{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T11:44:44Z","timestamp":1793274284207,"version":"4.5.0"},"reference-count":59,"publisher":"Cambridge University Press (CUP)","issue":"5","license":[{"start":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:00:00Z","timestamp":1760572800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:00:00Z","timestamp":1760572800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/tdm\/tdmrep-policy.json"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2025-24535699"],"award-info":[{"award-number":["RS-2025-24535699"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2024-00343129"],"award-info":[{"award-number":["RS-2024-00343129"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Econom. Theory"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:p>\n                    In this article, we develop a novel high-dimensional coefficient estimation procedure based on high-frequency data. Unlike usual high-dimensional regression procedures such as LASSO, we additionally handle the heavy-tailedness of high-frequency observations as well as time variations of coefficient processes. Specifically, we employ the Huber loss and a truncation scheme to handle heavy-tailed observations, while\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <mml:math xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xmlns:mnf=\"http:\/\/cambridge.org\/core\/manifest\" xmlns:cup=\"http:\/\/contentservices.cambridge.org\" xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" xmlns:m=\"http:\/\/cambridge.org\/core\/metadata\" xmlns:core=\"http:\/\/cambridge.org\/core\" xmlns:c=\"http:\/\/cambridge.org\/core\/content\" display=\"inline\">\n                          <mml:msub>\n                            <mml:mi>\u2113<\/mml:mi>\n                            <mml:mrow>\n                              <mml:mn>1<\/mml:mn>\n                            <\/mml:mrow>\n                          <\/mml:msub>\n                        <\/mml:math>\n                        <jats:tex-math>$\\ell _{1}$<\/jats:tex-math>\n                        <jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mime-subtype=\"png\" content-type=\"simple\" xlink:href=\"S0266466625100236_inline1.png\">\n                          <jats:alt-text content-type=\"machine-generated\">script l 1<\/jats:alt-text>\n                        <\/jats:inline-graphic>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    -regularization is adopted to overcome the curse of dimensionality. To account for the time-varying coefficient, we estimate local coefficients which are biased due to the\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <mml:math xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xmlns:mnf=\"http:\/\/cambridge.org\/core\/manifest\" xmlns:cup=\"http:\/\/contentservices.cambridge.org\" xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" xmlns:m=\"http:\/\/cambridge.org\/core\/metadata\" xmlns:core=\"http:\/\/cambridge.org\/core\" xmlns:c=\"http:\/\/cambridge.org\/core\/content\" display=\"inline\">\n                          <mml:msub>\n                            <mml:mi>\u2113<\/mml:mi>\n                            <mml:mrow>\n                              <mml:mn>1<\/mml:mn>\n                            <\/mml:mrow>\n                          <\/mml:msub>\n                        <\/mml:math>\n                        <jats:tex-math>$\\ell _{1}$<\/jats:tex-math>\n                        <jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mime-subtype=\"png\" content-type=\"simple\" xlink:href=\"S0266466625100236_inline2.png\">\n                          <jats:alt-text content-type=\"machine-generated\">script l 1<\/jats:alt-text>\n                        <\/jats:inline-graphic>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    -regularization. Thus, when estimating integrated coefficients, we propose a debiasing scheme to enjoy the law of large numbers property and employ a thresholding scheme to further accommodate the sparsity of the coefficients. We call this robust thresholding debiased LASSO (RED-LASSO) estimator. We show that the RED-LASSO estimator can achieve a near-optimal convergence rate. In the empirical study, we apply the RED-LASSO procedure to the high-dimensional integrated coefficient estimation using high-frequency trading data.\n                  <\/jats:p>","DOI":"10.1017\/s0266466625100236","type":"journal-article","created":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T08:24:13Z","timestamp":1760603053000},"page":"1416-1460","source":"Crossref","is-referenced-by-count":1,"title":["ROBUST HIGH-DIMENSIONAL TIME-VARYING COEFFICIENT ESTIMATION"],"prefix":"10.1017","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8981-9482","authenticated-orcid":false,"given":"Minseok","family":"Shin","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04xysgw12","id-type":"ROR","asserted-by":"publisher"}],"name":"Pohang University of Science and Technology (POSTECH)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1222-9198","authenticated-orcid":false,"given":"Donggyu","family":"Kim","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03nawhv43","id-type":"ROR","asserted-by":"publisher"}],"name":"University of California, Riverside"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2025,10,16]]},"reference":[{"key":"S0266466625100236_r56","doi-asserted-by":"publisher","DOI":"10.1214\/14-AOS1221"},{"key":"S0266466625100236_r53","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2018.1543124"},{"key":"S0266466625100236_r1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2020.01.007"},{"key":"S0266466625100236_r12","unstructured":"Chen, R. 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