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Forecasting expected shortfall and value at risk with a joint elicitable mixed data sampling model

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  • Qifa Xu
  • Lu Chen
  • Cuixia Jiang
  • Yezheng Liu

Abstract

Low‐frequency risk measures can filter out noise and better reflect the trend. In order to improve the forecasting accuracy of low‐frequency risk through making full use of the valuable information contained in high‐frequency independent variables, we propose a novel joint elicitable mixed data sampling (JE‐MIDAS) model by introducing MIDAS method into JE regression model. We utilize the JE‐MIDAS model to forecast value at risk and expected shortfall simultaneously and compare its performance with that of other popular models through both the Monte Carlo simulations and real‐world applications. The numerical results show that our model is superior to other models because it can model mixed‐frequency data directly, which avoids the information loss caused by frequency conversion. The empirical results on three stock indices also show that market volatility can increase financial risk. Interest rate has positive effects on risks for U.S. S&P500 and U.K. FTSE, whereas negative for SHCI of China.

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  • Qifa Xu & Lu Chen & Cuixia Jiang & Yezheng Liu, 2022. "Forecasting expected shortfall and value at risk with a joint elicitable mixed data sampling model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 407-421, April.
    • Handle: RePEc:wly:jforec:v:41:y:2022:i:3:p:407-421
    DOI: 10.1002/for.2817
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    as
    1. Natalia Nolde & Johanna F. Ziegel, 2016. "Elicitability and backtesting: Perspectives for banking regulation," Papers 1608.05498, arXiv.org, revised Feb 2017.
    2. Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2015. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," International Journal of Forecasting, Elsevier, vol. 31(2), pages 238-252.
    3. Gerlach, Richard & Wang, Chao, 2020. "Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures," International Journal of Forecasting, Elsevier, vol. 36(2), pages 489-506.
    4. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    5. Hyun Hak Kim & Norman R. Swanson, 2018. "Methods for backcasting, nowcasting and forecasting using factor†MIDAS: With an application to Korean GDP," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(3), pages 281-302, April.
    6. Bhattacharyay, Biswa Nath, 2013. "Determinants of bond market development in Asia," Journal of Asian Economics, Elsevier, vol. 24(C), pages 124-137.
    7. Campbell, John Y, 1991. "A Variance Decomposition for Stock Returns," Economic Journal, Royal Economic Society, vol. 101(405), pages 157-179, March.
    8. Eric Ghysels & Alberto Plazzi & Rossen Valkanov, 2016. "Why Invest in Emerging Markets? The Role of Conditional Return Asymmetry," Journal of Finance, American Finance Association, vol. 71(5), pages 2145-2192, October.
    9. Patton, Andrew J. & Ziegel, Johanna F. & Chen, Rui, 2019. "Dynamic semiparametric models for expected shortfall (and Value-at-Risk)," Journal of Econometrics, Elsevier, vol. 211(2), pages 388-413.
    10. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    11. Yuyan Tan & Yang Ji & Yiping Huang, 2016. "Completing China's Interest Rate Liberalization," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 24(2), pages 1-22, March.
    12. 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.
    13. Christoffersen, Peter & Errunza, Vihang & Jacobs, Kris & Jin, Xisong, 2014. "Correlation dynamics and international diversification benefits," International Journal of Forecasting, Elsevier, vol. 30(3), pages 807-824.
    14. Hall, Robert E, 1988. "Intertemporal Substitution in Consumption," Journal of Political Economy, University of Chicago Press, vol. 96(2), pages 339-357, April.
    15. Xu, Qifa & Zhuo, Xingxuan & Jiang, Cuixia & Liu, Xi & Liu, Yezheng, 2018. "Group penalized unrestricted mixed data sampling model with application to forecasting US GDP growth," Economic Modelling, Elsevier, vol. 75(C), pages 221-236.
    16. 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.
    17. Helen Higgs & Andrew Worthington, 2004. "Transmission of returns and volatility in art markets: a multivariate GARCH analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 11(4), pages 217-222.
    18. Chen, Hongyi & Chow, Kenneth & Tillmann, Peter, 2017. "The effectiveness of monetary policy in China: Evidence from a Qual VAR," China Economic Review, Elsevier, vol. 43(C), pages 216-231.
    19. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    20. Andrew Worthington & Helen Higgs, 2004. "Transmission of equity returns and volatility in Asian developed and emerging markets: a multivariate GARCH analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 9(1), pages 71-80.
    21. He, Dong & Wang, Honglin, 2012. "Dual-track interest rates and the conduct of monetary policy in China," China Economic Review, Elsevier, vol. 23(4), pages 928-947.
    22. James W. Taylor, 2019. "Forecasting Value at Risk and Expected Shortfall Using a Semiparametric Approach Based on the Asymmetric Laplace Distribution," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(1), pages 121-133, January.
    23. Tarun K. Mukherjee & Atsuyuki Naka, 1995. "Dynamic Relations Between Macroeconomic Variables And The Japanese Stock Market: An Application Of A Vector Error Correction Model," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 18(2), pages 223-237, June.
    24. Guo, Feng & Hu, Jinyan & Jiang, Mingming, 2013. "Monetary shocks and asymmetric effects in an emerging stock market: The case of China," Economic Modelling, Elsevier, vol. 32(C), pages 532-538.
    25. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    26. Xu, Qifa & Chen, Lu & Jiang, Cuixia & Yuan, Jing, 2018. "Measuring systemic risk of the banking industry in China: A DCC-MIDAS-t approach," Pacific-Basin Finance Journal, Elsevier, vol. 51(C), pages 13-31.
    27. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    28. Ghysels, Eric & Kvedaras, Virmantas & Zemlys, Vaidotas, 2016. "Mixed Frequency Data Sampling Regression Models: The R Package midasr," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i04).
    29. Acerbi, Carlo & Tasche, Dirk, 2002. "On the coherence of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1487-1503, July.
    30. Colacito, Riccardo & Engle, Robert F. & Ghysels, Eric, 2011. "A component model for dynamic correlations," Journal of Econometrics, Elsevier, vol. 164(1), pages 45-59, September.
    31. Wen-Yi Chen, 2017. "Demographic structure and monetary policy effectiveness: evidence from Taiwan," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2521-2544, November.
    32. Gatfaoui, Hayette, 2013. "Translating financial integration into correlation risk: A weekly reporting's viewpoint for the volatility behavior of stock markets," Economic Modelling, Elsevier, vol. 30(C), pages 776-791.
    33. Peter Reinhard Hansen & Zhuo Huang & Howard Howan Shek, 2012. "Realized GARCH: a joint model for returns and realized measures of volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 877-906, September.
    34. Xu, Qifa & Chen, Lu & Jiang, Cuixia & Yu, Keming, 2020. "Mixed data sampling expectile regression with applications to measuring financial risk," Economic Modelling, Elsevier, vol. 91(C), pages 469-486.
    35. 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.
    36. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    37. Claudia Foroni & Massimiliano Marcellino & Christian Schumacher, 2015. "Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 57-82, January.
    38. Saralees Nadarajah & Bo Zhang & Stephen Chan, 2014. "Estimation methods for expected shortfall," Quantitative Finance, Taylor & Francis Journals, vol. 14(2), pages 271-291, February.
    39. Mukherjee, Tarun K & Naka, Atsuyuki, 1995. "Dynamic Relations between Macroeconomic Variables and the Japanese Stock Market: An Application of a Vector Error Correction Model," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 18(2), pages 223-237, Summer.
    40. Francesco P. Esposito & Mark Cummins, 2016. "Multiple Hypothesis Testing of Market Risk Forecasting Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(5), pages 381-399, August.
    41. James W. Taylor, 2008. "Estimating Value at Risk and Expected Shortfall Using Expectiles," Journal of Financial Econometrics, Oxford University Press, vol. 6(2), pages 231-252, Spring.
    42. Liu, Li-gang & Zhang, Wenlang, 2010. "A New Keynesian model for analysing monetary policy in Mainland China," Journal of Asian Economics, Elsevier, vol. 21(6), pages 540-551, December.
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