Algorithms for Optimization of ValueatRisk 
This paper suggests two new heuristic algorithms for optimization of
Valueat
Risk (VaR). By definition, VaR is an estimate of the maximum
portfolio loss
during a standardized period with some confidence level. Pdffile 

An
Improved Methodology for Measuring VaR 
The purpose of this article is to describe a RiskMetrics VaR
methodology that allows for a more realistic model of financial
return tail distributions. Pdffile 

Analyzing Perceived Downside Risk: the Component ValueatRisk
Framework 
We develop the ‘Component ValueatRisk (VaR)’ framework for
companies to identify downside risk as perceived by shareholders.
This framework allows for decomposition into components attributable
to the underlying risk factors. (pdffile available for download) 

Approximations 
Approximations for the ValueatRisk approach to riskreturn
analysis. Pdffile 

Basic Methodes and Implementation 
The authors describe how to implement VaR. Mathematica is used to
demonstrate the basic methods for calculation of VaR for a
hypothetical portfolio of a stock and a foreign bond. Pdffile 

Comparative analyses ... 
... of expected shortfall and valueatrisk under market stress.
Pdffile 

Credit Risk Contributions 
Credit Risk Contributions to ValueatRisk and Expected Shortfall.
Pdffile 

Credit Risk Optimization with Conditional ValueAtRisk Criterion 
The model is based on the Conditional ValueatRisk (CVaR) risk
measure, the expected loss exceeding ValueatRisk. CVaR is also
known as Mean Excess, Mean Shortfall, or Tail VaR. Pdffile 2000 

Credit ValueatRisk Constraints 
Credit ValueatRisk Constraints, Pension and Insurance Fund Capital
Requirements, Credit Rationing and Monetary Policy. Pdffile 2002 

Dynamic ValueatRisk 
The purpose of this study is to describe dynamic ValueatRisk and
to estimated the advantages and disadvantages of using it in
portfolio management. 

Efficient Monte Carlo methods for valueatrisk 
The calculation of valueatrisk (VAR) for large portfolios of
complex derivative securities presents a tradeoff between speed and
accuracy. The fastest methods rely on simplifying assumptions about
changes in underlying risk factors and about how a portfolio’s value
responds to these changes in the risk factors. pdffile 

Forecasting Economic and Financial Variables with Global VARs 
This paper considers the problem of forecasting real and financial
macroeconomic variables across a large number of countries in the
global economy. Building on the forecast combination literature, the
effects of model and estimation uncertainty on forecast outcomes are
examined by pooling forecasts obtained from different GVAR models
estimated over alternative sample periods. Given the size of the
modeling problem, and the heterogeneity of economies considered —
industrialised, emerging, and less developed countries — as well as
the very real likelihood of possibly multiple structural breaks,
averaging forecasts across both models and windows makes a
significant difference. Indeed the doubleaveraged GVAR forecasts
performed better than the benchmark competitors, especially for
output, inflation and real equity prices. Abstract, full text
available for download 

LongTerm Value at Risk 
This paper investigates the estimation of longterm VaR. It also
suggests a simple approach to the estimation of longterm VaR that
avoids problems associated with the squareroot rule for
extrapolating VaR, as well as those associated with attempts to
extrapolate daytoday volatility forecasts over longer horizons.
pdffile 2003 

Optimization of Conditional ValueatRisk 
A new approach to optimizing or hedging a portfolio of financial
instruments to reduce risk is presented and tested on applications.
It focuses on minimizing Conditional ValueatRisk (CVaR) rather
than minimizing ValueatRisk (VaR), but portfolios with low CVaR
necessarily have low VaR as well. pdffile 

Portfolio Optimization with Conditional ValueAtRisk Objective and
Constraints 
pdffile 2001 

Remarks on the valueatrisk and the conditional valueatrisk 
The valueatrisk (VaR) and the conditional valueatrisk (CVaR) are
two commonly used risk measures. We state some of their properties
and make a comparison. Moreover, the structure of the portfolio
optimization problem using the VaR and CVaR is studied. pdffile 

Sensitivity Analysis of Values at Risk 
The aim of this paper is to analyze the sensitivity of Value at Risk
(VaR) with respect to portfolio allocation. Pdffile 

Using ValueatRisk to Control Risk Taking: How Wrong Can you Be? 
We study a source of bias in valueatrisk estimates that has not
previously been recognized. Because valueatrisk estimates are
based on past data, a trader will often have a good understanding of
the errors in the valueatrisk estimate, and it will be possible
for her to choose portfolios for which she knows that the
valueatrisk estimate is less than the “true” value at risk. pdffile 

Value At Risk and Maximum Loss Optimization 
The risk measure \Value At Risk" (VAR) is presented from a new point
of view and a general de nition of VAR is derived. Next, "Maximum
Loss" (ML) is formulated as a mathematical optimization problem and
its modelling is described. Pdffile 1995 

Value At Risk Models In Finance 
The main objective of this paper is to survey and evaluate the
performance of the most popular univariate VaR methodologies,
paying particular attention to their underlying assumptions and to
their logical flaws. Pdffile 2001 

Value at Risk: A methodology for Information Security Risk
Assessment 
This paper presents Value at Risk (VAR), a new methodology for
Information Security Risk Assessment. VAR summarizes the worst loss
due to a security breach over a target horizon, with a given level
of confidence. Pdffile 

ValueatRisk Based Risk Management 
Optimal Policies and Asset Prices. Pdffile 

Variance Reduction Techniques for Estimating ValueatRisk 
This paper describes, analyzes and evaluates an algorithm for
estimating portfolio loss probabilities using Monte Carlo
simulation. Pdffile 
