Enterprise Risk Management Formula Book
Appendix A.2: Probability Distributions:
Continuous (univariate) distributions (c) generalised Pareto, lognormal,
Student’s t
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Distribution name
  
  Generalised
  Pareto distribution (GPD) | 
  | Common notation | 
 | 
 
  | Parameters |  = shape
  parameter
  = location
  parameter
  = scale
  parameter (  )
 | 
 
  | Domain | 
 | 
 
  | Probability density
  function | 
 where 
 | 
 
  | Cumulative distribution
  function | 
 | 
 
  | Mean | 
 | 
 
  | Variance | 
 | 
 
  | Skewness | 
 | 
 
  | (Excess) kurtosis | 
 | 
 
  | Other comments | GPD is used in the peaks over thresholds variant of
  extreme value theory | 
 
 
 
  | Distribution name | Lognormal
  distribution | 
 
  | Common notation | 
 | 
 
  | Parameters |  = scale
  parameter (  )
  = location
  parameter
 | 
 
  | Domain | 
 | 
 
  | Probability density
  function | 
 | 
 
  | Cumulative distribution
  function | 
 | 
 
  | Mean | 
 | 
 
  | Variance | 
 | 
 
  | Skewness | 
 | 
 
  | (Excess) kurtosis | 
 | 
 
  | Characteristic function | No simple expression that is not divergent  | 
 
  | Other comments | The median of a lognormal distribution is  and its
  mode is  .   The truncated moments of   are: 
 | 
 
 
 
  | Distribution name | (Standard)
  Student’s t distribution | 
 
  | Common notation | 
 | 
 
  | Parameters |  = degrees
  of freedom (  , usually  is
  an integer although in some situations a non-integral  can
  arise)
 | 
 
  | Domain | 
 | 
 
  | Probability density
  function | 
 | 
 
  | Cumulative distribution
  function | 
 where  | 
 
  | Mean | 
 | 
 
  | Variance | 
 | 
 
  | Skewness | 
 | 
 
  | (Excess) kurtosis | 
 | 
 
  | Characteristic function | 
 where  is a
  Bessel function | 
 
  | Other comments | The Student’s t distribution (more simply the t
  distribution) arises when estimating the mean of a normally distributed
  population when sample sizes are small and the population standard deviation
  is unknown.   It is a special case of the generalised hyperbolic
  distribution.   Its non-central moments if  is
  even and  are: 
   If  is even
  and  then  , if  is
  odd and  then  and if  is
  odd and  then  is undefined. | 
 
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