# Portfolio risk management in a data-rich environment

- 527 Downloads
- 1 Citations

## 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.

## Keywords

Portfolio weights modeling Factor analysis Principal components Portfolio performance Value-at-risk Expected shortfall Downside probability## JEL Classification

C13 C43 G11 G19## Notes

### 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.

## References

- Amemiya, Y.: Instrumental variable estimation for nonlinear factor analysis. In: Cuadras, C.M., Rao, C.R. (eds.) Multivariate Analysis: Future Directions, vol. 2, pp. 113–129 Amesterdam, Elsevier (1993a)Google Scholar
- Amemiya, Y.: On nonlinear factor analysis. Proc Soc Stat Sect Annu Meet Am Stat Assoc
**92–96**, 290–294 (1993b)Google Scholar - Amemiya, Y., Yalcin, I.: Nonlinear factor analysis as a statistical method. Stat Sci
**16**, 275–294 (2001)CrossRefGoogle Scholar - Bai, J., Ng, S.: Determining the number of factors in approximate factor models. Econometrica
**70**, 191–221 (2002)CrossRefGoogle Scholar - Bai, J., Ng, S.: Confidence intervals for diffusion index forecasts and inference for factor-augmented regressions. Econometrica
**74**, 1133–1150 (2006)CrossRefGoogle Scholar - Benzoni, L., Collin-Dufresne, P., Goldstein, R.S.: Portfolio choice over the life-cycle when the stock and labor markets are cointegrated. J Finance
**62**, 2123–2167 (2007)CrossRefGoogle Scholar - Bernanke, B., Kuttner, K.: What explains the stock market’s reaction to Federal Reserve policy. J Finance
**60**, 1221–1257 (2005)CrossRefGoogle Scholar - Bouaddi, M., Taamouti, A.: Portfolio selection in a data-rich environment. Universidad Carlos III de Madrid Working Paper (2011)Google Scholar
- Brandt, M.W., Santa-Clara, P.: Dynamic portfolio selection by augmenting the asset space. J Finance
**61**, 2187–2217 (2006)CrossRefGoogle Scholar - Brandt, M.W., Santa-Clara, P., Valkanov, R.: Parametric portfolio policies: exploiting characteristics in the cross section of equity returns. Rev Financial Stud
**22**, 3411–3447 (2009)CrossRefGoogle Scholar - Briec, W., Kerstens, K., Jokung, O.: Mean-variance-skewness portfolio performance gauging: a general shortage function and dual approach. Manag Sci
**53**, 135–149 (2007)CrossRefGoogle Scholar - Brodie, J., Daubechies, I., De Mol, C., Giannone, D., Loris, I.: Sparse and stable Markowitz portfolios. PNAS
**106**, 12267–12272 (2009)CrossRefGoogle Scholar - Cascon, A., Keating, C., Shadwick, W.: The Omega function. Finance Development Centre, London (2003)Google Scholar
- Cochrane, J.H., Piazzesi, M.: Bond risk premia. Am Econ Rev
**95**, 138–160 (2005)CrossRefGoogle Scholar - Farinelli, S., Tibiletti, L.: Sharpe thinking in asset ranking with one-sided measures. Eur J Oper Res
**185**, 1542–1547 (2008)CrossRefGoogle Scholar - Flannery, M.J., Protopapadakis, A.A.: Macroeconomic factors do influence aggregate stock returns. Rev Financial Stud
**15**, 751–782 (2002)CrossRefGoogle Scholar - Gregoriou, A., Kontonikas, A., MacDonald, R., Montagnoli, A.: Monetary policy shocks and stock returns: evidence from the British market. Financial Markets Portfolio Manag
**23**, 401–410 (2009)CrossRefGoogle Scholar - He, J., Ng, L.: Economic forces, fundamental variables, and equity returns. J Bus
**67**, 599–609 (1994)CrossRefGoogle Scholar - Hess, M.: Sector specific impacts of macroeconomic fundamentals on the Swiss stock market. Financial Markets Portfolio Manag
**17**, 234–245 (2003)CrossRefGoogle Scholar - Hildebrand, P.M.: Monetary policy and financial markets. Financial Markets Portfolio Manag
**20**, 7–18 (2006)CrossRefGoogle Scholar - Katzur, T., Spierdijk, L.: Stock returns and inflation risk: implications for portfolio selection. University of Groningen Working Paper (2010)Google Scholar
- Konrad, E.: The impact of monetary policy surprises on asset return volatility: the case of Germany. Financial Markets Portfolio Manag
**23**, 111–135 (2009)CrossRefGoogle Scholar - Ludvigson, S.C., Ng, S.: Macro factors in bond risk premia. Rev Financial Stud
**22**, 5027–5067 (2009)CrossRefGoogle Scholar - Markowitz, H.: Portfolio selection. J Finance
**7**, 77–91 (1952)Google Scholar - Rangvid, J.: Output and expected returns. J Financial Econ
**81**, 595–624 (2006)CrossRefGoogle Scholar - Rigobon, R., Sack, B.: Measuring the reaction of monetary policy to the stock market. Q J Econ
**118**, 639–670 (2003)CrossRefGoogle Scholar - Sharpe, W.F.: Mutual fund performance. J Bus
**39**, 119–138 (1966)CrossRefGoogle Scholar - Sortino, F., van der Meer, R., Plantinga, A.: The Dutch triangle. J Portfolio Manag
**26**, 50–57 (1999)CrossRefGoogle Scholar - Specht, K., Gohout, W.: Portfolio selection using the principal components GARCH model. Financial Markets Portfolio Manag
**17**, 450–458 (2003)CrossRefGoogle Scholar - Stock, J.H., Watson, M.W.: Macroeconomic forecasting using diffusion indexes. J Bus Econ Stat
**20**, 147–162 (2002a)CrossRefGoogle Scholar - Stock, J.H., Watson, M.W.: Forecasting using principal components from a large number of predictors. J Am Stat Assoc
**97**, 1167–1179 (2002b)CrossRefGoogle Scholar