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Heidelberger, Philip’ AUTHOR PAGE
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E-Mail: philiph@us.ibm.com
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webpage: http://www.research.ibm.com/people/b/berger/home.html
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Affiliation: IBM
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Efficient Monte Carlo Methods for Value-at-Risk 2000
Glasserman, Paul
Heidelberger, Philip
Shahabuddin, Perwez
 
The calculation of value-at-risk for large portfolios presents a tradeoff between speed and accuracy, with the fastest methods relying on rough approx...
Value-at-Risk with Heavy-Tailed Risk Factors 2000
Glasserman, Paul
Heidelberger, Philip
Shahabuddin, Perwez
 
This paper develops efficient methods for computing portfolio value-at-risk (VAR) when the underlying risk factors have a heavy-tailed distribution. W...
Variance Reduction Techniques for Estimating Value at Risk 2000
Glasserman, Paul
Heidelberger, Philip
Shahabuddin, Perwez
 
This paper describes, analyzes and evaluates an algorithm for estimating portfolio loss probabilities using Monte Carlo simulation. Obtaining accurate...
Variance Reduction Techniques for Value-at-Risk With Heavy-Tailed Risk Factors 2000
Glasserman, Paul
Heidelberger, Philip
Shahabuddin, Perwez
 
The calculation of value-at-risk (VAR) for large portfolios of complex instruments is among the most demanding and widespread computational challenges...
Importance Sampling and Stratification for Value-at-Risk 2000
Glasserman, Paul
Heidelberger, Philip
Shahabuddin, Perwez
 
This paper proposes and evaluates variance reduction techniques for efficient estimation of portfolio loss probabilities using Monte Carlo simulation....
Stratification Issues in Estimatinf Value-at-Risk 2000
Glasserman, Paul
Heidelberger, Philip
Shahabuddin, Perwez
 
This paper considers efficient estimation of value-at-risk, which is an important problem in risk management. The value-at-risk is an extreme quantile...
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