{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T19:30:38Z","timestamp":1791487838155,"version":"4.3.4"},"reference-count":37,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2002,10,28]],"date-time":"2002-10-28T00:00:00Z","timestamp":1035763200000},"content-version":"vor","delay-in-days":57,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J of Applied Econometrics"],"published-print":{"date-parts":[[2002,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    This paper looks at some recent work on estimating quadratic variation using realized variance (RV)\u2014that is, sums of\n                    <jats:italic>M<\/jats:italic>\n                    squared returns. This econometrics has been motivated by the advent of the common availability of high\u2010frequency financial return data. When the underlying process is a semimartingale we recall the fundamental result that RV is a consistent (as\n                    <jats:italic>M<\/jats:italic>\n                    \u2192 \u221e) estimator of quadratic variation (QV). We express concern that without additional assumptions it seems difficult to give any measure of uncertainty of the RV in this context. The position dramatically changes when we work with a rather general SV model\u2014which is a special case of the semimartingale model. Then QV is integrated variance and we can derive the asymptotic distribution of the RV and its rate of convergence. These results do not require us to specify a model for either the drift or volatility functions, although we have to impose some weak regularity assumptions. We illustrate the use of the limit theory on some exchange rate data and some stock data. We show that even with large values of\n                    <jats:italic>M<\/jats:italic>\n                    the RV is sometimes a quite noisy estimator of integrated variance. Copyright \u00a9 2002 John Wiley &amp; Sons, Ltd.\n                  <\/jats:p>","DOI":"10.1002\/jae.691","type":"journal-article","created":{"date-parts":[[2002,10,29]],"date-time":"2002-10-29T08:56:52Z","timestamp":1035881812000},"page":"457-477","source":"Crossref","is-referenced-by-count":473,"title":["Estimating quadratic variation using realized variance"],"prefix":"10.1002","volume":"17","author":[{"given":"Ole E.","family":"Barndorff\u2010Nielsen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neil","family":"Shephard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2002,10,28]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.2307\/2527343"},{"key":"e_1_2_7_3_1","first-page":"105","article-title":"Great realizations","volume":"13","author":"Andersen TG","year":"2000","journal-title":"Risk"},{"key":"e_1_2_7_4_1","volume-title":"Handbook of Financial Econometrics","author":"Andersen TG","year":"2002"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-405X(01)00055-1"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214501750332965"},{"key":"e_1_2_7_7_1","doi-asserted-by":"crossref","unstructured":"AndersenTG BollerslevT DieboldFX LabysP.2001c.Modeling and forecasting realized volatility. 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