| Bell, D.R., Ledoit, O., and Wolf, M. (2024). A novel estimator of Earth's curvature (Allowing for inference as well). Annals of Applied Statistics, 18:585-599. (PDF, 581 KB) |
| Hediger, S., Näf, J., and Wolf, M. (2023). R-NL: Covariance matrix estimation for elliptical distributions based on nonlinear shrinkage. IEEE Transactions on Signal Processing, 71:1657-1668. (PDF, 942 KB) |
| Beck, E., De Nard, G., and Wolf, M. (2023). Improved inference in financial factor models. International Review of Economics and Finance, 86:364-379. (PDF, 1 MB) |
| Ledoit, O. and Wolf, M. (2022). Quadratic shrinkage for large covariance matrices. Bernoulli, 28:1519-1547. (PDF, 675 KB) |
| De Nard, G., Engle, R.F., Ledoit, O., and Wolf, M. (2022). Large dynamic covariance matrices: Enhancements based on intraday data. Journal of Banking and Finance, 138:106426. (PDF, 2 MB) |
| Ledoit, O. and Wolf, M. (2022). The power of (non-)linear shrinking: A review and guide to covariance matrix estimation. Journal of Financial Econometrics, 20:187-218. (PDF, 416 KB) |
| Ledoit, O. and Wolf, M. (2021). Shrinkage estimation of large covariance matrices: Keep it simple, statistician? Journal of Multivariate Analysis, 186:104796. (PDF, 921 KB) |
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De Nard, G., Ledoit, O., and Wolf, M. (2021). Factor models for portfolio selection in large dimensions: The good, the better and the ugly. Journal of Financial Econometrics, 19:236-257. (PDF, 249 KB)
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| Clarke, D., Romano, J.P., and Wolf, M. (2020). The Romano-Wolf multiple-hypothesis correction in Stata. The Stata Journal, 20:812-843. (PDF, 696 KB) |
| Ledoit, O. and Wolf, M. (2020). Analytical nonlinear shrinkage of large-dimensional covariance matrices. Annals of Statistics, 48:3043-3065. (PDF, 699 KB) |
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Ledoit, O., Wolf, M., and Zhao Z. (2019). Efficient sorting: A more powerful test for cross-sectional anomalies. Journal of Financial Econometrics, 17:645-686. (PDF, 515 KB)
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| Engle, R.F., Ledoit, O., and Wolf. M. (2019). Large dynamic covariance matrices. Journal of Business & Economic Statistics, 37:363-375. (PDF, 599 KB) |
| DiCiccio, C.J., Romano, J.P., and Wolf, M. (2019). Improving weighted least squares inference. Econometrics and Statistics, 10:96-119. (PDF, 825 KB) |
| Bruder, S. and Wolf, M. (2018). Balanced bootstrap joint confidence bands for structural impulse response functions. Journal of Time Series Analysis, 39:641-664. (PDF, 311 KB) |
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Ledoit, O. and Wolf, M. (2018). Optimal estimation of a large-dimensional covariance matrix under Stein's loss. Bernoulli, 24:3791-3832 (PDF, 759 KB). Supplementary Material (PDF, 270 KB)
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Ledoit, O. and Wolf, M. (2017). Nonlinear shrinkage of the covariance matrix for portfolio selection: Markowitz meets Goldilocks. Review of Financial Studies, 30:4349-4388. (PDF, 448 KB) Supplementary Material (PDF, 330 KB)
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| Ledoit, O. and Wolf. M. (2017). Numerical implementation of the QuEST function. Computational Statistics & Data Analysis, 115:199-223. (PDF, 1 MB) |
| Romano, J.P. and Wolf, M. (2017). Resurrecting weighted least squares. Journal of Econometrics, 197:1-19. (PDF, 674 KB) |
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Romano, J.P. and Wolf, M. (2016). Efficient computation of adjusted p-values for resampling-based stepdown multiple testing. Statistics & Probability Letters, 113:38-40. (PDF, 348 KB)
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| Ledoit, O. and Wolf, M. (2015). Spectrum estimation: a unified framework for covariance matrix estimation and PCA in large dimensions. Journal of Multivariate Analysis, 139:360-384. (PDF, 885 KB) |
| Wolf, M. and Wunderli, D. (2015). Bootstrap joint prediction regions. Journal of Time Series Analysis, 36:352-376. (PDF, 390 KB) |
| Bell, D.R., Ledoit, O., and Wolf, M. (2014). A new portfolio formation approach to mispricing of marketing performance indicators: an application to customer satisfaction. Customer Needs and Solutions, 1:263-276. (PDF, 344 KB) |
| Romano, J.P., Shaikh, A.M., and Wolf, M. (2014). A practical two-step method for testing moment inequalities. Econometrica, 82:1979-2002. (PDF, 271 KB)Supplement (PDF, 99 KB) |
| Romano, J.P. and Wolf, M. (2013). Testing for monotonicity in expected asset returns. Journal of Empirical Finance, 23:93-116. (PDF, 809 KB) |
| Ledoit, O. and Wolf, M. (2012). Nonlinear shrinkage estimation of large-dimensional covariance matrices. Annals of Statistics, 40:1024-1060. (PDF, 575 KB)Supplement. (PDF, 209 KB) |
| Ledoit, O. and Wolf, M. (2011). Robust performance hypothesis testing with the variance. Wilmott Magazine, September, 86-89. (PDF, 511 KB) |
| Romano, J.P., Shaikh, A.M., and Wolf, M. (2011). Consonance and the closure method in multiple testing. International Journal of Biostatistics, 7, Issue 1, Article 12. (PDF, 1016 KB) |
| Romano, J.P., Shaikh, A.M., and Wolf, M. (2010). Hypothesis testing in econometrics. Annual Review of Economics, 2:75-104. (PDF, 697 KB) |
| Romano, J.P. and Wolf, M. (2010). Balanced control of generalized error rates. Annals of Statistics, 38:598-633. (PDF, 307 KB) |
| Bittman, R.M., Romano, J.P., Vallarino, C., and Wolf, M. (2009). Optimal testing of multiple hypotheses with common effect direction. Biometrika, 96:399-410. (PDF, 159 KB) |
| Romano, J.P., Shaikh, A.M., and Wolf, M. (2008). Control of the false discovery rate under dependence using the bootstrap and subsampling. (Invited Paper with discussion), TEST 17, 417-442. (PDF, 871 KB) |
| Ledoit, O. and Wolf, M. (2008). Robust performance hypothesis testing with the Sharpe ratio. Journal of Empirical Finance, 15:850-859. (PDF, 301 KB) |
| Romano, J.P., Shaikh, A.M., and Wolf, M. (2008). Formalized data snooping based on generalized error rates. Econometric Theory, 24:404-447. (PDF, 358 KB) |
| Afshartous, D. and Wolf, M. (2007). Avoiding data snooping in multilevel and mixed effects models. Journal of the Royal Statistical Society, Series A, 170:1035-1059. (PDF, 648 KB) |
| Romano, J.P. and Wolf, M. (2007). Control of generalized error rates in multiple testing. Annals of Statistics, 35:1378-1408. (PDF, 304 KB) |
| Wolf, M. (2007). Resampling vs. shrinkage for benchmarked managers. Wilmott Magazine, January, 76-81. (PDF, 99 KB) |
| Romano, J.P. and Wolf, M. (2006). Improved nonparametric confidence intervals in time series regressions. Journal of Nonparametric Statistics, 18:199-214. (PDF, 192 KB) |
| Romano, J.P. and Wolf, M. (2005). Stepwise multiple testing as formalized data snooping. Econometrica, 73:1237-1282. (PDF, 308 KB) |
| Gonzalo, J. and Wolf, M. (2005). Subsampling inference in threshold autoregressive models. Journal of Econometrics, 127:201-224. (PDF, 391 KB) |
| Romano, J.P. and Wolf, M. (2005). Exact and approximate stepdown methods for multiple hypothesis testing. Journal of the American Statistical Association, 100:94-108. (PDF, 354 KB) |
| Ledoit, O. and Wolf, M. (2004). Honey, I shrunk the sample covariance matrix. Journal of Portfolio Management, 30:110-119. (PDF, 162 KB) |
| Politis, D.N., Romano, J.P., and Wolf M. (2004). Inference for autocorrelations in the possible presence of a unit root. Journal of Time Series Analysis, 25:251-263. (PDF, 141 KB) |
| Kokoszka, P. and Wolf, M. (2004). Subsampling the mean of heavy-tailed dependent observations. Journal of Time Series Analysis, 25:217-234. (PDF, 178 KB) |
| Ledoit, O. and Wolf, M. (2004). A well-conditioned estimator for large-dimensional covariance matrices. Journal of Multivariate Analysis, 88:365-411. (PDF, 494 KB) |
| Ledoit, O. and Wolf, M. (2003). Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. Journal of Empirical Finance, 10:603-621. (PDF, 193 KB) |
| Ledoit, O., Santa-Clara, P., and Wolf, M. (2003). Flexible multivariate GARCH modeling with an application to international stock markets. Review of Economics and Statistics, 85:735-747. (PDF, 225 KB) |
| Ledoit, O. and Wolf, M. (2002). Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size. Annals of Statistics, 30:1081-1102. (PDF, 190 KB) |
| Romano, J.P. and Wolf, M. (2002). Explicit nonparametric confidence intervals for the variance with guaranteed coverage. Communications in Statististics - Theory and Methods, 31:1231-125 (PDF, 123 KB) |
| Politis, D.N., Romano, J.P., and Wolf, M. (2001). On the asymptotic theory of subsampling. Statistica Sinica, 11:1105-1124. (PDF, 220 KB) |
| Delgado, M., Rodriguez-Poo, J., and Wolf, M. (2001). Subsampling inference in cube root asymptotics with an application to Manski'smaximum score estimator. Economics Letters, 73:241-250. (PDF, 72 KB) |
| Romano, J.P. and Wolf, M. (2001). Subsampling intervals in autoregressive models with linear time trend. Econometrica, 69:1283-1314. (PDF, 383 KB) |
| Politis, D.N., Romano, J.P., and Wolf, M. (2000). Subsampling, symmetrization, and robust interpolation. Communications in Statististics - Theory and Methods, 29:1741-1758. (PDF, 129 KB) |
| Romano, J.P. and Wolf, M. (2000). Finite sample nonparametric inference and large sample efficiency. Annals of Statistics, 28:756-778. (PDF, 185 KB) |
| Romano, J.P. and Wolf, M. (2000). A more general Central Limit Theorem for 'm'-dependent random variables with unbounded m. Statistics and Probability Letters, 47:115-124. (PDF, 108 KB) |
| Wolf, M. (2000). Stock returns and dividend yields revisited: A new way to look at an old problem. Journal of Business and Economic Statistics, 18:18-30. (PDF, 540 KB) |
| Romano, J.P. and Wolf, M. (1999). Inference for the mean in the heavy-tailed case. Metrika, 50:55-69. (PDF, 141 KB) |
| Politis, D.N., Romano, J.P., and Wolf, M. (1999). Weak convergence of dependent empirical measures with application to subsampling and confidence bands. Journal of Statistical Planning and Inference, 79:179-191. (PDF, 100 KB) |
| Politis, D.N., Romano, J.P., and Wolf, M. (1997). Subsampling for heteroskedastic time series. Journal of Econometrics, 81:281-317. (PDF, 2 MB) |
