Description Usage Arguments Details Value Author(s) References See Also Examples
Calculates total attribution effects over multiple
periods using Menchero linking method. Used internally by
the Attribution
function. Arithmetic
attribution effects do not naturally link over time. This
function uses Menchero smoothing algorithm to adjust
attribution effects so that they can be summed up over
multiple periods Attribution effect are multiplied by the
adjustment factor
At' = At * (M + at)
where
M=\frac{\frac{1}{n}(R_{p}-R_{b})}{(1+R_{p})^{\frac{1}{n}}- (1+R_{b})^{\frac{1}{n}}}
a_{t} = ≤ft(\frac{R_{p}-R_{b}-M\times∑^{n}_{t=1}(R_{pt}- R_{bt})}{∑^{n}_{t=1}(R_{pt}-R_{bt})^{2}}\right)\times(R_{pt}-R_{bt})
In case if portfolio and benchmark returns are equal the limit of the above value is used:
M = (1 + rp)^((n - 1) / n)
At' - adjusted attribution effects at period t, At - unadjusted attribution effects at period t, Rpt - portfolio returns at period t, Rbt - benchmark returns at period t, Rp - total portfolio returns, Rb - total benchmark returns, n - number of periods
1 | Menchero(rp, rb, attributions, adjusted)
|
rp |
xts of portfolio returns |
rb |
xts of benchmark returns |
attributions |
xts with attribution effects |
adjusted |
TRUE/FALSE, whether to show original or smoothed attribution effects for each period |
The total arithmetic excess returns can be explained in terms of the sum of adjusted attribution effects:
R_{p} - R_{b} = ∑^{n}_{t=1}≤ft(Allocation_{t}+Selection_{t}+ Interaction_{t}\right)
returns a data frame with original attribution effects and total attribution effects over multiple periods
Andrii Babii
Bacon, C. Practical Portfolio Performance
Measurement and Attribution. Wiley. 2004. p. 194-196
Menchero, J. (2000) An optimized approach to
linking attribution effects over time. Journal of
Performance Measurement. Fall. p. 36-42
Attribution
Carino
Grap
Frongello
Attribution.geometric
1 2 | data(attrib)
Menchero(rp = attrib.returns[, 21], rb = attrib.returns[, 22], attributions = attrib.allocation, adjusted = FALSE)
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