Description Usage Arguments Details Author(s) References Examples

Upside Risk is the similar of semideviation taking the return above the Minimum Acceptable Return instead of using the mean return or zero. To calculate it, we take the subset of returns that are more than the target (or Minimum Acceptable Returns (MAR)) returns and take the differences of those to the target. We sum the squares and divide by the total number of returns and return the square root.

1 2 | ```
UpsideRisk(R, MAR = 0, method = c("full", "subset"), stat = c("risk",
"variance", "potential"), ...)
``` |

`R` |
an xts, vector, matrix, data frame, timeSeries or zoo object of asset returns |

`MAR` |
Minimum Acceptable Return, in the same periodicity as your returns |

`method` |
one of "full" or "subset", indicating whether to use the length of the full series or the length of the subset of the series below the MAR as the denominator, defaults to "full" |

`stat` |
one of "risk", "variance" or "potential" indicating whether to return the Upside risk, variance or potential |

`...` |
any other passthru parameters |

*UpsideRisk(R, MAR) = sqrt(1/n * sum(t=1..n)
((max(R(t)-MAR, 0))^2))*

*UpsideVariance(R, MAR) = 1/n * sum(t=1..n)((max(R(t)-MAR, 0))^2)*

*DownsidePotential(R, MAR) = 1/n * sum(t=1..n)(max(R(t)-MAR, 0))*

where *n* is either the number of observations of the entire series or
the number of observations in the subset of the series falling below the
MAR.

Matthieu Lestel

Carl Bacon, *Practical portfolio performance measurement
and attribution*, second edition 2008

1 2 3 4 5 6 7 8 9 10 | ```
data(portfolio_bacon)
MAR = 0.005
print(UpsideRisk(portfolio_bacon[,1], MAR, stat="risk")) #expected 0.02937
print(UpsideRisk(portfolio_bacon[,1], MAR, stat="variance")) #expected 0.08628
print(UpsideRisk(portfolio_bacon[,1], MAR, stat="potential")) #expected 0.01771
MAR = 0
data(managers)
print(UpsideRisk(managers['1996'], MAR, stat="risk"))
print(UpsideRisk(managers['1996',1], MAR, stat="risk")) #expected 1.820
``` |

PerformanceAnalytics documentation built on May 31, 2017, 3:16 a.m.

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