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A coskewnesss shrinkage approach for estimating the skewness of linear combinations of random variables

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Abstract
Decision-making in finance often requires an accurate estimate of the coskewness matrix to optimize the allocation to random variables with asymmetric distributions. The classical sample estimator of the coskewness matrix performs poorly for small sample sizes. A solution is to use shrinkage estimators, defined as the convex combination between the sample coskewness matrix and a target matrix. We propose unbiased consistent estimators for the MSE loss function and include the possibility of having multiple target matrices. In a portfolio application, we find that the proposed shrinkage coskewness estimators are useful in mean–variance–skewness efficient portfolio allocation of funds of hedge funds.
Keywords
Economics and Econometrics, Finance

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MLA
Boudt, Kris, et al. “A Coskewnesss Shrinkage Approach for Estimating the Skewness of Linear Combinations of Random Variables.” JOURNAL OF FINANCIAL ECONOMETRICS, vol. 18, no. 1, 2020, pp. 1–23.
APA
Boudt, K., Cornilly, D., & Verdonck, T. (2020). A coskewnesss shrinkage approach for estimating the skewness of linear combinations of random variables. JOURNAL OF FINANCIAL ECONOMETRICS, 18(1), 1–23.
Chicago author-date
Boudt, Kris, Dries Cornilly, and Tim Verdonck. 2020. “A Coskewnesss Shrinkage Approach for Estimating the Skewness of Linear Combinations of Random Variables.” JOURNAL OF FINANCIAL ECONOMETRICS 18 (1): 1–23.
Chicago author-date (all authors)
Boudt, Kris, Dries Cornilly, and Tim Verdonck. 2020. “A Coskewnesss Shrinkage Approach for Estimating the Skewness of Linear Combinations of Random Variables.” JOURNAL OF FINANCIAL ECONOMETRICS 18 (1): 1–23.
Vancouver
1.
Boudt K, Cornilly D, Verdonck T. A coskewnesss shrinkage approach for estimating the skewness of linear combinations of random variables. JOURNAL OF FINANCIAL ECONOMETRICS. 2020;18(1):1–23.
IEEE
[1]
K. Boudt, D. Cornilly, and T. Verdonck, “A coskewnesss shrinkage approach for estimating the skewness of linear combinations of random variables,” JOURNAL OF FINANCIAL ECONOMETRICS, vol. 18, no. 1, pp. 1–23, 2020.
@article{8601076,
  abstract     = {Decision-making in finance often requires an accurate estimate of the coskewness matrix to optimize the allocation to random variables with asymmetric distributions. The classical sample estimator of the coskewness matrix performs poorly for small sample sizes. A solution is to use shrinkage estimators, defined as the convex combination between the sample coskewness matrix and a target matrix. We propose unbiased consistent estimators for the MSE loss function and include the possibility of having multiple target matrices. In a portfolio application, we find that the proposed shrinkage coskewness estimators are useful in mean–variance–skewness efficient portfolio allocation of funds of hedge funds.},
  author       = {Boudt, Kris and Cornilly, Dries and Verdonck, Tim},
  issn         = {1479-8409},
  journal      = {JOURNAL OF FINANCIAL ECONOMETRICS},
  keywords     = {Economics and Econometrics,Finance},
  language     = {eng},
  number       = {1},
  pages        = {1--23},
  title        = {A coskewnesss shrinkage approach for estimating the skewness of linear combinations of random variables},
  url          = {http://dx.doi.org/10.1093/jjfinec/nby022},
  volume       = {18},
  year         = {2020},
}

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