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Persistence-Robust Break Detection in Predictive Quantile and CoVaR Regressions

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  • Yannick Hoga

Abstract

Forecasting risk (as measured by quantiles) and systemic risk (as measured by Adrian and Brunnermeiers's (2016) CoVaR) is important in economics and finance. However, past research has shown that predictive relationships may be unstable over time. Therefore, this paper develops structural break tests in predictive quantile and CoVaR regressions. These tests can detect changes in the forecasting power of covariates, and are based on the principle of self-normalization. We show that our tests are valid irrespective of whether the predictors are stationary or near-stationary, rendering the tests suitable for a range of practical applications. Simulations illustrate the good finite-sample properties of our tests. Two empirical applications concerning equity premium and systemic risk forecasting models show the usefulness of the tests.

Suggested Citation

  • Yannick Hoga, 2024. "Persistence-Robust Break Detection in Predictive Quantile and CoVaR Regressions," Papers 2410.05861, arXiv.org.
    • Handle: RePEc:arx:papers:2410.05861
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    References listed on IDEAS

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    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Leland E. Farmer & Lawrence Schmidt & Allan Timmermann, 2023. "Pockets of Predictability," Journal of Finance, American Finance Association, vol. 78(3), pages 1279-1341, June.
    3. Fan, Rui & Lee, Ji Hyung & Shin, Youngki, 2023. "Predictive quantile regression with mixed roots and increasing dimensions: The ALQR approach," Journal of Econometrics, Elsevier, vol. 237(2).
    4. Paye, Bradley S. & Timmermann, Allan, 2006. "Instability of return prediction models," Journal of Empirical Finance, Elsevier, vol. 13(3), pages 274-315, June.
    5. Breitung, Jörg & Demetrescu, Matei, 2015. "Instrumental variable and variable addition based inference in predictive regressions," Journal of Econometrics, Elsevier, vol. 187(1), pages 358-375.
    6. Xiaofeng Shao, 2010. "Corrigendum: A self‐normalized approach to confidence interval construction in time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(5), pages 695-696, November.
    7. Viral V. Acharya & Lasse H. Pedersen & Thomas Philippon & Matthew Richardson, 2017. "Measuring Systemic Risk," The Review of Financial Studies, Society for Financial Studies, vol. 30(1), pages 2-47.
    8. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    9. Cai, Zongwu & Wang, Yunfei & Wang, Yonggang, 2015. "Testing Instability In A Predictive Regression Model With Nonstationary Regressors," Econometric Theory, Cambridge University Press, vol. 31(5), pages 953-980, October.
    10. Han, Heejoon & Linton, Oliver & Oka, Tatsushi & Whang, Yoon-Jae, 2016. "The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series," Journal of Econometrics, Elsevier, vol. 193(1), pages 251-270.
    11. Davidson, James, 1994. "Stochastic Limit Theory: An Introduction for Econometricians," OUP Catalogue, Oxford University Press, number 9780198774037.
    12. Christian Brownlees & Robert F. Engle, 2017. "SRISK: A Conditional Capital Shortfall Measure of Systemic Risk," The Review of Financial Studies, Society for Financial Studies, vol. 30(1), pages 48-79.
    13. Lee, Ji Hyung, 2016. "Predictive quantile regression with persistent covariates: IVX-QR approach," Journal of Econometrics, Elsevier, vol. 192(1), pages 105-118.
    14. Demetrescu, Matei & Georgiev, Iliyan & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2022. "Testing for episodic predictability in stock returns," Journal of Econometrics, Elsevier, vol. 227(1), pages 85-113.
    15. Chen, Long, 2009. "On the reversal of return and dividend growth predictability: A tale of two periods," Journal of Financial Economics, Elsevier, vol. 92(1), pages 128-151, April.
    16. Markus Brunnermeier & Simon Rother & Isabel Schnabel & Itay Goldstein, 2020. "Asset Price Bubbles and Systemic Risk," The Review of Financial Studies, Society for Financial Studies, vol. 33(9), pages 4272-4317.
    17. Magdalinos, Tassos & Phillips, Peter C.B., 2009. "Limit Theory For Cointegrated Systems With Moderately Integrated And Moderately Explosive Regressors," Econometric Theory, Cambridge University Press, vol. 25(2), pages 482-526, April.
    18. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    19. Georgiev, Iliyan & Harvey, David I. & Leybourne, Stephen J. & Taylor, A.M. Robert, 2018. "Testing for parameter instability in predictive regression models," Journal of Econometrics, Elsevier, vol. 204(1), pages 101-118.
    20. repec:taf:jnlbes:v:30:y:2012:i:2:p:212-228 is not listed on IDEAS
    21. Kato, Kengo, 2009. "Asymptotics for argmin processes: Convexity arguments," Journal of Multivariate Analysis, Elsevier, vol. 100(8), pages 1816-1829, September.
    22. Pesaran, M. Hashem & Pick, Andreas & Pranovich, Mikhail, 2013. "Optimal forecasts in the presence of structural breaks," Journal of Econometrics, Elsevier, vol. 177(2), pages 134-152.
    23. Cai, Zongwu & Chen, Haiqiang & Liao, Xiaosai, 2023. "A new robust inference for predictive quantile regression," Journal of Econometrics, Elsevier, vol. 234(1), pages 227-250.
    24. Pesaran, M. Hashem & Timmermann, Allan, 2002. "Market timing and return prediction under model instability," Journal of Empirical Finance, Elsevier, vol. 9(5), pages 495-510, December.
    25. Timo Dimitriadis & Yannick Hoga, 2022. "Dynamic CoVaR Modeling," Papers 2206.14275, arXiv.org, revised Feb 2024.
    26. Ting Zhang & Liliya Lavitas, 2018. "Unsupervised Self-Normalized Change-Point Testing for Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 637-648, April.
    27. Su, Liangjun & Xiao, Zhijie, 2008. "Testing for parameter stability in quantile regression models," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2768-2775, November.
    28. Chenxue Li & Deyuan Li & Liang Peng, 2017. "Uniform Test for Predictive Regression With AR Errors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 29-39, January.
    29. Campbell, John Y. & Yogo, Motohiro, 2006. "Efficient tests of stock return predictability," Journal of Financial Economics, Elsevier, vol. 81(1), pages 27-60, July.
    30. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    31. Xiaofeng Shao, 2010. "A self‐normalized approach to confidence interval construction in time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 343-366, June.
    32. Alexandros Kostakis & Tassos Magdalinos & Michalis P. Stamatogiannis, 2015. "Robust Econometric Inference for Stock Return Predictability," The Review of Financial Studies, Society for Financial Studies, vol. 28(5), pages 1506-1553.
    33. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    34. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    35. Tu, Yundong & Xie, Xinling, 2023. "Penetrating sporadic return predictability," Journal of Econometrics, Elsevier, vol. 237(1).
    36. Henkel, Sam James & Martin, J. Spencer & Nardari, Federico, 2011. "Time-varying short-horizon predictability," Journal of Financial Economics, Elsevier, vol. 99(3), pages 560-580, March.
    37. Milan Nedeljkovic, 2020. "A Projection-Based Nonparametric Test of Conditional Quantile Independence," Econometric Reviews, Taylor & Francis Journals, vol. 39(1), pages 1-26, January.
    38. Pettenuzzo, Davide & Timmermann, Allan, 2011. "Predictability of stock returns and asset allocation under structural breaks," Journal of Econometrics, Elsevier, vol. 164(1), pages 60-78, September.
    39. repec:taf:jnlbes:v:30:y:2012:i:2:p:229-241 is not listed on IDEAS
    40. Shao, Xiaofeng & Zhang, Xianyang, 2010. "Testing for Change Points in Time Series," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1228-1240.
    41. Timmermann, Allan, 2008. "Elusive return predictability," International Journal of Forecasting, Elsevier, vol. 24(1), pages 1-18.
    42. Giglio, Stefano & Kelly, Bryan & Pruitt, Seth, 2016. "Systemic risk and the macroeconomy: An empirical evaluation," Journal of Financial Economics, Elsevier, vol. 119(3), pages 457-471.
    43. Xiaofeng Shao, 2015. "Self-Normalization for Time Series: A Review of Recent Developments," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1797-1817, December.
    44. Stambaugh, Robert F., 1999. "Predictive regressions," Journal of Financial Economics, Elsevier, vol. 54(3), pages 375-421, December.
    45. Bingduo Yang & Xiaohui Liu & Liang Peng & Zongwu Cai, 2021. "Unified Tests for a Dynamic Predictive Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 684-699, July.
    46. Fan, Rui & Lee, Ji Hyung, 2019. "Predictive quantile regressions under persistence and conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 213(1), pages 261-280.
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