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Some Considerations of Modeling Severity in Operational Losses

August 16, 2015
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Some Considerations of Modeling Severity in Operational Losses

In the Loss Distributional Approach (LDA) for Operational Risk models, multiple distributions, including Log Normal, Gamma, Burr, Pareto, and so on, can be considered candidates for the distribution of severity measures. However, the challenge remains in the stress testing exercise, e.g. CCAR, to relate operational losses to macro-economic scenarios denoted by a set of macro-economic

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Are These Losses from The Same Distribution?

June 14, 2015
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Are These Losses from The Same Distribution?

In Advanced Measurement Approaches (AMA) for Operational Risk models, the bank needs to segment operational losses into homogeneous segments known as “Unit of Measures (UoM)”, which are often defined by the combination of lines of business (LOB) and Basel II event types. However, how do we support whether the losses in one UoM are statistically

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Are These Losses from The Same Distribution?

June 14, 2015
By
Are These Losses from The Same Distribution?

In Advanced Measurement Approaches (AMA) for Operational Risk models, the bank needs to segment operational losses into homogeneous segments known as “Unit of Measures (UoM)”, which are often defined by the combination of lines of business (LOB) and Basel II event types. However, how do we support whether the losses in one UoM are statistically

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Granger Causality Test

May 25, 2015
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Granger Causality Test

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Granger Causality Test

May 25, 2015
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Granger Causality Test

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Read A Block of Spreadsheet with R

May 10, 2015
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Read A Block of Spreadsheet with R

In R, there are two ways to read a block of the spreadsheet, e.g. xlsx file, as the one shown below. The xlsx package provides the most intuitive interface with readColumns() function by explicitly defining the starting and the ending columns and rows. However, if we can define a named range for the block in

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Read A Block of Spreadsheet with R

May 10, 2015
By
Read A Block of Spreadsheet with R

In R, there are two ways to read a block of the spreadsheet, e.g. xlsx file, as the one shown below. The xlsx package provides the most intuitive interface with readColumns() function by explicitly defining the starting and the ending columns and rows. However, if we can define a named range for the block in

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To Difference or Not To Difference?

May 9, 2015
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To Difference or Not To Difference?

In the textbook of time series analysis, we’ve been taught to difference the time series in order to have a stationary series, which can be justified by various plots and statistical tests. In the real-world time series analysis, things are not always as clear as shown in the textbook. For instance, although the ACF plot

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To Difference or Not To Difference?

May 9, 2015
By
To Difference or Not To Difference?

In the textbook of time series analysis, we’ve been taught to difference the time series in order to have a stationary series, which can be justified by various plots and statistical tests. In the real-world time series analysis, things are not always as clear as shown in the textbook. For instance, although the ACF plot

Read more »

Modeling Count Time Series with tscount Package

March 31, 2015
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Modeling Count Time Series with tscount Package

The example below shows how to estimate a simple univariate Poisson time series model with the tscount package. While the model estimation is straightforward and yeilds very similar parameter estimates to the ones generated with the acp package (https://statcompute.wordpress.com/2015/03/29/autoregressive-conditional-poisson-model-i), the prediction mechanism is a bit tricky. 1) For the in-sample and the 1-step-ahead predictions: yhat_

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