Conditional quantile change test for time series based on support vector regression

References

• Abe, S. 2005. Support vector machines for pattern classification, vol. 2. New York: Springer
   Google Scholar
• Anand, P., R. Rastogi, and S. Chandra. A new asymmetric -insensitive pinball loss function based support vector quantile regression model, 2019.
   Google Scholar
• Berkes, I., L. Horváth, and P. Kokoszka. 2004. Testing for parameter constancy in GARCH(p,q) models. Statistics & Probability Letters 70 (4):263–73. doi:10.1016/j.spl.2004.10.010.
   Web of Science ®Google Scholar
• Bezerra, P. C. S., and P. H. M. Albuquerque. 2017. Volatility forecasting via SVR–GARCH with mixture of Gaussian kernels. Computational Management Science 14 (2):179–96. doi:10.1007/s10287-016-0267-0.
   Web of Science ®Google Scholar
• Billingsley, P. 1968. Convergence of probability measure. New York: Wiley.
   Google Scholar
• Bloomfield, P., and W. L. Steiger. 1983. Least absolute deviations: Theory, applications, and algorithms. Boston: Birkhäuser.
   Google Scholar
• Bollerslev, T. 1986. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31 (3):307–27. doi:10.1016/0304-4076(86)90063-1.
   Web of Science ®Google Scholar
• Campbell, S. D. 2007. A review of backtesting and backtesting procedures. The Journal of Risk 9 (2):1–17. doi:10.21314/JOR.2007.146.
   Google Scholar
• Cao, L., and F. Tay. 2001. Financial forecasting using support vector machines. Neural Computing & Applications 10 (2):184–92. doi:10.1007/s005210170010.
   Web of Science ®Google Scholar
• Carrasco, M., and X. Chen. 2002. Mixing and moment properties of various GARCH and stochastic volatility models. Econometric Theory 18 (1):17–39. doi:10.1017/S0266466602181023.
   Web of Science ®Google Scholar
• Chen, J., and A. K. Gupta. 2012. Parametric statistical change point analysis with applications to genetics, medicine, and finance. New York: Wiley.
   Google Scholar
• Chen, S., W. K. Härdle, and K. Jeong. 2010. Forecasting volatility with support vector machine-based GARCH model. Journal of Forecasting 29 (4):406–33. doi:10.1002/for.1134.
   Web of Science ®Google Scholar
• Cherkassky, V., and Y. Ma. 2004. Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 17 (1):113–26. doi:10.1016/S0893-6080(03)00169-2.
   PubMed Web of Science ®Google Scholar
• Christoffersen, P. F. 1998. Evaluating interval forecasts. International Economic Review 39 (4):841–862. doi:10.2307/2527341.
   Web of Science ®Google Scholar
• Csörgő, M., and L. Horváth. 1997. Limit theorems in change-point analysis. New York: John Wiley & Sons Inc.
   Google Scholar
• Davis, R., and W. Dunsmuir. 1997. Least absolute deviation estimation for regression with arma errors. Journal of Theoretical Probability 10 (2):481–497. doi:10.1023/A:1022620818679.
   Web of Science ®Google Scholar
• de Pooter, M., and D. van Dijk. 2004. Testing for changes in volatility in heteroskedastic time series – A further examination. Econometric Institute Research Papers EI 2004-38, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute, September.
   Google Scholar
• Ding, Z., C. W. Granger, and R. F. Engle. 1993. A long memory property of stock market returns and a new model. Journal of Empirical Finance 1 (1):83–106. doi:10.1016/0927-5398(93)90006-D.
   Google Scholar
• Drucker, H., C. Burges, L. Kaufman, A. Smola, and V. Vapnik. 1997. Support vector regression machines. Advances in Neural Information Processing Systems 9 :155–161.
   Google Scholar
• Engle, R. F. 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50 (4):987–1007. doi:10.2307/1912773.
   Web of Science ®Google Scholar
• Engle, R. F. 1990. Stock volatility and the crash of ’87: Discussion. Review of Financial Studies 3 (1):103–6. doi:10.1093/rfs/3.1.103.
   Web of Science ®Google Scholar
• Engle, R., and S. Manganelli. 2004. Caviar: Conditional autoregressive value at risk by regression quantiles. Journal of Business & Economic Statistics 22 (4):367–381. doi:10.1198/073500104000000370.
   Web of Science ®Google Scholar
• Fan, L., Peter, W. Glynn, and M. Pelger. Change-point testing and estimation for risk measures in time series. arXiv preprint arXiv:1809.02303, 2018.
   Google Scholar
• Fernández-Rodrı́guez, F.,. C. González-Martel, and S. Sosvilla-Rivero. 2000. On the profitability of technical trading rules based on artificial neural networks: Evidence from the Madrid stock market. Economics Letters 69 (1):89–94. doi:10.1016/S0165-1765(00)00270-6.
   Web of Science ®Google Scholar
• Fitzenberger, B., R. Koenker, and J. Machado. 2013a. Economic applications of quantile regression. Berlin & Heidelberg, Germany: Springer Science & Business Media.
   Google Scholar
• Fitzenberger, B., R. Koenker, and J. Machado. 2013b. Quantile regression. volume 38, Econometric society monographs. Cambridge: Cambridge University Press.
   Google Scholar
• Glosten, L. R., R. Jagannathan, and D. E. Runkle. 1993. On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance 48 (5):1779–801. doi:10.1111/j.1540-6261.1993.tb05128.x.
   Web of Science ®Google Scholar
• Gombay, E. 2008. Change detection in autoregressive time series. Journal of Multivariate Analysis 99 (3):451–64. doi:10.1016/j.jmva.2007.01.003.
   Web of Science ®Google Scholar
• Hwang, C., and J. Shim. 2005. A simple quantile regression via support vector machine. Advances in Natural Computation 3610 :512–20. doi:10.1007/11539087_66.
   Google Scholar
• Inclán, C., and G. C. Tiao. 1994. Use of cumulative sums of squares for retrospective detection of changes of variance. Journal of the American Statistical Association 89 (427):913–23. doi:10.2307/2290916.
   Web of Science ®Google Scholar
• Khan, M. A. I. 2011. Modelling daily value-at-risk using realized volatility, non-linear support vector machine and arch type models. Journal of Economics and International Finance 3:305–321.
   Google Scholar
• Kim, M., and S. Lee. 2016. Nonlinear expectile regression with application to value-at-risk and expected shortfall estimation. Computational Statistics & Data Analysis 94:1–19. doi:10.1016/j.csda.2015.07.011.
   Web of Science ®Google Scholar
• Koenker, R., and G. Bassett. Jr. 1978. Regression quantiles. Econometrica 46 (1):33–50. doi:10.2307/1913643.
   Web of Science ®Google Scholar
• Koenker, R., and Q. Zhao. 1996. Conditional quantile estimation and inference for arch models. Econometric Theory 12 (5):793–813. doi:10.1017/S0266466600007167.
   Web of Science ®Google Scholar
• Koul, H., and A. Saleh. 1995. Autoregression quantiles and related rank-scores processes. The Annals of Statistics 23 (2):670–689. doi:10.1214/aos/1176324541.
   Web of Science ®Google Scholar
• Kupiec, P. 1995. Techniques for verifying the accuracy of risk measurement models. The Journal of Derivatives 3 (2):73–84. doi:10.3905/jod.1995.407942.
   Google Scholar
• Lee, S., J. Ha, O. Na, and S. Na. 2003. The cusum test for parameter change in time series models. Scandinavian Journal of Statistics 30 (4):781–96. doi:10.1111/1467-9469.00364.
   Web of Science ®Google Scholar
• Lee, S., and C. K. Kim. 2021. Test for conditional quantile change in GARCH models. Journal of the Korean Statistical Society. Advance online publication. doi:10.1007/s42952-021-00149-x.
   PubMed Web of Science ®Google Scholar
• Lee, S., C. Kim, and S. Lee. 2020. Hybrid CUSUM change point test for time series with time-varying volatilities based on support vector regression. Entropy 22 (5):578. doi:10.3390/e22050578.
   PubMed Web of Science ®Google Scholar
• Lee, S., S. Lee, and M. Moon. 2020. Hybrid change point detection for time series via support vector regression and CUSUM method. Applied Soft Computing 89:106101. doi:10.1016/j.asoc.2020.106101.
   Web of Science ®Google Scholar
• Lee, S., and J. Noh. 2010. Value at risk forecasting based on quantile regression for GARCH models. Korean Journal of Applied Statistics 23 (4):669–681. doi:10.5351/KJAS.2010.23.4.669.
   Google Scholar
• Lee, S., and J. Noh. 2013. Quantile regression estimator for garch models. Scandinavian Journal of Statistics 40 (1):2–20. doi:10.1111/j.1467-9469.2011.00759.x.
   Web of Science ®Google Scholar
• Lee, S., Y. Tokutsu, and K. Maekawa. 2004. The cusum test for parameter change in regression models with arch errors. Journal of the Japan Statistical Society 34 (2):173–88. doi:10.14490/jjss.34.173.
   Google Scholar
• Lux, M., W. K. Härdle, and S. Lessmann. 2020. Data driven value-at-risk forecasting using a SVR-GARCH-KDE hybrid. Computational Statistics 35 (3):947–35. doi:10.1007/s00180-019-00934-7.
   Web of Science ®Google Scholar
• Nelson, D. B. 1990. Stationarity and persistence in the GARCH (1, 1) model. Econometric Theory 6 (3):318–34. doi:10.1017/S0266466600005296.
   Web of Science ®Google Scholar
• Noh, J., and S. Lee. 2016. Quantile regression for location-scale time series models with conditional heteroscedasticity. Scandinavian Journal of Statistics 43 (3):700–720. doi:10.1111/sjos.12199.
   Web of Science ®Google Scholar
• Oh, H., and S. Lee. 2019. Modified residual CUSUM test for location-scale time series models with heteroscedasticity. Annals of the Institute of Statistical Mathematics 71 (5):1059–91. doi:10.1007/s10463-018-0679-4.
   Web of Science ®Google Scholar
• Page, E. S. 1955. A test for a change in a parameter occurring at an unknown point. Biometrika 42 (3-4):523–7. doi:10.2307/2333401.
   Web of Science ®Google Scholar
• Pérez-Cruz, F., J. Afonso-Rodriguez, and J. Giner. 2003. Estimating GARCH models using SVM. Quantitative Finance 3 (3):163–72. doi:10.1088/1469-7688/3/3/302.
   Web of Science ®Google Scholar
• Shim, J., C. Hwang, and K. Seok. 2016. Support vector quantile regression with varying coefficients. Computational Statistics 31 (3):1015–30. doi:10.1007/s00180-016-0647-5.
   Web of Science ®Google Scholar
• Shim, J., Y. Kim, J. Lee, and C. Hwang. 2012. Estimating value at risk with semiparametric support vector quantile regression. Computational Statistics 27 (4):685–700. doi:10.1007/s00180-011-0283-z.
   Web of Science ®Google Scholar
• Smola, A., and B. Schölkopf. 2004. A tutorial on support vector regression. Statistics and Computing 14 (3):199–222. doi:10.1023/B:STCO.0000035301.49549.88.
   Web of Science ®Google Scholar
• Su, L., and Z. Xiao. 2008. Testing for parameter stability in quantile regression models. Statistics & Probability Letters 78 (16):2768–2775. doi:10.1016/j.spl.2008.03.018.
   Web of Science ®Google Scholar
• Takeuchi, I., Q. V. Le, T. D. Sears, and A. J. Smola. 2006. Nonparametric quantile estimation. Journal of Machine Learning Research 7:1231–64.
   Web of Science ®Google Scholar
• Weiss, A. 1991. Estimating nonlinear dynamic models using least absolute error estimation. Econometric Theory 7 (1):46–68. doi:10.1017/S0266466600004230.
   Web of Science ®Google Scholar
• Xiao, Z., and R. Koenker. 2009. Conditional quantile estimation for generalized autoregressive conditional heteroscedasticity models. Journal of the American Statistical Association 104 (488):1696–1712. doi:10.1198/jasa.2009.tm09170.
   Web of Science ®Google Scholar
• Zakoïan, J. M. 1994. Threshold heteroskedastic models. Journal of Economic Dynamics and Control 18:931–55.
   Web of Science ®Google Scholar