# stats-maxdd: Drawdown Statistics In fBasics: Rmetrics - Markets and Basic Statistics

## Description

This is a collection and description of functions which compute drawdown statistics. Included are density, distribution function, and random generation for the maximum drawdown distribution. In addition the expectation of drawdowns for Brownian motion can be computed.

The functions are:

 `dmaxdd` the Density function, `pmaxdd` the Distribution function, `rmaxdd` the random number generator, `maxddStats` the expectation of drawdowns.

## Usage

 ```1 2 3 4 5``` ```dmaxdd(x, sd = 1, horizon = 100, N = 1000) pmaxdd(q, sd = 1, horizon = 100, N = 1000) rmaxdd(n, mean = 0, sd = 1, horizon = 100) maxddStats(mean = 0, sd = 1, horizon = 1000) ```

## Arguments

 `x, q` a numeric vector of quantiles. `n` an integer value, the number of observations. `mean, sd` two numeric values, the mean and standard deviation. `horizon` an integer value, the (run time) horizon of the investor. `N` an integer value, the precession index for summations. Before you change this value please inspect Magdon-Ismail et. al. (2003).

## Value

`dmaxdd`
returns for a trendless Brownian process `mean=0` and standard deviation "sd" the density from the probability that the maximum drawdown "D" is larger or equal to "h" in the interval [0,T], where "T" denotes the time `horizon` of the investor.

`pmaxdd`
returns for a trendless Brownian process `mean=0` and standard deviation "sd" the the probability that the maximum drawdown "D" is larger or equal to "h" in the interval [0,T], where "T" denotes the time `horizon` of the investor.

`rmaxdd`
returns for a Brownian Motion process with mean `mean` and standard deviation `sd` random variates of maximum drawdowns.

`maxddStats`
returns the expectation Value E[D] of maximum drawdowns of Brownian Motion for a given drift `mean`, variance `sd`, and runtime `horizon` of the Brownian Motion process.

## Note

Currrently, for the functions `dmaxdd` and `pmaxdd` only the trend or driftless case is implemented.

## References

Magdon-Ismail M., Atiya A.F., Pratap A., Abu-Mostafa Y.S. (2003); On the Maximum Drawdown of a Brownian Motion, Preprint, CalTech, Pasadena USA, p. 24.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38``` ```## rmaxdd - # Set a random seed set.seed(1953) # horizon of the investor, time T horizon = 1000 # number of MC samples, N -> infinity samples = 1000 # Range of expected Drawdons xlim = c(0, 5)*sqrt(horizon) # Plot Histogram of Simulated Max Drawdowns: r = rmaxdd(n = samples, mean = 0, sd = 1, horizon = horizon) hist(x = r, n = 40, probability = TRUE, xlim = xlim, col = "steelblue4", border = "white", main = "Max. Drawdown Density") points(r, rep(0, samples), pch = 20, col = "orange", cex = 0.7) ## dmaxdd - x = seq(0, xlim[2], length = 200) d = dmaxdd(x = x, sd = 1, horizon = horizon, N = 1000) lines(x, d, lwd = 2) ## pmaxdd - # Count Frequencies of Drawdowns Greater or Equal to "h": n = 50 x = seq(0, xlim[2], length = n) g = rep(0, times = n) for (i in 1:n) g[i] = length (r[r > x[i]]) / samples plot(x, g, type ="h", lwd = 3, xlab = "q", main = "Max. Drawdown Probability") # Compare with True Probability "G_D(h)": x = seq(0, xlim[2], length = 5*n) p = pmaxdd(q = x, sd = 1, horizon = horizon, N = 5000) lines(x, p, lwd = 2, col="steelblue4") ## maxddStats - # Compute expectation Value E[D]: maxddStats(mean = -0.5, sd = 1, horizon = 10^(1:4)) maxddStats(mean = 0.0, sd = 1, horizon = 10^(1:4)) maxddStats(mean = 0.5, sd = 1, horizon = 10^(1:4)) ```

fBasics documentation built on Nov. 18, 2017, 4:05 a.m.