High Moment Constraints for Predictive Density Combination

Laurent Pauwels, Peter Radchenko and Andrey Vasnev

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

Abstract: Financial data typically exhibit asymmetry and heavy tails, which makes forecasting the entire density of the returns critically important. We investigate the effects of aggregating, or combining, predictive densities and find that even if the individual densities are skewed and/or heavy-tailed, the combined density often has significantly reduced skewness and kurtosis. This phenomenon has important implications for measuring downside risk in financial assets. When forecasting financial risk, recently proposed combination methods have focused on specific regions of the density support. We propose an alternative approach, which modifies the popular Log-Score weighting scheme by introducing data-driven constraints on the combination weights that control the skewness and kurtosis of the resulting predictive density. An empirical application using S&P 500 daily index returns demonstrates that the corresponding skewness and kurtosis successfully track the respective sample characteristics of the returns over time. Moreover, the proposed approach outperforms its natural competitors at forecasting the 1% Value-at-Risk for a broad range of estimation-window sizes.

Keywords: Forecasting; Forecast combinations; Predictive densities; Moment constraints; Financial data (search for similar items in EconPapers)
JEL-codes: C53 C58 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2020-05, Revised 2023-06
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ore
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Citations: View citations in EconPapers (5)

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https://cama.crawford.anu.edu.au/sites/default/fil ... ev_orginal_may20.pdf Original version (application/pdf)
https://cama.crawford.anu.edu.au/sites/default/fil ... radchenko_vasnev.pdf Revised Version (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2020-45

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