<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE root>
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="research-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Economics and Mathematical Methods</journal-id><journal-title-group><journal-title xml:lang="en">Economics and Mathematical Methods</journal-title><trans-title-group xml:lang="ru"><trans-title>Экономика и математические методы</trans-title></trans-title-group></journal-title-group><issn publication-format="print">0424-7388</issn><issn publication-format="electronic">3034-6177</issn><publisher><publisher-name xml:lang="en">The Russian Academy of Sciences</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">265471</article-id><article-id pub-id-type="doi">10.31857/S0424738824030095</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>Mathematical analysis of economic models</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>Математический анализ экономических моделей</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Methods for estimating integrated variance: Jump robustness issues in high frequency time series</article-title><trans-title-group xml:lang="ru"><trans-title>Методы оценивания интегрированной дисперсии: проблемы устойчивости к скачкам в высокочастотных временных рядах</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Kosimov</surname><given-names>Z. O.</given-names></name><name xml:lang="ru"><surname>Косимов</surname><given-names>З. О.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>zohirsho1@gmail.com</email><xref ref-type="aff" rid="aff1"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Lomonosov Moscow State University</institution></aff><aff><institution xml:lang="ru">МГУ имени М. В. Ломоносова</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2024-10-05" publication-format="electronic"><day>05</day><month>10</month><year>2024</year></pub-date><volume>60</volume><issue>3</issue><issue-title xml:lang="en"/><issue-title xml:lang="ru"/><fpage>107</fpage><lpage>117</lpage><history><date date-type="received" iso-8601-date="2024-10-04"><day>04</day><month>10</month><year>2024</year></date><date date-type="accepted" iso-8601-date="2024-10-04"><day>04</day><month>10</month><year>2024</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2024, Russian Academy of Sciences</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2024, Российская академия наук</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="en">Russian Academy of Sciences</copyright-holder><copyright-holder xml:lang="ru">Российская академия наук</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/" start_date="2025-10-05"/></permissions><self-uri xlink:href="https://ogarev-online.ru/0424-7388/article/view/265471">https://ogarev-online.ru/0424-7388/article/view/265471</self-uri><abstract xml:lang="en"><p>Integrated variance is a volatility measure of a process in continuous time, which is applicable in financial mathematics as a means to optimize a portfolio, project dynamics of a price of a financial asset. The consistency of an estimator for integrated variance of a random process is at the core of this article. A fundamental diffusion process is extended by adding a jump component as a means of improving the descriptive function of the process. It is activity of jumps that is a factor subject to which is the consistency of the estimator for integrated variance. Due to this fact, consistency is defined as an extent to which the estimator at hand is jump robust. Two main methods for estimating integrated variance are considered and the capacity of corresponding estimators to withstand the effect of jumps while converging is briefly analyzed. The arguments indicating a necessity for further research of the effect of jumps with reference to works of the authors who have established a ground for analysis of integrated variance and those works containing main asymptotic results for consistency of integrated variance estimators are elaborated. Based on this, avenue for further research and development of asymptotic theory for consistency of an estimator for integrated variance is identified.</p></abstract><trans-abstract xml:lang="ru"><p>Интегрированная дисперсия является мерой волатильности процесса в непрерывном времени и используется в финансовой математике как инструмент оптимизации портфеля, прогноза динамики цены финансового актива. Состоятельность оценки интегрированной дисперсии случайного процесса находится в центре внимания настоящей статьи. Основополагающий диффузионный процесс расширен посредством включения компоненты скачков как средства улучшения описательной функции процесса. Именно активность скачков является тем фактором, который обуславливает состоятельность оценки интегрированной дисперсии. Поэтому состоятельность оценки определяется как степень ее устойчивости к скачкам. Рассмотрены два основных метода оценивания интегрированной дисперсии и проанализирована способность соответствующих оценок в нейтрализации эффекта скачков на сходимость. Приведены доводы, указывающие на необходимость дальнейшего исследования эффекта скачков, ссылаясь на работы авторов, заложившие основу анализа интегрированной дисперсии, а также на работы, в которых содержатся основные асимптотические результаты относительно устойчивости оценки интегрированной дисперсии к скачкам. По результатам проведенного анализа выделены направления дальнейшего развития асимптотической теории для анализа состоятельности оценки интегрированной дисперсии.</p></trans-abstract><kwd-group xml:lang="en"><kwd>integrated variance</kwd><kwd>diffusion process</kwd><kwd>consistency</kwd><kwd>jumps</kwd><kwd>jump robustness</kwd><kwd>asymptotic theory</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>интегрированная дисперсия</kwd><kwd>диффузионный процесс</kwd><kwd>состоятельность</kwd><kwd>скачки</kwd><kwd>устойчивость к скачкам</kwd><kwd>асимптотическая теория</kwd></kwd-group><funding-group/></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Andersen T. G., Dobrev D., Schaumburg E. (2012). Jump robust volatility estimation using nearest neighbour truncation. Journal of Econometrics, 169, 75–93.</mixed-citation></ref><ref id="B2"><label>2.</label><mixed-citation>Aїt-Sahalia Y., Jacod J. (2014). High frequency financial econometrics. Princeton: Princeton University Press.</mixed-citation></ref><ref id="B3"><label>3.</label><mixed-citation>Barndorff-Nielsen O.E., Graversen S. E., Jacod J., Podolskij M., Shephard N. (2006). A central limit theorem for realized power and bipower variations of continuous semimartingales. In: From stochastic calculus to mathematical finance: The Shiryaev festschrift. Y. Kabanov, R. Lipster (eds.), 33–68. Berlin, Heidelberg: Springer–Verlag.</mixed-citation></ref><ref id="B4"><label>4.</label><mixed-citation>Barndorff-Nielsen O.E., Shephard N. (2002). Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Part 2. Journal of the Royal Statistical Society Series B — Statistical Methodology, 64, 253–280.</mixed-citation></ref><ref id="B5"><label>5.</label><mixed-citation>Barndorff-Nielsen O.E., Shephard N. (2004). Power and bipower variation with stochastic volatility and jumps. Journal of Financial Econometrics, 2, 1, 1–37.</mixed-citation></ref><ref id="B6"><label>6.</label><mixed-citation>Barndorff-Nielsen O.E., Shephard N. (2006). Econometrics of testing for jumps in financial economics using bipower variation. Journal of Financial Econometrics, 4, 1, 1–30.</mixed-citation></ref><ref id="B7"><label>7.</label><mixed-citation>Barndorff-Nielsen O.E., Shephard N. (2007). Variation, jumps, market frictions and high frequency data in financial econometrics. In: R. Blundell, T. Persson, W. Newey (eds.). Advances in Economics and Econometrics. Theory and Applications. Ninth World Congress. Cambridge: Cambridge University Press.</mixed-citation></ref><ref id="B8"><label>8.</label><mixed-citation>Barndorff-Nielsen O.E., Shephard N., Winkel M. (2006). Limit theorems for multipower variation in the presence of jumps in financial econometrics. Stochastic Processes and their Applications, 116, 796–806.</mixed-citation></ref><ref id="B9"><label>9.</label><mixed-citation>Christensen K. (2016). High frequency data econometrics. PhD course, Aarhus University. Available at: https://econ.au.dk/fileadmin/site_files/filer_oekonomi/subsites/creates/Diverse_2016/PhD_High-Frequency/Slides_day_2.pdf</mixed-citation></ref><ref id="B10"><label>10.</label><mixed-citation>Eberlein E. (2010). Jump processes. In: Encyclopaedia of quantitative finance by Rama Cont. In 4 vols., 1850–1869. Chichester: John Wiley &amp; Sons.</mixed-citation></ref><ref id="B11"><label>11.</label><mixed-citation>Jacod J., Shiryaev A. N. (2003). Limit theorems for stochastic processes. 2nd ed. N.Y.: Springer-Verlag.</mixed-citation></ref><ref id="B12"><label>12.</label><mixed-citation>Mancini C. (2001). Disentangling the jumps of the diffusion in a geometric jumping Brownian motion. Giornale dell’Istituto Italiano degli Attuari, 64, 19–47.</mixed-citation></ref><ref id="B13"><label>13.</label><mixed-citation>Mancini C. (2009). Non-parametric threshold estimation for models with stochastic diffusion coefficient and jumps. Scandinavian Journal of Statistics, 36, 270–296.</mixed-citation></ref><ref id="B14"><label>14.</label><mixed-citation>Mancini C. (2012). Jumps. Handbook of volatility models and their applications. L. Bauwens, C. Hafner, S. Laurent (eds.). New Jersey: Wiley and Sons, 403–445.</mixed-citation></ref><ref id="B15"><label>15.</label><mixed-citation>Protter P. (2004). Stochastic integration and differential equations. N.Y.: Springer–Verlag.</mixed-citation></ref><ref id="B16"><label>16.</label><mixed-citation>Shiryaev A. N. (1999). Essential of stochastic finance: Facts, models and theory. Singapore: World Scientific.</mixed-citation></ref><ref id="B17"><label>17.</label><mixed-citation>Shiryaev A. N. (1999). Essential of stochastic finance: Facts, models, theory. Advanced Series on Statistical Science &amp; Applied Probability. 1st ed. Singapore: Scientific Pub. Co Inc.</mixed-citation></ref><ref id="B18"><label>18.</label><mixed-citation>Woerner H. C.J. (2004). Power and multipower variance: Inference for high frequency data. In: Shiryaev Al. Handbook of Stochastic Finance. Chapter 12, 343–364. N.Y.: Springer–Verlag.</mixed-citation></ref></ref-list></back></article>
