Optimal and robust combination of forecasts via constrained optimization and shrinkage
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- Roccazzella, Francesco & Gambetti, Paolo & Vrins, Frédéric, 2022. "Optimal and robust combination of forecasts via constrained optimization and shrinkage," International Journal of Forecasting, Elsevier, vol. 38(1), pages 97-116.
- Roccazzella, Francesco & Gambetti, Paolo & Vrins, Frédéric, 2021. "Optimal and robust combination of forecasts via constrained optimization and shrinkage," LIDAM Reprints LFIN 2021014, Université catholique de Louvain, Louvain Finance (LFIN).
References listed on IDEAS
- Newey, Whitney & West, Kenneth, 2014.
"A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix,"
Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
- Newey, Whitney K & West, Kenneth D, 1987. "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, Econometric Society, vol. 55(3), pages 703-708, May.
- Whitney K. Newey & Kenneth D. West, 1986. "A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix," NBER Technical Working Papers 0055, National Bureau of Economic Research, Inc.
- Ledoit, Olivier & Wolf, Michael, 2004.
"A well-conditioned estimator for large-dimensional covariance matrices,"
Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 365-411, February.
- Ledoit, Olivier & Wolf, Michael, 2000. "A well conditioned estimator for large dimensional covariance matrices," DES - Working Papers. Statistics and Econometrics. WS 10087, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Victor DeMiguel & Lorenzo Garlappi & Francisco J. Nogales & Raman Uppal, 2009. "A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms," Management Science, INFORMS, vol. 55(5), pages 798-812, May.
- Diebold, Francis X. & Shin, Minchul, 2019.
"Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives,"
International Journal of Forecasting, Elsevier, vol. 35(4), pages 1679-1691.
- Francis X. Diebold & Minchul Shin, 2018. "Machine Learning for Regularized Survey Forecast Combination: Partially-Egalitarian Lasso and its Derivatives," NBER Working Papers 24967, National Bureau of Economic Research, Inc.
- Francis X. Diebold & Minchul Shin, 2018. "Machine Learning for Regularized Survey Forecast Combination: Partially Egalitarian Lasso and its Derivatives," PIER Working Paper Archive 18-014, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 17 Aug 2018.
- Claeskens, Gerda & Magnus, Jan R. & Vasnev, Andrey L. & Wang, Wendun, 2016.
"The forecast combination puzzle: A simple theoretical explanation,"
International Journal of Forecasting, Elsevier, vol. 32(3), pages 754-762.
- Gerda Claeskens & Jan Magnus & Andrey Vasnev & Wendun Wang, 2014. "The Forecast Combination Puzzle: A Simple Theoretical Explanation," Tinbergen Institute Discussion Papers 14-127/III, Tinbergen Institute.
- Gerda Claeskens & Jan Magnus & Andrey Vasnev & Wendun Wang, 2016. "The forecast combination puzzle: a simple theoretical explanation," Working Papers of Department of Decision Sciences and Information Management, Leuven 532152, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven.
- Tu, Jun & Zhou, Guofu, 2011. "Markowitz meets Talmud: A combination of sophisticated and naive diversification strategies," Journal of Financial Economics, Elsevier, vol. 99(1), pages 204-215, January.
- Sydney C. Ludvigson & Sai Ma & Serena Ng, 2021.
"Uncertainty and Business Cycles: Exogenous Impulse or Endogenous Response?,"
American Economic Journal: Macroeconomics, American Economic Association, vol. 13(4), pages 369-410, October.
- Sydney C. Ludvigson & Sai Ma & Serena Ng, 2015. "Uncertainty and Business Cycles: Exogenous Impulse or Endogenous Response?," NBER Working Papers 21803, National Bureau of Economic Research, Inc.
- Diebold, Francis X. & Pauly, Peter, 1990.
"The use of prior information in forecast combination,"
International Journal of Forecasting, Elsevier, vol. 6(4), pages 503-508, December.
- Francis X. Diebold & Peter Pauly, 1987. "The use of prior information in forecast combination," Special Studies Papers 218, Board of Governors of the Federal Reserve System (U.S.).
- Michael W. McCracken & Serena Ng, 2016.
"FRED-MD: A Monthly Database for Macroeconomic Research,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
- Michael W. McCracken & Serena Ng, 2015. "FRED-MD: A Monthly Database for Macroeconomic Research," Working Papers 2015-12, Federal Reserve Bank of St. Louis.
- Bunn, Derek W., 1985. "Statistical efficiency in the linear combination of forecasts," International Journal of Forecasting, Elsevier, vol. 1(2), pages 151-163.
- Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
- Behr, Patrick & Guettler, Andre & Miebs, Felix, 2013. "On portfolio optimization: Imposing the right constraints," Journal of Banking & Finance, Elsevier, vol. 37(4), pages 1232-1242.
- Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
- Conflitti, Cristina & De Mol, Christine & Giannone, Domenico, 2015.
"Optimal combination of survey forecasts,"
International Journal of Forecasting, Elsevier, vol. 31(4), pages 1096-1103.
- Cristina Conflitti & Christine De Mol & Domenico Giannone, 2012. "Optimal Combination of Survey Forecasts," Working Papers ECARES ECARES 2012-023, ULB -- Universite Libre de Bruxelles.
- Giannone, Domenico & De Mol, Christine & Conflitti, Cristina, 2012. "Optimal Combination of Survey Forecasts," CEPR Discussion Papers 9096, C.E.P.R. Discussion Papers.
- Harrison, David Jr. & Rubinfeld, Daniel L., 1978. "Hedonic housing prices and the demand for clean air," Journal of Environmental Economics and Management, Elsevier, vol. 5(1), pages 81-102, March.
- Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
- Marco Aiolfi & Carlo Ambrogio Favero, "undated".
"Model Uncertainty, Thick Modelling and the predictability of Stock Returns,"
Working Papers
221, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
- Favero, Carlo A. & Aiolfi, Marco, 2003. "Model Uncertainty, Thick Modelling and the Predictability of Stock Returns," CEPR Discussion Papers 3997, C.E.P.R. Discussion Papers.
- Green, Richard C & Hollifield, Burton, 1992.
"When Will Mean-Variance Efficient Portfolios Be Well Diversified?,"
Journal of Finance, American Finance Association, vol. 47(5), pages 1785-1809, December.
- Green, R.C. & Hollifield, B., 1990. "When Will Mean-Variance Efficient Portfolios Be Well Diversified?," GSIA Working Papers 1990-12, Carnegie Mellon University, Tepper School of Business.
- Amendola, Alessandra & Braione, Manuela & Candila, Vincenzo & Storti, Giuseppe, 2020. "A Model Confidence Set approach to the combination of multivariate volatility forecasts," International Journal of Forecasting, Elsevier, vol. 36(3), pages 873-891.
- Cade Massey & George Wu, 2005. "Detecting Regime Shifts: The Causes of Under- and Overreaction," Management Science, INFORMS, vol. 51(6), pages 932-947, June.
- Ledoit, Olivier & Wolf, Michael, 2003.
"Improved estimation of the covariance matrix of stock returns with an application to portfolio selection,"
Journal of Empirical Finance, Elsevier, vol. 10(5), pages 603-621, December.
- Ledoit, Olivier & Wolf, Michael, 2000. "Improved estimation of the covariance matrix of stock returns with an application to portfolio selection," DES - Working Papers. Statistics and Econometrics. WS 10089, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Olivier Ledoit & Michael Wolf, 2001. "Improved estimation of the covariance matrix of stock returns with an application to portofolio selection," Economics Working Papers 586, Department of Economics and Business, Universitat Pompeu Fabra.
- Peter R. Hansen & Asger Lunde & James M. Nason, 2011.
"The Model Confidence Set,"
Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
- Peter R. Hansen & Asger Lunde & James M. Nason, 2010. "The Model Confidence Set," CREATES Research Papers 2010-76, Department of Economics and Business Economics, Aarhus University.
- Olivier Ledoit & Michael Wolf, 2017. "Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks," The Review of Financial Studies, Society for Financial Studies, vol. 30(12), pages 4349-4388.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020.
"Empirical Asset Pricing via Machine Learning,"
The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," NBER Working Papers 25398, National Bureau of Economic Research, Inc.
- Shihao Gu & Bryan T. Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," Swiss Finance Institute Research Paper Series 18-71, Swiss Finance Institute.
- repec:bla:jfinan:v:58:y:2003:i:4:p:1651-1684 is not listed on IDEAS
- Samuels, Jon D. & Sekkel, Rodrigo M., 2017. "Model Confidence Sets and forecast combination," International Journal of Forecasting, Elsevier, vol. 33(1), pages 48-60.
- Taylor, James W., 2020. "Forecast combinations for value at risk and expected shortfall," International Journal of Forecasting, Elsevier, vol. 36(2), pages 428-441.
- 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.
- Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-263, July.
- Francis X. Diebold & Roberto S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
- Tom Doan, "undated". "DMARIANO: RATS procedure to compute Diebold-Mariano Forecast Comparison Test," Statistical Software Components RTS00055, Boston College Department of Economics.
- Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015.
"Measuring Uncertainty,"
American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
- Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2013. "Measuring Uncertainty," NBER Working Papers 19456, National Bureau of Economic Research, Inc.
- Jeremy Smith & Kenneth F. Wallis, 2009. "A Simple Explanation of the Forecast Combination Puzzle," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 331-355, June.
- G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
- Ravi Jagannathan & Tongshu Ma, 2003.
"Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps,"
Journal of Finance, American Finance Association, vol. 58(4), pages 1651-1683, August.
- Ravi Jagannathan & Tongshu Ma, 2002. "Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps," NBER Working Papers 8922, National Bureau of Economic Research, Inc.
- Ryan T. Ball & Eric Ghysels, 2018. "Automated Earnings Forecasts: Beat Analysts or Combine and Conquer?," Management Science, INFORMS, vol. 64(10), pages 4936-4952, October.
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- Gambetti, Paolo & Roccazzella, Francesco & Vrins, Frédéric, 2020. "Meta-learning approaches for recovery rate prediction," LIDAM Discussion Papers LFIN 2020007, Université catholique de Louvain, Louvain Finance (LFIN).
- Gambetti, Paolo & Roccazzella, Francesco & Vrins, Frédéric, 2022. "Meta-Learning Approaches for Recovery Rate Prediction," LIDAM Reprints LFIN 2022011, Université catholique de Louvain, Louvain Finance (LFIN).
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- Thompson, Ryan & Qian, Yilin & Vasnev, Andrey L., 2024.
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Omega, Elsevier, vol. 126(C).
- Ryan Thompson & Yilin Qian & Andrey L. Vasnev, 2022. "Flexible global forecast combinations," Papers 2207.07318, arXiv.org, revised Mar 2024.
- Radchenko, Peter & Vasnev, Andrey L. & Wang, Wendun, 2023.
"Too similar to combine? On negative weights in forecast combination,"
International Journal of Forecasting, Elsevier, vol. 39(1), pages 18-38.
- Radchenko, Peter & Vasnev, Andrey & Wang, Wendun, 2020. "Too similar to combine? On negative weights in forecast combination," Working Papers BAWP-2020-02, University of Sydney Business School, Discipline of Business Analytics.
- Distaso, Walter & Roccazzella, Francesco & Vrins, Frédéric, 2023. "Business cycle and realized losses in the consumer credit industry," LIDAM Discussion Papers LFIN 2023007, Université catholique de Louvain, Louvain Finance (LFIN).
- Zhentao Shi & Liangjun Su & Tian Xie, 2020. "L2-Relaxation: With Applications to Forecast Combination and Portfolio Analysis," Papers 2010.09477, arXiv.org, revised Aug 2022.
- Roccazzella, Francesco & Gambetti, Paolo & Vrins, Frédéric, 2022. "Correction to: Optimal and robust combination of forecasts via constrained optimization and shrinkage," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1050-1050.
- Qian, Yilin & Thompson, Ryan & Vasnev, Andrey L, 2022. "Global combinations of expert forecasts," Working Papers BAWP-2022-02, University of Sydney Business School, Discipline of Business Analytics.
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More about this item
Keywords
forecasts combination ; robust methods ; optimal combination ; machine learning;All these keywords.
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2020-10-12 (Big Data)
- NEP-ECM-2020-10-12 (Econometrics)
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