of daily exchange rate returns from forecasts of realized volatility," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 729-750, September.
    13. Giovannetti, Bruno C., 2013. "Asset pricing under quantile utility maximization," Review of Financial Economics, Elsevier, vol. 22(4), pages 169-179.
    14. Covas, Francisco B. & Rump, Ben & Zakrajšek, Egon, 2014. "Stress-testing US bank holding companies: A dynamic panel quantile regression approach," International Journal of Forecasting, Elsevier, vol. 30(3), pages 691-713.
    15. Andrew J. Patton & Kevin Sheppard, 2015. "Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility," The Review of Economics and Statistics, MIT Press, vol. 97(3), pages 683-697, July.
    16. Galvao, Antonio F. & Wang, Liang, 2015. "Efficient minimum distance estimator for quantile regression fixed effects panel data," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 1-26.
    17. Gilles Dufrenot & Valerie Mignon & Charalambos Tsangarides, 2010. "The trade-growth nexus in the developing countries: a quantile regression approach," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 146(4), pages 731-761, December.
    18. Christian M. Dahl & Daniel le Maire & Jakob R. Munch, 2013. "Wage Dispersion and Decentralization of Wage Bargaining," Journal of Labor Economics, University of Chicago Press, vol. 31(3), pages 501-533.
    19. Daníelsson, Jón & Jorgensen, Bjørn N. & Samorodnitsky, Gennady & Sarma, Mandira & de Vries, Casper G., 2013. "Fat tails, VaR and subadditivity," Journal of Econometrics, Elsevier, vol. 172(2), pages 283-291.
    20. White, Halbert & Kim, Tae-Hwan & Manganelli, Simone, 2015. "VAR for VaR: Measuring tail dependence using multivariate regression quantiles," Journal of Econometrics, Elsevier, vol. 187(1), pages 169-188.
    21. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    22. Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
    23. Harding, Matthew & Lamarche, Carlos, 2009. "A quantile regression approach for estimating panel data models using instrumental variables," Economics Letters, Elsevier, vol. 104(3), pages 133-135, September.
    24. Damette, Olivier & Delacote, Philippe, 2012. "On the economic factors of deforestation: What can we learn from quantile analysis?," Economic Modelling, Elsevier, vol. 29(6), pages 2427-2434.
    25. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    26. Harding, Matthew & Lamarche, Carlos, 2014. "Estimating and testing a quantile regression model with interactive effects," Journal of Econometrics, Elsevier, vol. 178(P1), pages 101-113.
    27. Alexander Kempf & Christoph Memmel, 2006. "Estimating the global Minimum Variance Portfolio," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 58(4), pages 332-348, October.
    28. Ott Toomet, 2011. "Learn English, Not the Local Language! Ethnic Russians in the Baltic States," American Economic Review, American Economic Association, vol. 101(3), pages 526-531, May.
    29. Zhang, Lan & Mykland, Per A. & Ait-Sahalia, Yacine, 2005. "A Tale of Two Time Scales: Determining Integrated Volatility With Noisy High-Frequency Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1394-1411, December.
    30. Lorenzo Cappiello & Bruno Gérard & Arjan Kadareja & Simone Manganelli, 2014. "Measuring Comovements by Regression Quantiles," Journal of Financial Econometrics, Oxford University Press, vol. 12(4), pages 645-678.
    31. Ivan A. Canay, 2011. "A simple approach to quantile regression for panel data," Econometrics Journal, Royal Economic Society, vol. 14(3), pages 368-386, October.
    32. Andersen, Torben G. & Bollerslev, Tim & Huang, Xin, 2011. "A reduced form framework for modeling volatility of speculative prices based on realized variation measures," Journal of Econometrics, Elsevier, vol. 160(1), pages 176-189, January.
    33. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    34. Klomp, Jeroen & Haan, Jakob de, 2012. "Banking risk and regulation: Does one size fit all?," Journal of Banking & Finance, Elsevier, vol. 36(12), pages 3197-3212.
    35. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    36. Bryan S. Graham & Jinyong Hahn & Alexandre Poirier & James L. Powell, 2015. "Quantile regression with panel data," CeMMAP working papers CWP12/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    37. Sherrilyn Billger & Carlos Lamarche, 2015. "A panel data quantile regression analysis of the immigrant earnings distribution in the United Kingdom and United States," Empirical Economics, Springer, vol. 49(2), pages 705-750, September.
    38. 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.
    39. Chambers, Christopher P., 2007. "Ordinal aggregation and quantiles," Journal of Economic Theory, Elsevier, vol. 137(1), pages 416-431, November.
    40. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
    41. Jeremy Berkowitz & Peter Christoffersen & Denis Pelletier, 2011. "Evaluating Value-at-Risk Models with Desk-Level Data," Management Science, INFORMS, vol. 57(12), pages 2213-2227, December.
    42. Lamarche, Carlos, 2011. "Measuring the incentives to learn in Colombia using new quantile regression approaches," Journal of Development Economics, Elsevier, vol. 96(2), pages 278-288, November.
    43. Alessia Matano & Paolo Naticchioni, 2012. "Wage distribution and the spatial sorting of workers," Journal of Economic Geography, Oxford University Press, vol. 12(2), pages 379-408, March.
    44. Antonio F. Galvao & Gabriel Montes-Rojas, 2015. "On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study," Econometrics, MDPI, vol. 3(3), pages 1-13, September.
    45. Lamarche, Carlos, 2010. "Robust penalized quantile regression estimation for panel data," Journal of Econometrics, Elsevier, vol. 157(2), pages 396-408, August.
    46. Marzena Rostek, 2010. "Quantile Maximization in Decision Theory ," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(1), pages 339-371.
    47. You, Wan-Hai & Zhu, Hui-Ming & Yu, Keming & Peng, Cheng, 2015. "Democracy, Financial Openness, and Global Carbon Dioxide Emissions: Heterogeneity Across Existing Emission Levels," World Development, Elsevier, vol. 66(C), pages 189-207.
    48. Lamarche, Carlos, 2008. "Private school vouchers and student achievement: A fixed effects quantile regression evaluation," Labour Economics, Elsevier, vol. 15(4), pages 575-590, August.
    49. Lee, Jen-Sin & Huang, Gow-Liang & Kuo, Chin-Tai & Lee, Liang-Chien, 2012. "The momentum effect on Chinese real estate stocks: Evidence from firm performance levels," Economic Modelling, Elsevier, vol. 29(6), pages 2392-2406.
    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. Tobias Adrian & Federico Grinberg & Nellie Liang & Sheheryar Malik & Jie Yu, 2022. "The Term Structure of Growth-at-Risk," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(3), pages 283-323, July.

    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. Baruník, Jozef & Čech, František, 2021. "Measurement of common risks in tails: A panel quantile regression model for financial returns," Journal of Financial Markets, Elsevier, vol. 52(C).
    2. Panayiotis Tzeremes, 2022. "The Asymmetric Effects of Regional House Prices in the UK: New Evidence from Panel Quantile Regression Framework," Studies in Microeconomics, , vol. 10(1), pages 7-22, June.
    3. Filip Žikeš & Jozef Baruník, 2016. "Semi-parametric Conditional Quantile Models for Financial Returns and Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 14(1), pages 185-226.
    4. Manuel Arellano & Stéphane Bonhomme, 2016. "Nonlinear panel data estimation via quantile regressions," Econometrics Journal, Royal Economic Society, vol. 19(3), pages 61-94, October.
    5. Santos, Douglas G. & Candido, Osvaldo & Tófoli, Paula V., 2022. "Forecasting risk measures using intraday and overnight information," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    6. Zhu, Huiming & Duan, Lijun & Guo, Yawei & Yu, Keming, 2016. "The effects of FDI, economic growth and energy consumption on carbon emissions in ASEAN-5: Evidence from panel quantile regression," Economic Modelling, Elsevier, vol. 58(C), pages 237-248.
    7. Dogan, Eyup & Altinoz, Buket & Tzeremes, Panayiotis, 2020. "The analysis of ‘Financial Resource Curse’ hypothesis for developed countries: Evidence from asymmetric effects with quantile regression," Resources Policy, Elsevier, vol. 68(C).
    8. You, Wan-Hai & Zhu, Hui-Ming & Yu, Keming & Peng, Cheng, 2015. "Democracy, Financial Openness, and Global Carbon Dioxide Emissions: Heterogeneity Across Existing Emission Levels," World Development, Elsevier, vol. 66(C), pages 189-207.
    9. Galvao, Antonio F. & Gu, Jiaying & Volgushev, Stanislav, 2020. "On the unbiased asymptotic normality of quantile regression with fixed effects," Journal of Econometrics, Elsevier, vol. 218(1), pages 178-215.
    10. You, Wanhai & Guo, Yawei & Zhu, Huiming & Tang, Yong, 2017. "Oil price shocks, economic policy uncertainty and industry stock returns in China: Asymmetric effects with quantile regression," Energy Economics, Elsevier, vol. 68(C), pages 1-18.
    11. Zhang, Yingying & Wang, Huixia Judy & Zhu, Zhongyi, 2019. "Quantile-regression-based clustering for panel data," Journal of Econometrics, Elsevier, vol. 213(1), pages 54-67.
    12. Philip Kostov & Julie Le Gallo, 2018. "What role for human capital in the growth process: new evidence from endogenous latent factor panel quantile regressions," Scottish Journal of Political Economy, Scottish Economic Society, vol. 65(5), pages 501-527, November.
    13. Tomohiro Ando & Jushan Bai, 2020. "Quantile Co-Movement in Financial Markets: A Panel Quantile Model With Unobserved Heterogeneity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 266-279, January.
    14. 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.
    15. Zongwu Cai & Chaoqun Ma & Xianhua Mi, 2020. "Realized Volatility Forecasting Based on Dynamic Quantile Model Averaging," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202016, University of Kansas, Department of Economics, revised Sep 2020.
    16. Abid, Nabila & Ahmad, Fayyaz & Aftab, Junaid & Razzaq, Asif, 2023. "A blessing or a burden? Assessing the impact of Climate Change Mitigation efforts in Europe using Quantile Regression Models," Energy Policy, Elsevier, vol. 178(C).
    17. Luciano de Castro & Antonio F. Galvao & Gabriel Montes-Rojas & Jose Olmo, 2022. "Portfolio selection in quantile decision models," Annals of Finance, Springer, vol. 18(2), pages 133-181, June.
    18. Chao-Qun Ma & Jiang-Long Liu & Yi-Shuai Ren & Yong Jiang, 2019. "The Impact of Economic Growth, FDI and Energy Intensity on China’s Manufacturing Industry’s CO 2 Emissions: An Empirical Study Based on the Fixed-Effect Panel Quantile Regression Model," Energies, MDPI, vol. 12(24), pages 1-16, December.
    19. Sherrilyn Billger & Carlos Lamarche, 2015. "A panel data quantile regression analysis of the immigrant earnings distribution in the United Kingdom and United States," Empirical Economics, Springer, vol. 49(2), pages 705-750, September.
    20. Alexander Blasberg & Rüdiger Kiesel & Luca Taschini, 2022. "Carbon Default Swap - Disentangling the Exposure to Carbon Risk through CDS," CESifo Working Paper Series 10016, CESifo.

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:1708.08622. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.