average_daily_volume_sovereign: Average daily volume in Million Euro of sovereign bonds of...
In Martin-Summer-1090/syslosseval: Provide data and functions to support the analysis in Systemic Loss Evaluation
Description
Usage
Format
Source
Description
A dataset containing the average daily volume of sovereign bonds for Germany, Spain, Great Britain,
France, Italy, Japan, United States and the rest of the world starting in 2009 up to the end of 2019.
Rest of the world: We have no data on the ADV in all the rest of exposures, because there are no
or insufficient direct observations. Cont and Schaanning (2016) observe a high correlation between
nominal debt outsanding and adv. They use this correlation by estimating unobserved ADV using regression
techniques. We apply an even simpler estimate. We compute the share of nominal debt outstanding of our
observed countries DE, ES, FR, IT, JP, GB, US in the total nominal government debt outstanding. We use this
proportionality factor to impute an ADV on the residual position. The dataset is compiled by running the
make_price_volume_data script in the data-raw folder. The exact compilation can be looked up in
this script.
Usage
1
Format
A data frame with 88 rows and 5 variables:
- Country
Country, ISO-code of the country.The rest of the world all excluding DE, ES, FR, IT, JP, UK, US is calles Total
- Year
Year, year of the observation
- Volume
Volume, Average daily turnover volume in Million Euro
- Unit
Unit, unit of Volume
- Currency
Currency, currency of volume
Source
The volume data are hand-collected from various sources on the internet
DE https://www.deutsche-finanzagentur.de/en/institutional-investors/secondary-market/
ES https://www.tesoro.es/sites/default/files/estadisticas/15I.xlsx
FR https://www.afme.eu/reports/data/details//Government-Bond-Data-Report-Q2-2019
IT https://infostat.bancaditalia.it/
JP https://asianbondsonline.adb.org/data-portal/
UK https://www.dmo.gov.uk/data/gilt-market/turnover-data/
US https://www.sifma.org/resources/research/us-treasury-trading-volume/
Total https://www.bis.org/statistics/secstats.htm and estimation of
Cont and Schaanning 2017, equation (28)
Martin-Summer-1090/syslosseval documentation built on Dec. 17, 2021, 3:14 a.m.
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Description Usage Format Source
Description
A dataset containing the average daily volume of sovereign bonds for Germany, Spain, Great Britain,
France, Italy, Japan, United States and the rest of the world starting in 2009 up to the end of 2019.
Rest of the world: We have no data on the ADV in all the rest of exposures, because there are no
or insufficient direct observations. Cont and Schaanning (2016) observe a high correlation between
nominal debt outsanding and adv. They use this correlation by estimating unobserved ADV using regression
techniques. We apply an even simpler estimate. We compute the share of nominal debt outstanding of our
observed countries DE, ES, FR, IT, JP, GB, US in the total nominal government debt outstanding. We use this
proportionality factor to impute an ADV on the residual position. The dataset is compiled by running the
make_price_volume_data script in the data-raw folder. The exact compilation can be looked up in
this script.
Usage
1 |
Format
A data frame with 88 rows and 5 variables:
- Country
Country, ISO-code of the country.The rest of the world all excluding DE, ES, FR, IT, JP, UK, US is calles Total
- Year
Year, year of the observation
- Volume
Volume, Average daily turnover volume in Million Euro
- Unit
Unit, unit of Volume
- Currency
Currency, currency of volume
Source
The volume data are hand-collected from various sources on the internet DE https://www.deutsche-finanzagentur.de/en/institutional-investors/secondary-market/ ES https://www.tesoro.es/sites/default/files/estadisticas/15I.xlsx FR https://www.afme.eu/reports/data/details//Government-Bond-Data-Report-Q2-2019 IT https://infostat.bancaditalia.it/ JP https://asianbondsonline.adb.org/data-portal/ UK https://www.dmo.gov.uk/data/gilt-market/turnover-data/ US https://www.sifma.org/resources/research/us-treasury-trading-volume/ Total https://www.bis.org/statistics/secstats.htm and estimation of Cont and Schaanning 2017, equation (28)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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