GRS.test-package: GRS Test for Portfolio Efficiency, Its Statistical Power...

Description Details Author(s) References See Also Examples

Description

Computational resources for test proposed by Gibbons, Ross, Shanken (1989)<DOI:10.2307/1913625>. It also has the functions for the power analysis and the choice of the optimal level of significance. The optimal level is determined by minimizing the expected loss from hypothesis testing.

Details

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The package accompanies the working paper:

Kim and Shamsuddin, 2017, Empirical Validity of Asset-pricing Models: Application of Optimal Significance Level and Equal Probability Test

The function GRS.test returns the GRS test statistics with model estimation results.

The function GRS.MLtest provides an alternative test statistic with theta and theta* estimation results.

Additional functions for the power analysis and calculation of optimal level of significance are also included.

Author(s)

Jae H. Kim <[email protected]>

Maintainer: Jae H. Kim <[email protected]>

References

Gibbons, Ross, Shanken, 1989. A test of the efficiency of a given portfolio, Econometrica, 57,1121-1152. <DOI:10.2307/1913625>

Fama and French, 1993, Common risk factors in the returns on stocks and bonds, Journal of Financial Economics, 33, 3-56. <DOI:10.1016/0304-405X(93)90023-5>

Fama and French, 2015, A five-factor asset-pricing model, Journal of Financial Economics, 1-22. <DOI:http://dx.doi.org/10.1016/j.jfineco.2014.10.010>

See Also

The examples replicate the results reported in Fama and French (1993) and Kim and Shamsuddin (2016)

Examples

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data(data)
factor.mat = data[1:342,2:4]            # Fama-French 3-factor model
ret.mat = data[1:342,8:ncol(data)]      # 25 size-BM portfolio returns
GRS.test(ret.mat,factor.mat)$GRS.stat   # Table 9C of Fama-French (1993)

Example output

          GRS
[1,] 1.604879

GRS.test documentation built on Dec. 4, 2017, 9:03 a.m.