LOCAL INSTRUMENTAL VARIABLE METHOD FOR THE GENERALIZED ADDITIVE-INTERACTIVE NONLINEAR VOLATILITY MODEL ESTIMATION
In this article we consider a new separable nonparametric volatility model that includes second-order interaction terms in both mean and conditional variance functions. This is a very flexible nonparametric ARCH model that can potentially explain the behavior of the wide variety of financial assets....
Gespeichert in:
| Veröffentlicht in: | Econometric theory 2012-06, Vol.28 (3), p.629-669 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 669 |
|---|---|
| container_issue | 3 |
| container_start_page | 629 |
| container_title | Econometric theory |
| container_volume | 28 |
| creator | Levine, Michael Li, Jinguang |
| description | In this article we consider a new separable nonparametric volatility model that includes second-order interaction terms in both mean and conditional variance functions. This is a very flexible nonparametric ARCH model that can potentially explain the behavior of the wide variety of financial assets. The model is estimated using the generalized version of the local instrumental variable estimation method first introduced in Kim and Linton (2004, Econometric Theory 20, 1094–1139). This method is computationally more effective than most other nonparametric estimation methods that can potentially be used to estimate components of such a model. Asymptotic behavior of the resulting estimators is investigated and their asymptotic normality is established. Explicit expressions for asymptotic means and variances of these estimators are also obtained. |
| doi_str_mv | 10.1017/S0266466611000363 |
| format | Article |
| fullrecord | <record><control><sourceid>jstor_proqu</sourceid><recordid>TN_cdi_proquest_miscellaneous_1023194811</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><cupid>10_1017_S0266466611000363</cupid><jstor_id>23257646</jstor_id><sourcerecordid>23257646</sourcerecordid><originalsourceid>FETCH-LOGICAL-c388t-41d2e5c5e6b6e0997c2dc076e8b579e3b6dc8614487143ed5f4c0c286c56a3833</originalsourceid><addsrcrecordid>eNp1kcFLwzAUxoMoOKd_gAch4MVLNWnSpD3WNdsCWQtdHOhldGkmG5t1zXbwvzdlIqJ4SvK-3_e9xwsA1xjdY4T5wxSFjFHGGMYIIcLICehhypKAEoZOQa-Tg04_BxfOrRHCYcJJD-xUMUgVlPlUl08TkWv_mKWlTB-VgBOhx0UGh0UJ9VjAkchFmSr5IjKYZpnUciYCmWtfHHR3mBe5krlISzgrVKqlkvoZTopMKCimWk58qcgvwdmy2jh79XX2wdNQ6ME4UMVI-lkCQ-J4H1BchzYykWULZlGScBPWBnFm40XEE0sWrDYxw5TGHFNi62hJDTJhzEzEKhIT0gd3x9z3ttkdrNvPtytn7GZTvdnm4OYYhQQnNMbYo7e_0HVzaN_8dJ7CiPOI-532AT5Spm2ca-1y_t6utlX74aGO4_M_n-A9N0fP2u2b9tsQkjDinvQ6-cqstot2Vb_an63_S_0EU7WHhw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1010775700</pqid></control><display><type>article</type><title>LOCAL INSTRUMENTAL VARIABLE METHOD FOR THE GENERALIZED ADDITIVE-INTERACTIVE NONLINEAR VOLATILITY MODEL ESTIMATION</title><source>Jstor Complete Legacy</source><source>Cambridge University Press Journals Complete</source><creator>Levine, Michael ; Li, Jinguang</creator><creatorcontrib>Levine, Michael ; Li, Jinguang</creatorcontrib><description>In this article we consider a new separable nonparametric volatility model that includes second-order interaction terms in both mean and conditional variance functions. This is a very flexible nonparametric ARCH model that can potentially explain the behavior of the wide variety of financial assets. The model is estimated using the generalized version of the local instrumental variable estimation method first introduced in Kim and Linton (2004, Econometric Theory 20, 1094–1139). This method is computationally more effective than most other nonparametric estimation methods that can potentially be used to estimate components of such a model. Asymptotic behavior of the resulting estimators is investigated and their asymptotic normality is established. Explicit expressions for asymptotic means and variances of these estimators are also obtained.</description><identifier>ISSN: 0266-4666</identifier><identifier>EISSN: 1469-4360</identifier><identifier>DOI: 10.1017/S0266466611000363</identifier><language>eng</language><publisher>New York, USA: Cambridge University Press</publisher><subject>Density estimation ; Econometric models ; Econometrics ; Economic models ; Ergodic theory ; Estimating techniques ; Estimation ; Estimation methods ; Estimators ; Estimators for the mean ; Financial assets ; GARCH models ; Instrumental variables ; Instrumental variables estimation ; Mathematical analysis ; Nonparametric models ; Portfolio management ; Statistical variance ; Stochastic models ; Studies ; Time series ; Variance analysis ; Volatility</subject><ispartof>Econometric theory, 2012-06, Vol.28 (3), p.629-669</ispartof><rights>Copyright © Cambridge University Press 2011</rights><rights>Cambridge University Press 2012</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c388t-41d2e5c5e6b6e0997c2dc076e8b579e3b6dc8614487143ed5f4c0c286c56a3833</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/23257646$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.cambridge.org/core/product/identifier/S0266466611000363/type/journal_article$$EHTML$$P50$$Gcambridge$$H</linktohtml><link.rule.ids>164,314,776,780,799,27901,27902,55603,57992,58225</link.rule.ids></links><search><creatorcontrib>Levine, Michael</creatorcontrib><creatorcontrib>Li, Jinguang</creatorcontrib><title>LOCAL INSTRUMENTAL VARIABLE METHOD FOR THE GENERALIZED ADDITIVE-INTERACTIVE NONLINEAR VOLATILITY MODEL ESTIMATION</title><title>Econometric theory</title><addtitle>Econom. Theory</addtitle><description>In this article we consider a new separable nonparametric volatility model that includes second-order interaction terms in both mean and conditional variance functions. This is a very flexible nonparametric ARCH model that can potentially explain the behavior of the wide variety of financial assets. The model is estimated using the generalized version of the local instrumental variable estimation method first introduced in Kim and Linton (2004, Econometric Theory 20, 1094–1139). This method is computationally more effective than most other nonparametric estimation methods that can potentially be used to estimate components of such a model. Asymptotic behavior of the resulting estimators is investigated and their asymptotic normality is established. Explicit expressions for asymptotic means and variances of these estimators are also obtained.</description><subject>Density estimation</subject><subject>Econometric models</subject><subject>Econometrics</subject><subject>Economic models</subject><subject>Ergodic theory</subject><subject>Estimating techniques</subject><subject>Estimation</subject><subject>Estimation methods</subject><subject>Estimators</subject><subject>Estimators for the mean</subject><subject>Financial assets</subject><subject>GARCH models</subject><subject>Instrumental variables</subject><subject>Instrumental variables estimation</subject><subject>Mathematical analysis</subject><subject>Nonparametric models</subject><subject>Portfolio management</subject><subject>Statistical variance</subject><subject>Stochastic models</subject><subject>Studies</subject><subject>Time series</subject><subject>Variance analysis</subject><subject>Volatility</subject><issn>0266-4666</issn><issn>1469-4360</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp1kcFLwzAUxoMoOKd_gAch4MVLNWnSpD3WNdsCWQtdHOhldGkmG5t1zXbwvzdlIqJ4SvK-3_e9xwsA1xjdY4T5wxSFjFHGGMYIIcLICehhypKAEoZOQa-Tg04_BxfOrRHCYcJJD-xUMUgVlPlUl08TkWv_mKWlTB-VgBOhx0UGh0UJ9VjAkchFmSr5IjKYZpnUciYCmWtfHHR3mBe5krlISzgrVKqlkvoZTopMKCimWk58qcgvwdmy2jh79XX2wdNQ6ME4UMVI-lkCQ-J4H1BchzYykWULZlGScBPWBnFm40XEE0sWrDYxw5TGHFNi62hJDTJhzEzEKhIT0gd3x9z3ttkdrNvPtytn7GZTvdnm4OYYhQQnNMbYo7e_0HVzaN_8dJ7CiPOI-532AT5Spm2ca-1y_t6utlX74aGO4_M_n-A9N0fP2u2b9tsQkjDinvQ6-cqstot2Vb_an63_S_0EU7WHhw</recordid><startdate>20120601</startdate><enddate>20120601</enddate><creator>Levine, Michael</creator><creator>Li, Jinguang</creator><general>Cambridge University Press</general><scope>AAYXX</scope><scope>CITATION</scope><scope>0U~</scope><scope>1-H</scope><scope>3V.</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>8BJ</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FQK</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>JBE</scope><scope>K60</scope><scope>K6~</scope><scope>L.-</scope><scope>L.0</scope><scope>M0C</scope><scope>M2O</scope><scope>MBDVC</scope><scope>PADUT</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYYUZ</scope><scope>Q9U</scope></search><sort><creationdate>20120601</creationdate><title>LOCAL INSTRUMENTAL VARIABLE METHOD FOR THE GENERALIZED ADDITIVE-INTERACTIVE NONLINEAR VOLATILITY MODEL ESTIMATION</title><author>Levine, Michael ; Li, Jinguang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c388t-41d2e5c5e6b6e0997c2dc076e8b579e3b6dc8614487143ed5f4c0c286c56a3833</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Density estimation</topic><topic>Econometric models</topic><topic>Econometrics</topic><topic>Economic models</topic><topic>Ergodic theory</topic><topic>Estimating techniques</topic><topic>Estimation</topic><topic>Estimation methods</topic><topic>Estimators</topic><topic>Estimators for the mean</topic><topic>Financial assets</topic><topic>GARCH models</topic><topic>Instrumental variables</topic><topic>Instrumental variables estimation</topic><topic>Mathematical analysis</topic><topic>Nonparametric models</topic><topic>Portfolio management</topic><topic>Statistical variance</topic><topic>Stochastic models</topic><topic>Studies</topic><topic>Time series</topic><topic>Variance analysis</topic><topic>Volatility</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Levine, Michael</creatorcontrib><creatorcontrib>Li, Jinguang</creatorcontrib><collection>CrossRef</collection><collection>Global News & ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>ProQuest Central (Corporate)</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>International Bibliography of the Social Sciences</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>ABI/INFORM Global</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Research Library China</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><jtitle>Econometric theory</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Levine, Michael</au><au>Li, Jinguang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>LOCAL INSTRUMENTAL VARIABLE METHOD FOR THE GENERALIZED ADDITIVE-INTERACTIVE NONLINEAR VOLATILITY MODEL ESTIMATION</atitle><jtitle>Econometric theory</jtitle><addtitle>Econom. Theory</addtitle><date>2012-06-01</date><risdate>2012</risdate><volume>28</volume><issue>3</issue><spage>629</spage><epage>669</epage><pages>629-669</pages><issn>0266-4666</issn><eissn>1469-4360</eissn><abstract>In this article we consider a new separable nonparametric volatility model that includes second-order interaction terms in both mean and conditional variance functions. This is a very flexible nonparametric ARCH model that can potentially explain the behavior of the wide variety of financial assets. The model is estimated using the generalized version of the local instrumental variable estimation method first introduced in Kim and Linton (2004, Econometric Theory 20, 1094–1139). This method is computationally more effective than most other nonparametric estimation methods that can potentially be used to estimate components of such a model. Asymptotic behavior of the resulting estimators is investigated and their asymptotic normality is established. Explicit expressions for asymptotic means and variances of these estimators are also obtained.</abstract><cop>New York, USA</cop><pub>Cambridge University Press</pub><doi>10.1017/S0266466611000363</doi><tpages>41</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0266-4666 |
| ispartof | Econometric theory, 2012-06, Vol.28 (3), p.629-669 |
| issn | 0266-4666 1469-4360 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1023194811 |
| source | Jstor Complete Legacy; Cambridge University Press Journals Complete |
| subjects | Density estimation Econometric models Econometrics Economic models Ergodic theory Estimating techniques Estimation Estimation methods Estimators Estimators for the mean Financial assets GARCH models Instrumental variables Instrumental variables estimation Mathematical analysis Nonparametric models Portfolio management Statistical variance Stochastic models Studies Time series Variance analysis Volatility |
| title | LOCAL INSTRUMENTAL VARIABLE METHOD FOR THE GENERALIZED ADDITIVE-INTERACTIVE NONLINEAR VOLATILITY MODEL ESTIMATION |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-31T11%3A10%3A24IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-jstor_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=LOCAL%20INSTRUMENTAL%20VARIABLE%20METHOD%20FOR%20THE%20GENERALIZED%20ADDITIVE-INTERACTIVE%20NONLINEAR%20VOLATILITY%20MODEL%20ESTIMATION&rft.jtitle=Econometric%20theory&rft.au=Levine,%20Michael&rft.date=2012-06-01&rft.volume=28&rft.issue=3&rft.spage=629&rft.epage=669&rft.pages=629-669&rft.issn=0266-4666&rft.eissn=1469-4360&rft_id=info:doi/10.1017/S0266466611000363&rft_dat=%3Cjstor_proqu%3E23257646%3C/jstor_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1010775700&rft_id=info:pmid/&rft_cupid=10_1017_S0266466611000363&rft_jstor_id=23257646&rfr_iscdi=true |