Indirect estimation of randomized generalized autoregressive conditional heteroskedastic models
The class of generalized autoregressive conditional heteroskedastic (GARCH) models can be used to describe the volatility with less parameters than autoregressive conditional heteroskedastic (ARCH)-type models, their distributions are heavy-tailed, with time-dependent conditional variance, and are a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sampaio, J.M [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2015 |
|---|
| Rechteinformationen: |
Nutzungsrecht: © 2014 Taylor & Francis 2014 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Journal of statistical computation and simulation - Abingdon : Taylor & Francis, 1972, 85(2015), 13, Seite 2702-2717 |
|---|---|
| Übergeordnetes Werk: |
volume:85 ; year:2015 ; number:13 ; pages:2702-2717 |
| Links: |
|---|
| DOI / URN: |
10.1080/00949655.2014.934244 |
|---|
| Katalog-ID: |
OLC1966206348 |
|---|
| LEADER | 01000caa a2200265 4500 | ||
|---|---|---|---|
| 001 | OLC1966206348 | ||
| 003 | DE-627 | ||
| 005 | 20220217030012.0 | ||
| 007 | tu | ||
| 008 | 160206s2015 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1080/00949655.2014.934244 |2 doi | |
| 028 | 5 | 2 | |a PQ20160617 |
| 035 | |a (DE-627)OLC1966206348 | ||
| 035 | |a (DE-599)GBVOLC1966206348 | ||
| 035 | |a (PRQ)c2682-b276e3f3b9d43b928f29499a57066407494ed29d683d40f6f73c78d1354fe4250 | ||
| 035 | |a (KEY)0054835920150000085001302702indirectestimationofrandomizedgeneralizedautoregre | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 510 |q DNB |
| 084 | |a 31.73 |2 bkl | ||
| 084 | |a 54.76 |2 bkl | ||
| 100 | 1 | |a Sampaio, J.M |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Indirect estimation of randomized generalized autoregressive conditional heteroskedastic models |
| 264 | 1 | |c 2015 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 520 | |a The class of generalized autoregressive conditional heteroskedastic (GARCH) models can be used to describe the volatility with less parameters than autoregressive conditional heteroskedastic (ARCH)-type models, their distributions are heavy-tailed, with time-dependent conditional variance, and are able to model clustering of volatility. Despite all these facts, the way that GARCH models are built imposes limits on the heaviness of the tails of their unconditional distribution. The class of randomized generalized autoregressive conditional heteroskedastic (R-GARCH) models includes the ARCH and GARCH models allowing the use of stable innovations. Estimation methods and empirical analysis of R-GARCH models are the focus of this work. We present the indirect inference method to estimate the R-GARCH models, some simulations and an empirical application. | ||
| 540 | |a Nutzungsrecht: © 2014 Taylor & Francis 2014 | ||
| 650 | 4 | |a stable distributions | |
| 650 | 4 | |a time series | |
| 650 | 4 | |a indirect estimation | |
| 650 | 4 | |a R-GARCH-t | |
| 650 | 4 | |a R-GARCH | |
| 650 | 4 | |a Regression analysis | |
| 650 | 4 | |a Simulation | |
| 650 | 4 | |a Estimating techniques | |
| 650 | 4 | |a Stochastic models | |
| 650 | 4 | |a Volatility | |
| 650 | 4 | |a Statistical inference | |
| 700 | 1 | |a Morettin, P.A |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of statistical computation and simulation |d Abingdon : Taylor & Francis, 1972 |g 85(2015), 13, Seite 2702-2717 |w (DE-627)129391786 |w (DE-600)184912-8 |w (DE-576)014776928 |x 0094-9655 |7 nnns |
| 773 | 1 | 8 | |g volume:85 |g year:2015 |g number:13 |g pages:2702-2717 |
| 856 | 4 | 1 | |u http://dx.doi.org/10.1080/00949655.2014.934244 |3 Volltext |
| 856 | 4 | 2 | |u http://www.tandfonline.com/doi/abs/10.1080/00949655.2014.934244 |
| 856 | 4 | 2 | |u http://search.proquest.com/docview/1688627381 |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a SSG-OPC-MAT | ||
| 912 | |a GBV_ILN_70 | ||
| 936 | b | k | |a 31.73 |q AVZ |
| 936 | b | k | |a 54.76 |q AVZ |
| 951 | |a AR | ||
| 952 | |d 85 |j 2015 |e 13 |h 2702-2717 | ||
| author_variant |
j s js |
|---|---|
| matchkey_str |
article:00949655:2015----::nietsiainfadmzdeeaieatrgesvcnii |
| hierarchy_sort_str |
2015 |
| bklnumber |
31.73 54.76 |
| publishDate |
2015 |
| allfields |
10.1080/00949655.2014.934244 doi PQ20160617 (DE-627)OLC1966206348 (DE-599)GBVOLC1966206348 (PRQ)c2682-b276e3f3b9d43b928f29499a57066407494ed29d683d40f6f73c78d1354fe4250 (KEY)0054835920150000085001302702indirectestimationofrandomizedgeneralizedautoregre DE-627 ger DE-627 rakwb eng 510 DNB 31.73 bkl 54.76 bkl Sampaio, J.M verfasserin aut Indirect estimation of randomized generalized autoregressive conditional heteroskedastic models 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The class of generalized autoregressive conditional heteroskedastic (GARCH) models can be used to describe the volatility with less parameters than autoregressive conditional heteroskedastic (ARCH)-type models, their distributions are heavy-tailed, with time-dependent conditional variance, and are able to model clustering of volatility. Despite all these facts, the way that GARCH models are built imposes limits on the heaviness of the tails of their unconditional distribution. The class of randomized generalized autoregressive conditional heteroskedastic (R-GARCH) models includes the ARCH and GARCH models allowing the use of stable innovations. Estimation methods and empirical analysis of R-GARCH models are the focus of this work. We present the indirect inference method to estimate the R-GARCH models, some simulations and an empirical application. Nutzungsrecht: © 2014 Taylor & Francis 2014 stable distributions time series indirect estimation R-GARCH-t R-GARCH Regression analysis Simulation Estimating techniques Stochastic models Volatility Statistical inference Morettin, P.A oth Enthalten in Journal of statistical computation and simulation Abingdon : Taylor & Francis, 1972 85(2015), 13, Seite 2702-2717 (DE-627)129391786 (DE-600)184912-8 (DE-576)014776928 0094-9655 nnns volume:85 year:2015 number:13 pages:2702-2717 http://dx.doi.org/10.1080/00949655.2014.934244 Volltext http://www.tandfonline.com/doi/abs/10.1080/00949655.2014.934244 http://search.proquest.com/docview/1688627381 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 31.73 AVZ 54.76 AVZ AR 85 2015 13 2702-2717 |
| spelling |
10.1080/00949655.2014.934244 doi PQ20160617 (DE-627)OLC1966206348 (DE-599)GBVOLC1966206348 (PRQ)c2682-b276e3f3b9d43b928f29499a57066407494ed29d683d40f6f73c78d1354fe4250 (KEY)0054835920150000085001302702indirectestimationofrandomizedgeneralizedautoregre DE-627 ger DE-627 rakwb eng 510 DNB 31.73 bkl 54.76 bkl Sampaio, J.M verfasserin aut Indirect estimation of randomized generalized autoregressive conditional heteroskedastic models 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The class of generalized autoregressive conditional heteroskedastic (GARCH) models can be used to describe the volatility with less parameters than autoregressive conditional heteroskedastic (ARCH)-type models, their distributions are heavy-tailed, with time-dependent conditional variance, and are able to model clustering of volatility. Despite all these facts, the way that GARCH models are built imposes limits on the heaviness of the tails of their unconditional distribution. The class of randomized generalized autoregressive conditional heteroskedastic (R-GARCH) models includes the ARCH and GARCH models allowing the use of stable innovations. Estimation methods and empirical analysis of R-GARCH models are the focus of this work. We present the indirect inference method to estimate the R-GARCH models, some simulations and an empirical application. Nutzungsrecht: © 2014 Taylor & Francis 2014 stable distributions time series indirect estimation R-GARCH-t R-GARCH Regression analysis Simulation Estimating techniques Stochastic models Volatility Statistical inference Morettin, P.A oth Enthalten in Journal of statistical computation and simulation Abingdon : Taylor & Francis, 1972 85(2015), 13, Seite 2702-2717 (DE-627)129391786 (DE-600)184912-8 (DE-576)014776928 0094-9655 nnns volume:85 year:2015 number:13 pages:2702-2717 http://dx.doi.org/10.1080/00949655.2014.934244 Volltext http://www.tandfonline.com/doi/abs/10.1080/00949655.2014.934244 http://search.proquest.com/docview/1688627381 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 31.73 AVZ 54.76 AVZ AR 85 2015 13 2702-2717 |
| allfields_unstemmed |
10.1080/00949655.2014.934244 doi PQ20160617 (DE-627)OLC1966206348 (DE-599)GBVOLC1966206348 (PRQ)c2682-b276e3f3b9d43b928f29499a57066407494ed29d683d40f6f73c78d1354fe4250 (KEY)0054835920150000085001302702indirectestimationofrandomizedgeneralizedautoregre DE-627 ger DE-627 rakwb eng 510 DNB 31.73 bkl 54.76 bkl Sampaio, J.M verfasserin aut Indirect estimation of randomized generalized autoregressive conditional heteroskedastic models 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The class of generalized autoregressive conditional heteroskedastic (GARCH) models can be used to describe the volatility with less parameters than autoregressive conditional heteroskedastic (ARCH)-type models, their distributions are heavy-tailed, with time-dependent conditional variance, and are able to model clustering of volatility. Despite all these facts, the way that GARCH models are built imposes limits on the heaviness of the tails of their unconditional distribution. The class of randomized generalized autoregressive conditional heteroskedastic (R-GARCH) models includes the ARCH and GARCH models allowing the use of stable innovations. Estimation methods and empirical analysis of R-GARCH models are the focus of this work. We present the indirect inference method to estimate the R-GARCH models, some simulations and an empirical application. Nutzungsrecht: © 2014 Taylor & Francis 2014 stable distributions time series indirect estimation R-GARCH-t R-GARCH Regression analysis Simulation Estimating techniques Stochastic models Volatility Statistical inference Morettin, P.A oth Enthalten in Journal of statistical computation and simulation Abingdon : Taylor & Francis, 1972 85(2015), 13, Seite 2702-2717 (DE-627)129391786 (DE-600)184912-8 (DE-576)014776928 0094-9655 nnns volume:85 year:2015 number:13 pages:2702-2717 http://dx.doi.org/10.1080/00949655.2014.934244 Volltext http://www.tandfonline.com/doi/abs/10.1080/00949655.2014.934244 http://search.proquest.com/docview/1688627381 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 31.73 AVZ 54.76 AVZ AR 85 2015 13 2702-2717 |
| allfieldsGer |
10.1080/00949655.2014.934244 doi PQ20160617 (DE-627)OLC1966206348 (DE-599)GBVOLC1966206348 (PRQ)c2682-b276e3f3b9d43b928f29499a57066407494ed29d683d40f6f73c78d1354fe4250 (KEY)0054835920150000085001302702indirectestimationofrandomizedgeneralizedautoregre DE-627 ger DE-627 rakwb eng 510 DNB 31.73 bkl 54.76 bkl Sampaio, J.M verfasserin aut Indirect estimation of randomized generalized autoregressive conditional heteroskedastic models 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The class of generalized autoregressive conditional heteroskedastic (GARCH) models can be used to describe the volatility with less parameters than autoregressive conditional heteroskedastic (ARCH)-type models, their distributions are heavy-tailed, with time-dependent conditional variance, and are able to model clustering of volatility. Despite all these facts, the way that GARCH models are built imposes limits on the heaviness of the tails of their unconditional distribution. The class of randomized generalized autoregressive conditional heteroskedastic (R-GARCH) models includes the ARCH and GARCH models allowing the use of stable innovations. Estimation methods and empirical analysis of R-GARCH models are the focus of this work. We present the indirect inference method to estimate the R-GARCH models, some simulations and an empirical application. Nutzungsrecht: © 2014 Taylor & Francis 2014 stable distributions time series indirect estimation R-GARCH-t R-GARCH Regression analysis Simulation Estimating techniques Stochastic models Volatility Statistical inference Morettin, P.A oth Enthalten in Journal of statistical computation and simulation Abingdon : Taylor & Francis, 1972 85(2015), 13, Seite 2702-2717 (DE-627)129391786 (DE-600)184912-8 (DE-576)014776928 0094-9655 nnns volume:85 year:2015 number:13 pages:2702-2717 http://dx.doi.org/10.1080/00949655.2014.934244 Volltext http://www.tandfonline.com/doi/abs/10.1080/00949655.2014.934244 http://search.proquest.com/docview/1688627381 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 31.73 AVZ 54.76 AVZ AR 85 2015 13 2702-2717 |
| allfieldsSound |
10.1080/00949655.2014.934244 doi PQ20160617 (DE-627)OLC1966206348 (DE-599)GBVOLC1966206348 (PRQ)c2682-b276e3f3b9d43b928f29499a57066407494ed29d683d40f6f73c78d1354fe4250 (KEY)0054835920150000085001302702indirectestimationofrandomizedgeneralizedautoregre DE-627 ger DE-627 rakwb eng 510 DNB 31.73 bkl 54.76 bkl Sampaio, J.M verfasserin aut Indirect estimation of randomized generalized autoregressive conditional heteroskedastic models 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The class of generalized autoregressive conditional heteroskedastic (GARCH) models can be used to describe the volatility with less parameters than autoregressive conditional heteroskedastic (ARCH)-type models, their distributions are heavy-tailed, with time-dependent conditional variance, and are able to model clustering of volatility. Despite all these facts, the way that GARCH models are built imposes limits on the heaviness of the tails of their unconditional distribution. The class of randomized generalized autoregressive conditional heteroskedastic (R-GARCH) models includes the ARCH and GARCH models allowing the use of stable innovations. Estimation methods and empirical analysis of R-GARCH models are the focus of this work. We present the indirect inference method to estimate the R-GARCH models, some simulations and an empirical application. Nutzungsrecht: © 2014 Taylor & Francis 2014 stable distributions time series indirect estimation R-GARCH-t R-GARCH Regression analysis Simulation Estimating techniques Stochastic models Volatility Statistical inference Morettin, P.A oth Enthalten in Journal of statistical computation and simulation Abingdon : Taylor & Francis, 1972 85(2015), 13, Seite 2702-2717 (DE-627)129391786 (DE-600)184912-8 (DE-576)014776928 0094-9655 nnns volume:85 year:2015 number:13 pages:2702-2717 http://dx.doi.org/10.1080/00949655.2014.934244 Volltext http://www.tandfonline.com/doi/abs/10.1080/00949655.2014.934244 http://search.proquest.com/docview/1688627381 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 31.73 AVZ 54.76 AVZ AR 85 2015 13 2702-2717 |
| language |
English |
| source |
Enthalten in Journal of statistical computation and simulation 85(2015), 13, Seite 2702-2717 volume:85 year:2015 number:13 pages:2702-2717 |
| sourceStr |
Enthalten in Journal of statistical computation and simulation 85(2015), 13, Seite 2702-2717 volume:85 year:2015 number:13 pages:2702-2717 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
stable distributions time series indirect estimation R-GARCH-t R-GARCH Regression analysis Simulation Estimating techniques Stochastic models Volatility Statistical inference |
| dewey-raw |
510 |
| isfreeaccess_bool |
false |
| container_title |
Journal of statistical computation and simulation |
| authorswithroles_txt_mv |
Sampaio, J.M @@aut@@ Morettin, P.A @@oth@@ |
| publishDateDaySort_date |
2015-01-01T00:00:00Z |
| hierarchy_top_id |
129391786 |
| dewey-sort |
3510 |
| id |
OLC1966206348 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1966206348</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220217030012.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160206s2015 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/00949655.2014.934244</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160617</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1966206348</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1966206348</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c2682-b276e3f3b9d43b928f29499a57066407494ed29d683d40f6f73c78d1354fe4250</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0054835920150000085001302702indirectestimationofrandomizedgeneralizedautoregre</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.73</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.76</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sampaio, J.M</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Indirect estimation of randomized generalized autoregressive conditional heteroskedastic models</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The class of generalized autoregressive conditional heteroskedastic (GARCH) models can be used to describe the volatility with less parameters than autoregressive conditional heteroskedastic (ARCH)-type models, their distributions are heavy-tailed, with time-dependent conditional variance, and are able to model clustering of volatility. Despite all these facts, the way that GARCH models are built imposes limits on the heaviness of the tails of their unconditional distribution. The class of randomized generalized autoregressive conditional heteroskedastic (R-GARCH) models includes the ARCH and GARCH models allowing the use of stable innovations. Estimation methods and empirical analysis of R-GARCH models are the focus of this work. We present the indirect inference method to estimate the R-GARCH models, some simulations and an empirical application.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: © 2014 Taylor & Francis 2014</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">stable distributions</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">time series</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">indirect estimation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">R-GARCH-t</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">R-GARCH</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Regression analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Simulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Estimating techniques</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Stochastic models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Volatility</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistical inference</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Morettin, P.A</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of statistical computation and simulation</subfield><subfield code="d">Abingdon : Taylor & Francis, 1972</subfield><subfield code="g">85(2015), 13, Seite 2702-2717</subfield><subfield code="w">(DE-627)129391786</subfield><subfield code="w">(DE-600)184912-8</subfield><subfield code="w">(DE-576)014776928</subfield><subfield code="x">0094-9655</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:85</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:13</subfield><subfield code="g">pages:2702-2717</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1080/00949655.2014.934244</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://www.tandfonline.com/doi/abs/10.1080/00949655.2014.934244</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://search.proquest.com/docview/1688627381</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">31.73</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.76</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">85</subfield><subfield code="j">2015</subfield><subfield code="e">13</subfield><subfield code="h">2702-2717</subfield></datafield></record></collection>
|
| author |
Sampaio, J.M |
| spellingShingle |
Sampaio, J.M ddc 510 bkl 31.73 bkl 54.76 misc stable distributions misc time series misc indirect estimation misc R-GARCH-t misc R-GARCH misc Regression analysis misc Simulation misc Estimating techniques misc Stochastic models misc Volatility misc Statistical inference Indirect estimation of randomized generalized autoregressive conditional heteroskedastic models |
| authorStr |
Sampaio, J.M |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)129391786 |
| format |
Article |
| dewey-ones |
510 - Mathematics |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0094-9655 |
| topic_title |
510 DNB 31.73 bkl 54.76 bkl Indirect estimation of randomized generalized autoregressive conditional heteroskedastic models stable distributions time series indirect estimation R-GARCH-t R-GARCH Regression analysis Simulation Estimating techniques Stochastic models Volatility Statistical inference |
| topic |
ddc 510 bkl 31.73 bkl 54.76 misc stable distributions misc time series misc indirect estimation misc R-GARCH-t misc R-GARCH misc Regression analysis misc Simulation misc Estimating techniques misc Stochastic models misc Volatility misc Statistical inference |
| topic_unstemmed |
ddc 510 bkl 31.73 bkl 54.76 misc stable distributions misc time series misc indirect estimation misc R-GARCH-t misc R-GARCH misc Regression analysis misc Simulation misc Estimating techniques misc Stochastic models misc Volatility misc Statistical inference |
| topic_browse |
ddc 510 bkl 31.73 bkl 54.76 misc stable distributions misc time series misc indirect estimation misc R-GARCH-t misc R-GARCH misc Regression analysis misc Simulation misc Estimating techniques misc Stochastic models misc Volatility misc Statistical inference |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| author2_variant |
p m pm |
| hierarchy_parent_title |
Journal of statistical computation and simulation |
| hierarchy_parent_id |
129391786 |
| dewey-tens |
510 - Mathematics |
| hierarchy_top_title |
Journal of statistical computation and simulation |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)129391786 (DE-600)184912-8 (DE-576)014776928 |
| title |
Indirect estimation of randomized generalized autoregressive conditional heteroskedastic models |
| ctrlnum |
(DE-627)OLC1966206348 (DE-599)GBVOLC1966206348 (PRQ)c2682-b276e3f3b9d43b928f29499a57066407494ed29d683d40f6f73c78d1354fe4250 (KEY)0054835920150000085001302702indirectestimationofrandomizedgeneralizedautoregre |
| title_full |
Indirect estimation of randomized generalized autoregressive conditional heteroskedastic models |
| author_sort |
Sampaio, J.M |
| journal |
Journal of statistical computation and simulation |
| journalStr |
Journal of statistical computation and simulation |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science |
| recordtype |
marc |
| publishDateSort |
2015 |
| contenttype_str_mv |
txt |
| container_start_page |
2702 |
| author_browse |
Sampaio, J.M |
| container_volume |
85 |
| class |
510 DNB 31.73 bkl 54.76 bkl |
| format_se |
Aufsätze |
| author-letter |
Sampaio, J.M |
| doi_str_mv |
10.1080/00949655.2014.934244 |
| dewey-full |
510 |
| title_sort |
indirect estimation of randomized generalized autoregressive conditional heteroskedastic models |
| title_auth |
Indirect estimation of randomized generalized autoregressive conditional heteroskedastic models |
| abstract |
The class of generalized autoregressive conditional heteroskedastic (GARCH) models can be used to describe the volatility with less parameters than autoregressive conditional heteroskedastic (ARCH)-type models, their distributions are heavy-tailed, with time-dependent conditional variance, and are able to model clustering of volatility. Despite all these facts, the way that GARCH models are built imposes limits on the heaviness of the tails of their unconditional distribution. The class of randomized generalized autoregressive conditional heteroskedastic (R-GARCH) models includes the ARCH and GARCH models allowing the use of stable innovations. Estimation methods and empirical analysis of R-GARCH models are the focus of this work. We present the indirect inference method to estimate the R-GARCH models, some simulations and an empirical application. |
| abstractGer |
The class of generalized autoregressive conditional heteroskedastic (GARCH) models can be used to describe the volatility with less parameters than autoregressive conditional heteroskedastic (ARCH)-type models, their distributions are heavy-tailed, with time-dependent conditional variance, and are able to model clustering of volatility. Despite all these facts, the way that GARCH models are built imposes limits on the heaviness of the tails of their unconditional distribution. The class of randomized generalized autoregressive conditional heteroskedastic (R-GARCH) models includes the ARCH and GARCH models allowing the use of stable innovations. Estimation methods and empirical analysis of R-GARCH models are the focus of this work. We present the indirect inference method to estimate the R-GARCH models, some simulations and an empirical application. |
| abstract_unstemmed |
The class of generalized autoregressive conditional heteroskedastic (GARCH) models can be used to describe the volatility with less parameters than autoregressive conditional heteroskedastic (ARCH)-type models, their distributions are heavy-tailed, with time-dependent conditional variance, and are able to model clustering of volatility. Despite all these facts, the way that GARCH models are built imposes limits on the heaviness of the tails of their unconditional distribution. The class of randomized generalized autoregressive conditional heteroskedastic (R-GARCH) models includes the ARCH and GARCH models allowing the use of stable innovations. Estimation methods and empirical analysis of R-GARCH models are the focus of this work. We present the indirect inference method to estimate the R-GARCH models, some simulations and an empirical application. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 |
| container_issue |
13 |
| title_short |
Indirect estimation of randomized generalized autoregressive conditional heteroskedastic models |
| url |
http://dx.doi.org/10.1080/00949655.2014.934244 http://www.tandfonline.com/doi/abs/10.1080/00949655.2014.934244 http://search.proquest.com/docview/1688627381 |
| remote_bool |
false |
| author2 |
Morettin, P.A |
| author2Str |
Morettin, P.A |
| ppnlink |
129391786 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth |
| doi_str |
10.1080/00949655.2014.934244 |
| up_date |
2024-07-03T20:44:36.192Z |
| _version_ |
1803592113787502592 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1966206348</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220217030012.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160206s2015 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/00949655.2014.934244</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160617</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1966206348</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1966206348</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c2682-b276e3f3b9d43b928f29499a57066407494ed29d683d40f6f73c78d1354fe4250</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0054835920150000085001302702indirectestimationofrandomizedgeneralizedautoregre</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.73</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.76</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sampaio, J.M</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Indirect estimation of randomized generalized autoregressive conditional heteroskedastic models</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The class of generalized autoregressive conditional heteroskedastic (GARCH) models can be used to describe the volatility with less parameters than autoregressive conditional heteroskedastic (ARCH)-type models, their distributions are heavy-tailed, with time-dependent conditional variance, and are able to model clustering of volatility. Despite all these facts, the way that GARCH models are built imposes limits on the heaviness of the tails of their unconditional distribution. The class of randomized generalized autoregressive conditional heteroskedastic (R-GARCH) models includes the ARCH and GARCH models allowing the use of stable innovations. Estimation methods and empirical analysis of R-GARCH models are the focus of this work. We present the indirect inference method to estimate the R-GARCH models, some simulations and an empirical application.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: © 2014 Taylor & Francis 2014</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">stable distributions</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">time series</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">indirect estimation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">R-GARCH-t</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">R-GARCH</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Regression analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Simulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Estimating techniques</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Stochastic models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Volatility</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistical inference</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Morettin, P.A</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of statistical computation and simulation</subfield><subfield code="d">Abingdon : Taylor & Francis, 1972</subfield><subfield code="g">85(2015), 13, Seite 2702-2717</subfield><subfield code="w">(DE-627)129391786</subfield><subfield code="w">(DE-600)184912-8</subfield><subfield code="w">(DE-576)014776928</subfield><subfield code="x">0094-9655</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:85</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:13</subfield><subfield code="g">pages:2702-2717</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1080/00949655.2014.934244</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://www.tandfonline.com/doi/abs/10.1080/00949655.2014.934244</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://search.proquest.com/docview/1688627381</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">31.73</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.76</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">85</subfield><subfield code="j">2015</subfield><subfield code="e">13</subfield><subfield code="h">2702-2717</subfield></datafield></record></collection>
|
| score |
7.399255 |