|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
THETA AG
|
|
|
Tel. +41 44 217 80 14
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Drawdown
|
|
Single Asset View: Eastern Europe CECE
|
|
|
Index Family: [dynAAx World]
|
|
|
|
|
|
|
|
|
|
|
|
How to read this graph
|
The drawdowns of financial prices are the measure of the decline from a historical peak. Several measures of drawdowns can quantify this risk. This includes the average drawdown, the maxium drawdown, the drawdown at risk, and the conditional drawdown at risk.
The histogram displays tabulated drawdowns from constant bins or intervals as bars. Thus a histogram gives an estimate of the density of the drawdawns. Like in the case of the financial returns we can investigate in the same way the drawdown at risk and its exptected shortfall which are marked in the tail of the plot by vertical lines. For the distribution which is added as a fit we expect a generalized Pareto distribution.
The quantile-quantile plot is a graphical method for comparing two probability distributions, by plotting their quantiles against each other. Here we use the quantile-quantile plot to compare the empirical distribution of drawdowns (on the y-axes) with the expected theoretical one (on the x-axes). If the empirical drawdowns match the theoretical ditribution, the plot will be on a straight line. Points of the empirical drawdowns which fall below the straight are assumed to be more risky than be expected for the theoretical distribution.
The tail estimate plot shows how the estimate of a high quantile in the tail of the financial returns based on the GPD approximation varies with threshold or number of extremes.
|
|
|
|
|
|
|
|
|
|
|
|
|
|