sharpeScreening: Screening using the Sharpe outperformance ratio

Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/sharpeScreening.R

Description

Function which performs the screening of a universe of returns, and computes the Sharpe outperformance ratio.

Usage

1
sharpeScreening(X, control = list())

Arguments

X

Matrix (TxN) of T returns for the N funds. NA values are allowed.

control

Control parameters (see *Details*).

Details

The Sharpe ratio (Sharpe 1992) is one industry standard for measuring the absolute risk adjusted performance of hedge funds. We propose to complement the Sharpe ratio with the fund's outperformance ratio, defined as the percentage number of funds that have a significantly lower Sharpe ratio. In a pairwise testing framework, a fund can have a significantly higher Sharpe ratio because of luck. We correct for this by applying the false discovery rate approach by Storey (2002).

For the testing, only the intersection of non-NA observations for the two funds are used.

The methodology proceeds as follows:

The argument control is a list that can supply any of the following components:

Value

A list with the following components:

n: Vector (of length N) of number of non-NA observations.

npeer: Vector (of length N) of number of available peers.

sharpe: Vector (of length N) of unconditional Sharpe ratios.

dsharpe: Matrix (of size NxN) of Sharpe ratios differences.

tstat: Matrix (of size NxN) of t-statistics.

pval: Matrix (of size NxN) of pvalues of test for Sharpe ratios differences.

lambda: vector (of length N) of lambda values.

pizero: vector (of length N) of probability of equal performance.

pipos: vector (of length N) of probability of outperformance performance.

pineg: Vector (of length N) of probability of underperformance performance.

Note

Further details on the methdology with an application to the hedge fund industry is given in in Ardia and Boudt (2016).

Some internal functions where adapted from Michael Wolf MATLAB code.

Application of the false discovery rate approach applied to the mutual fund industry has been presented in Barraz, Scaillet and Wermers (2010).

Author(s)

David Ardia and Kris Boudt.

References

Ardia, D., Boudt, K. (2015). Testing equality of modified Sharpe ratios. Finance Research Letters 13, pp.97-104. doi: 10.1016/j.frl.2015.02.008

Ardia, D., Boudt, K. (2016). The peer performance ratios of hedge funds. Working paper. doi: 10.2139/ssrn.2000901

Barras, L., Scaillet, O., Wermers, R. (2010). False discoveries in mutual fund performance: Measuring luck in estimated alphas. Journal of Finance 65(1), pp.179-216. doi: 10.1111/j.1540-6261.2009.01527.x

Sharpe, W.F. (1994). The Sharpe ratio. Journal of Portfolio Management 21(1), pp.49-58. doi: 10.3905/jpm.1994.409501

Ledoit, O., Wolf, M. (2008). Robust performance hypothesis testing with the Sharpe ratio. Journal of Empirical Finance 15(5), pp.850-859. doi: 10.1016/j.jempfin.2008.03.002

Storey, J. (2002). A direct approach to false discovery rates. Journal of the Royal Statistical Society B 64(3), pp.479-498. doi: 10.1111/1467-9868.00346

See Also

sharpe, sharpeTesting, msharpeScreening and alphaScreening.

Examples

1
2
3
4
5
6
7
8
9
## Load the data (randomized data of monthly hedge fund returns)
data("hfdata")
rets = hfdata[,1:10]

## Sharpe screening 
sharpeScreening(rets, control = list(nCore = 1))

## Sharpe screening with bootstrap and HAC standard deviation
sharpeScreening(rets, control = list(nCore = 1, type = 2, hac = TRUE))

ArdiaD/PeerPerformance documentation built on June 7, 2017, 10:44 a.m.