Fitting a Pareto-Normal-Pareto distribution to the residuals of financial data
被引:2
|
作者:
Ellis, S
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机构:
Potchefstroom Univ Christian Higher Educ, ZA-2050 Potchefstroom, South AfricaPotchefstroom Univ Christian Higher Educ, ZA-2050 Potchefstroom, South Africa
Ellis, S
[1
]
Steyn, F
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机构:
Potchefstroom Univ Christian Higher Educ, ZA-2050 Potchefstroom, South AfricaPotchefstroom Univ Christian Higher Educ, ZA-2050 Potchefstroom, South Africa
Steyn, F
[1
]
Venter, H
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机构:
Potchefstroom Univ Christian Higher Educ, ZA-2050 Potchefstroom, South AfricaPotchefstroom Univ Christian Higher Educ, ZA-2050 Potchefstroom, South Africa
Venter, H
[1
]
机构:
[1] Potchefstroom Univ Christian Higher Educ, ZA-2050 Potchefstroom, South Africa
GARCH models;
Extreme Value Theory;
Value-at-Risk;
Expected Shortfall;
D O I:
10.1007/BF03354611
中图分类号:
O21 [概率论与数理统计];
C8 [统计学];
学科分类号:
020208 ;
070103 ;
0714 ;
摘要:
The Pareto-Normal-Pareto (PNP) distribution assumes that, for log returns of financial series, the innovations are normally distributed between two threshold values with Pareto tails below and above the respective thresholds. These threshold values can be estimated by maximum likelihood estimation (MLE). Monte Carlo simulations of normal, as well as heavy tailed error distributions, are used to compare the methods using this distribution with other methods to calculate Value-at-Risk (VaR) and Expected Shortfall (ESf). It is also applied to South African stock exchange data.