Transform approach for operational risk modeling: value-at-risk and tail conditional expectation

2008 ◽  
Vol 3 (2) ◽  
pp. 45-61 ◽  
Author(s):  
Jiwook Jang ◽  
Genyuan Fu
Keyword(s):  
At Risk ◽  
Conditional Expectation ◽  
Value At Risk ◽  
Operational Risk ◽  
Risk Modeling ◽  
Tail Conditional Expectation ◽  
Operational Risk Modeling

Applying robust methods to operational risk modeling

2006 ◽  
Vol 1 (1) ◽  
pp. 27-41 ◽  
Author(s):  
Anna Chernobai ◽  
Svetlozar Rachev
Keyword(s):  
Operational Risk ◽  
Risk Modeling ◽  
Robust Methods ◽  
Operational Risk Modeling

scholarly journals Value-at-risk modeling and forecasting with range-based volatility models: empirical evidence

2017 ◽  
Vol 28 (75) ◽  
pp. 361-376 ◽  
Author(s):  
Leandro dos Santos Maciel ◽  
Rosangela Ballini
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Garch Models ◽  
Time Interval ◽  
Risk Modeling ◽  
Additional Information ◽  
Volatility Models ◽  
Modeling And Forecasting ◽  
The Difference ◽  
Market Indexes

ABSTRACT This article considers range-based volatility modeling for identifying and forecasting conditional volatility models based on returns. It suggests the inclusion of range measuring, defined as the difference between the maximum and minimum price of an asset within a time interval, as an exogenous variable in generalized autoregressive conditional heteroscedasticity (GARCH) models. The motivation is evaluating whether range provides additional information to the volatility process (intraday variability) and improves forecasting, when compared to GARCH-type approaches and the conditional autoregressive range (CARR) model. The empirical analysis uses data from the main stock market indexes for the U.S. and Brazilian economies, i.e. S&P 500 and IBOVESPA, respectively, within the period from January 2004 to December 2014. Performance is compared in terms of accuracy, by means of value-at-risk (VaR) modeling and forecasting. The out-of-sample results indicate that range-based volatility models provide more accurate VaR forecasts than GARCH models.

Operational Risk: Modeling Analytics

Technometrics ◽  
2007 ◽  
Vol 49 (4) ◽  
pp. 492-492
Author(s):  
Kristina Sendova
Keyword(s):  
Operational Risk ◽  
Risk Modeling ◽  
Operational Risk Modeling

scholarly journals Tail Conditional Expectations for Exponential Dispersion Models

2005 ◽  
Vol 35 (1) ◽  
pp. 189-209 ◽  
Author(s):  
Zinoviy Landsman ◽  
Emiliano A. Valdez
Keyword(s):  
Conditional Expectation ◽  
Value At Risk ◽  
Risk Measure ◽  
Inverse Gaussian ◽  
Dispersion Models ◽  
Average Amount ◽  
Tail Conditional Expectation ◽  
Conditional Expectations ◽  
Exponential Dispersion Models ◽  
Fixed Period

There is a growing interest in the use of the tail conditional expectation as a measure of risk. For an institution faced with a random loss, the tail conditional expectation represents the conditional average amount of loss that can be incurred in a fixed period, given that the loss exceeds a specified value. This value is typically based on the quantile of the loss distribution, the so-called value-at-risk. The tail conditional expectation can therefore provide a measure of the amount of capital needed due to exposure to loss. This paper examines this risk measure for “exponential dispersion models”, a wide and popular class of distributions to actuaries which, on one hand, generalizes the Normal and shares some of its many important properties, but on the other hand, contains many distributions of nonnegative random variables like the Gamma and the Inverse Gaussian.

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