Risk Equals a Probability Distribution Quantile (Value-at-Risk)

2009 ◽  
pp. 35-41
2020 ◽  
pp. 161-177
Author(s):  
Paul Weirich

In finance, a common way of evaluating an investment uses the investment’s expected return and the investment’s risk, in the sense of the investment’s volatility, or exposure to chance. A version of this method derives from a general mean-risk evaluation of acts, under the assumption that only money, risk, and their sources matter. Although the method does not require a measure of risk, finance investigates measures of risks to assist evaluations of risks. An investment creates possible returns, and the variance of the probability distribution of their utilities is a measure of the investment’s risk. This measure neglects some factors affecting an investment’s risk, and so is satisfactory only in special cases. Another measure of risk is known as value-at-risk, or VAR. It also neglects some factors affecting an investment’s risk, and so should be restricted to special cases.


2021 ◽  
Author(s):  
Julie Ao

This thesis describes the joint probability distribution of defaults in two, three and four dimensions. In particular, default as defined by Merton and Black and Cox using analytical and simulated Monte Carlo approaches. Our analytical approach in a Merton setting, utilizes the multivariate normal to compute the joint probability distribution in any dimension. In a Black-Cox setting, analytical solutions are defined in specific dimensions, therefore we rely on a simulated approach. The precision of our simulated approaches are evaluated using 104, 107 and up to 107.5 paths 1. We use our results to compare the probability of defaults in both settings as well as tail dependence, portfolio value and value at risk. Tail dependence is evaluated in two and three dimensions with ρ=0.3 and ρ=0.9. We define covariance parameters in four dimensions; "normal" and "crisis" market conditions, to evaluate portfolio value in a credit and market portfolio and value at risk.


2021 ◽  
Author(s):  
Julie Ao

This thesis describes the joint probability distribution of defaults in two, three and four dimensions. In particular, default as defined by Merton and Black and Cox using analytical and simulated Monte Carlo approaches. Our analytical approach in a Merton setting, utilizes the multivariate normal to compute the joint probability distribution in any dimension. In a Black-Cox setting, analytical solutions are defined in specific dimensions, therefore we rely on a simulated approach. The precision of our simulated approaches are evaluated using 104, 107 and up to 107.5 paths 1. We use our results to compare the probability of defaults in both settings as well as tail dependence, portfolio value and value at risk. Tail dependence is evaluated in two and three dimensions with ρ=0.3 and ρ=0.9. We define covariance parameters in four dimensions; "normal" and "crisis" market conditions, to evaluate portfolio value in a credit and market portfolio and value at risk.


2019 ◽  
Vol 256 ◽  
pp. 113918 ◽  
Author(s):  
Yangyang Liu ◽  
Zhongqi Shen ◽  
Xiaowei Tang ◽  
Hongbo Lian ◽  
Jiarui Li ◽  
...  

2015 ◽  
Vol 44 (5) ◽  
pp. 259-267
Author(s):  
Frank Schuhmacher ◽  
Benjamin R. Auer
Keyword(s):  
At Risk ◽  

Controlling ◽  
2004 ◽  
Vol 16 (7) ◽  
pp. 425-426
Author(s):  
Mischa Seiter ◽  
Sven Eckert
Keyword(s):  
At Risk ◽  

CFA Digest ◽  
1999 ◽  
Vol 29 (2) ◽  
pp. 76-78
Author(s):  
Thomas J. Latta

Author(s):  
Arndt P. Funken ◽  
Alexander Obeid
Keyword(s):  
At Risk ◽  

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