Search Results - subject:"Covariance matrix"

Sample Covariance Shrinkage for High Dimensional Dependent Data

Sancetta, A. - Faculty of Economics, University of Cambridge - 2006

For high dimensional data sets the sample covariance matrix is usually unbiased but noisy if the sample is not large …

Persistent link: https://www.econbiz.de/10005650534

Looking far in the past:Revisiting the growth-returns nexus with non-parametric tests

Panopoulou, Ekaterini; Pittis, N.; Kalyvitis, S. - Department of Economics, National University of Ireland - 2006

In this paper we reexamine the linkages between output growth and real stock price changes for the G7 countries using a battery of non-parametric procedures to account for the impact of long-lagged observations. We find that correlation between growth and returns is detected at larger horizons...

Persistent link: https://www.econbiz.de/10005656657

Resampling vs. Shrinkage for Benchmarked Managers

Wolf, Michael - Institut für Volkswirtschaftslehre, … - 2006

A well-known pitfall of Markowitz (1952) portfolio optimization is that the sample covariance matrix, which is a … sample covariance matrix by pulling its most extreme elements towards more moderate values. An alternative solution is the …

Persistent link: https://www.econbiz.de/10005627983

Nonparametric estimation of time-varying covariance matrix in a slowly changing vector random walk model

Feng, Yuanhua; Yu, Keming - Volkswirtschaftliche Fakultät, … - 2006

. Nonparametric estimation of local covariance matrix is proposed. The asymptotic distributions, including asymptotic biases …

Persistent link: https://www.econbiz.de/10005835868

Looking far in the past: Revisiting the growth-returns nexus with non-parametric tests

Panopoulou, Ekaterini; Pittis, Nikitas; Kalyvitis, Sarantis - Institute for International Integration Studies (IIIS), … - 2006

Persistent link: https://www.econbiz.de/10005187437

A New Test for Superior Predictive Ability

Cai, Zongwu; Jiang, Jiancheng; Zhang, Jingshuang - 2013

covariance matrix through certain decomposition, which decreases the number of elements needed to be estimated. With the help of … covariance matrix, we are able to exploit more information contained in the diagonal and off-diagonal terms and as a result, so …

Persistent link: https://www.econbiz.de/10010892160

Asymptotic theory for maximum deviations of sample covariance matrix estimates

Xiao, Han; Wu, Wei Biao - In: Stochastic Processes and their Applications 123 (2013) 7, pp. 2899-2920

We consider asymptotic distributions of maximum deviations of sample covariance matrices, a fundamental problem in high-dimensional inference of covariances. Under mild dependence conditions on the entries of the data matrices, we establish the Gumbel convergence of the maximum deviations. Our...

Persistent link: https://www.econbiz.de/10010875059

An exact test for a column of the covariance matrix based on a single observation

Bodnar, Taras; Gupta, Arjun - In: Metrika 76 (2013) 6, pp. 847-855

In this paper, we derive an exact test for a column of the covariance matrix. The test statistic is calculated by using … covariance matrix to a given vector. It is shown that the information contained in a single vector is large enough to ensure a …

Persistent link: https://www.econbiz.de/10010846100

Improvement in finite-sample properties of GMM-based Wald tests

Chen, Qihui; Ren, Yu - In: Computational Statistics 28 (2013) 2, pp. 735-749

GMM-based Wald tests tend to overreject when used for small samples, mainly due to inaccurate estimation of the weighting matrix. This article proposes applying the shrinkage method to address this problem. Using a possibly-misspecified factor model, the shrinkage method can provide a good...

Persistent link: https://www.econbiz.de/10010847469

Robust estimation in joint mean–covariance regression model for longitudinal data

Zheng, Xueying; Fung, Wing; Zhu, Zhongyi - In: Annals of the Institute of Statistical Mathematics 65 (2013) 4, pp. 617-638

reliable estimation in the covariance matrix. Copyright The Institute of Statistical Mathematics, Tokyo 2013 …

Persistent link: https://www.econbiz.de/10010848628