QuantLib
A free/open-source library for quantitative finance
Reference manual - version 1.20
Public Types | Public Member Functions | List of all members
GenericGaussianStatistics< Stat > Class Template Reference

Statistics tool for gaussian-assumption risk measures. More...

#include <ql/math/statistics/gaussianstatistics.hpp>

Inherits Stat.

Public Types

typedef Stat::value_type value_type
 

Public Member Functions

 GenericGaussianStatistics (const Stat &s)
 
Gaussian risk measures
Real gaussianDownsideVariance () const
 
Real gaussianDownsideDeviation () const
 
Real gaussianRegret (Real target) const
 
Real gaussianPercentile (Real percentile) const
 
Real gaussianTopPercentile (Real percentile) const
 
Real gaussianPotentialUpside (Real percentile) const
 gaussian-assumption Potential-Upside at a given percentile More...
 
Real gaussianValueAtRisk (Real percentile) const
 gaussian-assumption Value-At-Risk at a given percentile More...
 
Real gaussianExpectedShortfall (Real percentile) const
 gaussian-assumption Expected Shortfall at a given percentile More...
 
Real gaussianShortfall (Real target) const
 gaussian-assumption Shortfall (observations below target)
 
Real gaussianAverageShortfall (Real target) const
 gaussian-assumption Average Shortfall (averaged shortfallness)
 

Detailed Description

template<class Stat>
class QuantLib::GenericGaussianStatistics< Stat >

Statistics tool for gaussian-assumption risk measures.

This class wraps a somewhat generic statistic tool and adds a number of gaussian risk measures (e.g.: value-at-risk, expected shortfall, etc.) based on the mean and variance provided by the underlying statistic tool.

Member Function Documentation

◆ gaussianDownsideVariance()

Real gaussianDownsideVariance ( ) const

returns the downside variance, defined as

\[ \frac{N}{N-1} \times \frac{ \sum_{i=1}^{N} \theta \times x_i^{2}}{ \sum_{i=1}^{N} w_i} \]

, where \( \theta \) = 0 if x > 0 and \( \theta \) =1 if x <0

◆ gaussianDownsideDeviation()

Real gaussianDownsideDeviation ( ) const

returns the downside deviation, defined as the square root of the downside variance.

◆ gaussianRegret()

Real gaussianRegret ( Real  target) const

returns the variance of observations below target

\[ \frac{\sum w_i (min(0, x_i-target))^2 }{\sum w_i}. \]

See Dembo, Freeman "The Rules Of Risk", Wiley (2001)

◆ gaussianPercentile()

Real gaussianPercentile ( Real  percentile) const

gaussian-assumption y-th percentile, defined as the value x such that

\[ y = \frac{1}{\sqrt{2 \pi}} \int_{-\infty}^{x} \exp (-u^2/2) du \]

Precondition
percentile must be in range (0%-100%) extremes excluded

◆ gaussianTopPercentile()

Real gaussianTopPercentile ( Real  percentile) const
Precondition
percentile must be in range (0%-100%) extremes excluded

◆ gaussianPotentialUpside()

Real gaussianPotentialUpside ( Real  percentile) const

gaussian-assumption Potential-Upside at a given percentile

Precondition
percentile must be in range [90%-100%)

◆ gaussianValueAtRisk()

Real gaussianValueAtRisk ( Real  percentile) const

gaussian-assumption Value-At-Risk at a given percentile

Precondition
percentile must be in range [90%-100%)

◆ gaussianExpectedShortfall()

Real gaussianExpectedShortfall ( Real  percentile) const

gaussian-assumption Expected Shortfall at a given percentile

Assuming a gaussian distribution it returns the expected loss in case that the loss exceeded a VaR threshold,

\[ \mathrm{E}\left[ x \;|\; x < \mathrm{VaR}(p) \right], \]

that is the average of observations below the given percentile \( p \). Also know as conditional value-at-risk.

See Artzner, Delbaen, Eber and Heath, "Coherent measures of risk", Mathematical Finance 9 (1999)

Precondition
percentile must be in range [90%-100%)