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Portfolio risk management in a data-rich environment

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Abstract

We study risk assessment using an optimal portfolio in which the weights are functions of latent factors and firm-specific characteristics (hereafter, diffusion index portfolio). The factors are used to summarize the information contained in a large set of economic data and thus reflect the state of the economy. First, we evaluate the performance of the diffusion index portfolio and compare it to both that of a portfolio in which the weights depend only on firm-specific characteristics and an equally weighted portfolio. We then use value-at-risk, expected shortfall, and downside probability to investigate whether the weights-modeling approach, which is based on factor analysis, helps reduce market risk. Our empirical results clearly indicate that using economic factors together with firm-specific characteristics helps protect investors against market risk.

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Acknowledgments

We thank an anonymous referee and the Co-Editor Markus Schmid for their very helpful comments. We also thank Kenneth French and Mark Watson for making the data available to us. The first author acknowledges financial support from IFM2, Montreal, and the second author acknowledges financial support from the Spanish Ministry of Education through Grants SEJ 2011-0031-001.

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Authors and Affiliations

  1. Economics Department, American University in Cairo, AUC Avenue, P.O. Box 74, New Cairo, 11835, Egypt

    Mohammed Bouaddi

  2. Departamento de Economía, Universidad Carlos III de Madrid, Calle Madrid 126, 28903 , Getafe (Madrid), Spain

    Abderrahim Taamouti

Authors
  1. Mohammed Bouaddi
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  2. Abderrahim Taamouti
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Corresponding author

Correspondence to Abderrahim Taamouti.

Appendix: Additional estimation results

Appendix: Additional estimation results

See Figs. 1, 2, , 4, 5, 6, 7, 8, 9 and Tables  5, 6, 7, 8, 9, 10, 11.

Table 5 Estimation results for portfolio policy function (weight)
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Table 6 Estimation results for portfolio policy function
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Table 7 Estimation results for portfolio policy function
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Table 8 Estimation results for portfolio policy function
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Table 9 This table shows the descriptive statistics that correspond to an out-of-sample prediction of DFI, IC, and EW portfolio returns
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Table 10 This table reports the sharpe ratio (SR), (Sharpe 1966), and the FT ratios, (Farinelli and Tibiletti 2008), that correspond to an out-of-sample prediction of DFI, IC, and EW portfolio returns
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Table 11 This table shows the average of portfolio weights in low and high portfolio return statistics (mean, standard-deviation, skewness) that correspond to an out-of-sample prediction of DFI, IC, and EW portfolio returns
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Bouaddi, M., Taamouti, A. Portfolio risk management in a data-rich environment. Financ Mark Portf Manag 26, 469–494 (2012). https://doi.org/10.1007/s11408-012-0199-9

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