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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">ASDS</journal-id><journal-title-group><journal-title>Applied Statistics and Data Science</journal-title></journal-title-group><issn>3066-8433</issn><eissn>3066-8441</eissn><publisher><publisher-name>Art and Design</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/ASDS.2025070017</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>基于日内GARCH模型LAD估计的混成检验</title><url>https://artdesignp.com/journal/ASDS/1/7/10.61369/ASDS.2025070017</url><author>朱华锋,陈燕珊,张兴发</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>1</volume><issue>7</issue><history><date date-type="pub"><published-time>2025-09-20</published-time></date></history><abstract>基于GARCH模型的LAD估计和混成检验理论，本文将LAD估计引入日内高频数据GARCH模型，得到了对应的混成检验统计量以及估计的渐近正态性。数值模拟和实证研究的结果显示，相比低频数据模型，基于高频数据模型的估计效果更好，对应的波动率代表模型能够更好的捕捉波动率信息，参数估计的精确度也更高。</abstract><keywords>最小绝对值偏差估计（LADE）,GARCH模型,混成检验</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] Engle R F. Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica[J], 1982, 50(4): 987&amp;ndash;1007.[2] Bollerslev T. Generalized autoregressive conditional heteroskedasticity[J]. Journal of Econometrics, 1986, 31(3): 307-327.[3] Visser M P. GARCH parameter estimation using high-frequency data[J]. Journal of Financial Econometrics, 2011, 9(1): 162-197.[4] Lee S, Hansen B. Asymptotic Theory for the GARCH (1,1) Quasi-Maximum Likelihood Estimator[J]. Econometric Theory, 1994, 10(1): 29-52.[5] Hall P, Yao Q. Inference in ARCH and GARCH models with heavy-tailed errors[J]. Econometrica, 2003, 71(1): 285-317.[6] Peng L, Yao Q. Least absolute deviations estimation for ARCH and GARCH models[J]. Biometrika, 2003, 90(4): 967-975.[7] Li G, Li W K. Least absolute deviation estimation for fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity[K].Biometrika, 2008, 95(2): 399-414.[8] 李莉丽, 张兴发, 李元, 等. 基于高频数据的日频GARCH 模型估计[J]. 广西师范大学学报（自然科学版）, 2021, 39(4): 68-78.[9] 李莉丽, 张兴发, 邓春亮, 等. 基于高频数据的GARCH 模型拟极大指数似然估计[J]. 应用数学学报, 2022, 45(5): 652-664.[10] 陈燕珊, 张兴发, 田玥, 等. 基于高频数据的GARCH 模型拟极大指数似然估计的一种portmanteau Q 检验[J]. 广州大学学报（自然科学版）, 2024, 23(5):54-68.[11]Box G E P, Pierce D A. Distribution of Residual Autocorrelations in Autoregressive Integrated Moving Average Time Series Models[J]. Journal of the American Statistical Association, 65, 1509-1526.[12]Ljung G M, Box G E P. On a Measure of Lack of Fit in Time Series Models[J]. Biometrika, 1978, 65(332): 297-303.[13]McLeod A I, Li W K. Diagnostic Checking ARMA Time Series Models Using Squared-Residual Autocorrelations[J]. Journal of Time Series Analysis, 1983, 4(4): 269-273.[14]Li W K, Mak T K. On the squared residual autocorrelations in non-linear time series with conditional heteroskedasticity[J]. Journal of Time series Analysis, 1994, 15(6):627-636.[15]Min C, Ke Z. Sign-based portmanteau test for ARCH-type models with heavy-tailed innovations, Journal of Econometrics, 2015, 189(2): 313-320.[16] 陈燕珊. 基于高频数据的GARCH 模型的混成检验研究[D]. 广州大学,2024.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
