Estimating a covariance matrix for market risk management and the case of credit default swaps

References

• Alexander, C., Principal component models for generating large GARCH covariance matrices. Econom. Notes, 2002, 31, 337–359. doi: 10.1111/1468-0300.00089
   Google Scholar
• Alexander, C. and Kaeck, A., Regime dependent determinants of credit default swap spreads. J. Bank. Financ., 2008, 32, 1008–1021. doi: 10.1016/j.jbankfin.2007.08.002
   Web of Science ®Google Scholar
• Bai, J. and Liao, Y., Efficient estimation of approximate factor models via regularized maximum likelihood. Working Paper, 2012. arXiv 1209.5911.
   Google Scholar
• Bai, J. and Shi, S., Estimating high dimensional covariance matrices and its applications. Ann. Econ. Financ., 2011, 12, 199–215.
   Web of Science ®Google Scholar
• Bollerslev, T., Modelling the coherence in short-run nominal exchange rates: A multivariate generalized ARCH model. Rev. Econ. Stat., 1990, 72, 498–505. doi: 10.2307/2109358
   Web of Science ®Google Scholar
• Boudoukh, J., Richardson, M. and Whitelaw, R.F., Investigation of a class of volatility estimators. J. Derivatives, 1997, 4, 63–71. doi: 10.3905/jod.1997.407973
   Google Scholar
• Chamberlain, G. and Rothschild, M., Arbitrage, factor structure, and mean–variance analysis on large asset markets. Econometrica, 1983, 51, 1281–304. doi: 10.2307/1912275
   Web of Science ®Google Scholar
• Chandrasekaran, V., Parrilo, P.A. and Willsky, A.S., Latent variable graphical model selection via convex optimization. Ann. Stat., 2012, 40, 1935–1967. doi: 10.1214/11-AOS949
   Web of Science ®Google Scholar
• Efron, B., Bootstrap methods: Another look at the Jackknife. Ann. Stat., 1979, 7, 1–26. doi: 10.1214/aos/1176344552
   Web of Science ®Google Scholar
• Engle, R., Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. J. Bus. Econ. Stat., 2002, 20, 339–350. doi: 10.1198/073500102288618487
   Web of Science ®Google Scholar
• Engle, R. and Bollerslev, T., Modelling the persistence of conditional variances. Econom. Rev., 1986, 5, 1–50. doi: 10.1080/07474938608800095
   Google Scholar
• Engle, R. and Sheppard, K., Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH. Research Note, National Bureau of Economic Research, 2001.
   Google Scholar
• Ericsson, J., Jacobs, K. and Oviedo, R., The determinants of credit default swap premia. J. Financ. Quant. Anal., 2009, 44, 109–132. doi: 10.1017/S0022109009090061
   Web of Science ®Google Scholar
• Fang, Y., Wang, B. and Feng, Y., Tuning-parameter selection in regularized estimations of large covariance matrices. J. Stat. Comput. Simul., 2016, 86, 494–509. doi: 10.1080/00949655.2015.1017823
   Web of Science ®Google Scholar
• Friedman, J., Hastie, T. and Tibshirani, R., Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 2008, 9, 432–441. doi: 10.1093/biostatistics/kxm045
   PubMed Web of Science ®Google Scholar
• Halbleib, R. and Voev, V., Forecasting covariance matrices: A mixed frequency approach. Working Paper, ECARES, 2011.
   Google Scholar
• Hastie, T., Tibshirani, R. and Friedman, J., The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2009 (Springer: New York).
   Google Scholar
• Higham, N.J., Computing the nearest correlation matrix – A problem from finance. IMA J. Numer. Anal., 2002, 22, 329–343. doi: 10.1093/imanum/22.3.329
   Web of Science ®Google Scholar
• Hurvich, C.M. and Tsai, C.L., Regression and time series model selection in small samples. Biometrika, 1989, 76, 297–307. doi: 10.1093/biomet/76.2.297
   Web of Science ®Google Scholar
• James, W. and Stein, C., Estimation with quadratic loss. Proceedings of the 4th Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, pp. 361–379, 1961.
   Google Scholar
• Ledoit, O., Santa-Clara, P. and Wolf, M., Flexible multivariate GARCH modeling with an application to international stock markets. Rev. Econ. Stat., 2003, 85, 735–747. doi: 10.1162/003465303322369858
   Web of Science ®Google Scholar
• Ledoit, O. and Wolf, M., Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. J. Empirical Finance, 2003, 10, 603–621. doi: 10.1016/S0927-5398(03)00007-0
   Google Scholar
• Ledoit, O. and Wolf, M., Honey, I shrunk the sample covariance matrix. J. Portfolio Manage., 2004, 30, 110–119. doi: 10.3905/jpm.2004.110
   Web of Science ®Google Scholar
• Ledoit, O. and Wolf, M., Nonlinear shrinkage estimation of large-dimensional covariance matrices. Ann. Stat., 2012, 40, 1024–1060. doi: 10.1214/12-AOS989
   Web of Science ®Google Scholar
• Markowitz, H., Portfolio selection. J. Finance, 1952, 7, 77–91.
   Web of Science ®Google Scholar
• Stein, C., Estimation of a covariance matrix. Proceedings of the Rietz Lectures, 1975.
   Google Scholar
• Tsay, R., Analysis of Financial Time Series, 2005 (Wiley: New York).
   Google Scholar
• Tumminello, M., Lillo, F. and Mantegna, R.N., Hierarchically nested factor model from multivariate data. EPL, 2007, 78. doi: 10.1209/0295-5075/78/30006
   Web of Science ®Google Scholar
• Tumminello, M., Lillo, F. and Mantegna, R.N., Correlation, hierarchies, and networks in financial markets. J. Econ. Behav. Organ., 2010, 75, 40–58. doi: 10.1016/j.jebo.2010.01.004
   Web of Science ®Google Scholar
• Yuan, M. and Lin, Y., Model selection and estimation in the Gaussian graphical model. Biometrika, 2007, 94, 19–35. doi: 10.1093/biomet/asm018
   Web of Science ®Google Scholar
• Zumbach, G., The Riskmetrics 2006 methodology. Technical Report, SSRN 1420185, 2007.
   Google Scholar