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2014 | 15 | 1 | 7-17
Article title

BOOSTING UNDER QUANTILE REGRESSION – CAN WE USE IT FOR MARKET RISK EVALUATION?

Content
Title variants
Languages of publication
EN
Abstracts
We consider boosting, i.e. one of popular statistical machine-learning meta-algorithms, as a possible tool for combining individual volatility estimates under a quantile regression (QR) framework. Short empirical exercise is carried out for the S&P500 daily return series in the period of 2004-2009. Our initial findings show that this novel approach is very promising and the in-sample goodness-of-fit of the QR model is very good. However much further research should be conducted as far as the out-of-sample quality of conditional quantile predictions is concerned.
Year
Volume
15
Issue
1
Pages
7-17
Physical description
Dates
published
2014
Contributors
  • Institute of Econometrics, Warsaw School of Economics
References
  • Aiolfi M., Capistran C., Timmermann A. (2010) Forecast Combinations, forthcoming in Forecast Handbook (Oxford) ed. Clements, M. and Hendry, D.
  • Amendola A., Storti G. (2008) A GMM procedure for combining volatility forecasts, Computational Statistics and Data Analysis, 52(6), 3047–3060.
  • Bollerslev T. (1986) Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, 307-327.
  • Bühlmann P. (2006) Boosting for high-dimensional linear models, Annals of Statistics 34, 559–583.
  • Bühlmann P., Hothorn T. (2007) Boosting Algorithms: Regularization, Prediction and Model Fitting (with Discussion), Statistical Science, 22(4), 477–505.
  • Bühlmann P., Yu B. (2003) Boosting with the L2 Loss: Regression and Classification, Journal of the American Statistical Association, 98, 324–339.
  • Chen Y., Zhenyu J., Mercola D., Xiaohui X. (2013) A Gradient Boosting Algorithm for Survival Analysis via Direct Optimization of Concordance Index, Computational and Mathematical Methods in Medicine 2013, 1-8.
  • Chiriac R., Rohlmeier W. (2012) Improving the Value at Risk Forecasts: Theory and Evidence from the Financial Crisis, Journal of Economic Dynamics and Control 36, 1212–1228.
  • Christoffersen P. F. (1998) Evaluating Interval Forecasts, International Economic Review 39, 841–862.
  • Engle R.F., Bollerslev T. (1986) Modelling the persistence of conditional variances, Econometric Reviews, 5(1), 1-50.
  • Engle R.F., Manganelli S. (2004) CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles, Journal of Business & Economic Statistics 22(4), p. 367-381.
  • Engle R.F., Ng V.K. (1993) Measuring and testing the impact of news on volatility, Journal of Finance, 48(5) 1749 - 1778.
  • Fenske N., Kneib T., Hothorn T. (2011) Identifying Risk Factors for Severe Childhood Malnutrition by Boosting Additive Quantile Regression, Journal of the American Statistical Association, 106, 494-510.
  • Freund Y., Schapire R.E. (1997) A decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences 55, p. 119-139.
  • Friedman J., Hastie T., Tibshirani R. (2000) Additive Logistic Regression: A statistical view of boosting, The Annals of Statistics 28, 337-407.
  • Glosten L.R., Jagannathan R., Runkle D.E. (1993) On the relation between the expected value and the volatility of the nominal excess return on stocks, Journal of Finance, 48(5), 1779-1801.
  • Hall S.G., Mitchell J. (2007) Combining density forecasts, International Journal of Forecasting 23, 1-13.
  • Hothorn T., Bühlmann P., Dudoit S., Molinaro A. and Van Der Laan M. (2006) Survival ensembles, Biostatistics 7, 355–373.
  • Hothorn T., Bühlmann P., Kneib T., Schmid M., and Hofner B. (2010) Model-Based Boosting 2.0, Journal of Machine Learning Research, 11, p. 1851–1855.
  • Hothorn T., Bühlmann P., Kneib T., Schmid M., Hofner B., Sobotka F., Scheipl F. (2013) Mboost: Model-Based Boosting, R package version 2.2-3, available at http://CRAN.R-project.org/package=mboost.
  • Jeon J., Taylor J.W. (2013) Using CAViaR Models with Implied Volatility for Value at Risk Estimation, Journal of Forecasting 32, p. 62-74.
  • Jing-Rong D., Yu-Ke C., Yan Z. (2011) Combining Stock Market Volatility Forecasts with analysis of Stock Materials under Regime Switching, Advances in Computer Science, Intelligent System and Environment, Springer, 393-402.
  • Jorion P. (2000) Value at Risk. The new benchmark for managing financial risk, McGraw-Hill.
  • Koenker R. (2005) Quantile Regression. Economic Society Monographs, Cambridge University Press, New York.
  • Kupiec P. H. (1995) Techniques for verifying the accuracy of risk measurement models, The Journal of Derivatives, 3(2), 73-84.
  • Li Y., and Zhu J. (2008) L1-Norm Quantile Regression, Journal of Computational and Graphical Statistics 17, 163–185.
  • Lu W., Li L. (2008) Boosting method for nonlinear transformation models with censored survival data, Biostatistics 9, 658-667.
  • Menardi G., Tedeschi F., Torelli N. (2011) On the Use of Boosting Procedures to Predict the Risk of Default, [in:] Classification and Multivariate Analysis for Complex Data Structures, Springer, Berlin, 211-218.
  • R Development Core Team (2008) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL http://www.R-project.org.
  • Ridgeway G. (2002) Looking for lumps: Boosting and bagging for density estimation, Computational Statistics and Data Analysis 38, 379–392.
  • Schwert G. W. (1990) Stock volatility and the crash of '87, Review of Financial Studies, 3(1), 77-102.
  • Taylor S. J. (1986) Modelling financial time series, Wiley.
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.desklight-d8f762e2-497f-40e7-b1a6-3746b60c67c2
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