sharpeScreening: Screening using the Sharpe outperformance ratio

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

Description

Function which performs the screening of a universe of returns, and compute 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:

(1)

compute all pairwise tests of Sharpe differences using the bootstrap approach of Ledoit and Wolf (2002). This means that for a universe of N funds, we perform N*(N-1)/2 tests. The algorithm has been parallelized and the computational burden can be slip across several cores. The number of cores can be defined in control, see below.

(2)

for each fund, the false discovery rate approach by Storey (2002) is used to determine the proportions over, equal, and underperfoming funds, in terms of Sharpe ratio, in the database.

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

type

asymptotic approach (type = 1) or studentized circular bootstrap approach (type = 2). Default: type = 1.

ttype

test based on ratio (type = 1) or product (type = 2). Default: type = 2.

hac

heteroscedastic-autocorrelation consistent standard errors. Default: hac = FALSE.

nBoot

number of boostrap replications for computing the p-value. Default: nBoot = 499.

bBoot

block length in the circular bootstrap. Default: bBoot = 1, i.e. iid bootstrap. bBoot = 0 uses optimal block-length.

pBoot

symmetric p-value (pBoot = 1) or asymmetric p-value (pBoot = 2). Default: pBoot = 1.

nCore

number of cores to be used. Default: nCore = 1.

minObs

minimum number of concordant observations to compute the ratios. Default: minObs = 10.

minObsPi

minimum number of observations to compute pi0. Default: minObsPi = 1.

lambda

threshold value to compute pi0. Default: lambda = NULL, i.e. data driven choice.

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.

pval: matrix (of size N \times N) 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 (2014). The file ‘ThePeerPerformanceOfHedgeFunds.txt’ in the ‘/doc’ package's folder allows the reprodution of the steps followed in the article. See also the presentation by Kris Boudt at the R/Finance conference 2012 at http://www.rinfinance.com.

Some internal functions where adapted from Wolf's R code.

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

Please cite the package in publications. Use citation("PeerPerformance").

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.

Ardia, D., Boudt, K. (2015). The Peer Performance of Hedge Funds. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2000901

Barras, L., Scaillet, O., Wermers, R. (2010). False discoveries in mutual fund performance: Measuring luck in estimated alphas. Journal of Finance 5, pp.179–216.

Ledoit, O., Wolf, M. (2008). Robust performance hypothesis testing with the Sharpe ratio. Journal of Empirical Finance 15, pp.850–859.

Sharpe, W. F. (1994). The Sharpe ratio. Journal of Portfolio Management Fall, pp.49–58.

Storey, J. (2002). A direct approach to false discovery rates. Journal of the Royal Statistical Society B 64, pp.479–498.

See Also

sharpe, sharpeTesting, msharpeScreening and alphaScreening.


PeerPerformance documentation built on May 2, 2019, 4:53 p.m.