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Statistical inference for GQARCH-Itô-jumps model based on the realized range volatility 基于已实现波动范围的 GQARCH-Itô-jumps 模型的统计推断
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-12-19 DOI: 10.1111/jtsa.12729
Jin Yu Fu, Jin Guan Lin, Guangying Liu, Hong Xia Hao
{"title":"Statistical inference for GQARCH-Itô-jumps model based on the realized range volatility","authors":"Jin Yu Fu,&nbsp;Jin Guan Lin,&nbsp;Guangying Liu,&nbsp;Hong Xia Hao","doi":"10.1111/jtsa.12729","DOIUrl":"10.1111/jtsa.12729","url":null,"abstract":"<p>This article introduces a novel approach that unifies two types of models: one is the continuous-time jump-diffusion used to model high-frequency market financial data, and the other is discrete-time GQARCH for modeling low-frequency financial data by embedding the discrete GQARCH structure with jumps in the instantaneous volatility process. This model is named GQARCH-Itô-Jumps model. Quasi-likelihood functions for the low-frequency GQARCH structure are developed for the parametric estimations. In the quasi-likelihood functions, for high-frequency financial data, the realized range-based estimations are adopted as the ‘observations’, rather than the realized return-based volatility estimators which entail the loss of intra-day information of the price movements. Meanwhile, the asymptotic properties are mainly established for the proposed estimators in the case of finite activity jumps. Moreover, simulation studies and some financial data are implemented to check the finite sample performance of the proposed methodology.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138825729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Count network autoregression 计数网络自回归
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-12-19 DOI: 10.1111/jtsa.12728
Mirko Armillotta, Konstantinos Fokianos
{"title":"Count network autoregression","authors":"Mirko Armillotta,&nbsp;Konstantinos Fokianos","doi":"10.1111/jtsa.12728","DOIUrl":"10.1111/jtsa.12728","url":null,"abstract":"<p>We consider network autoregressive models for count data with a non-random neighborhood structure. The main methodological contribution is the development of conditions that guarantee stability and valid statistical inference for such models. We consider both cases of fixed and increasing network dimension and we show that quasi-likelihood inference provides consistent and asymptotically normally distributed estimators. The article is complemented by simulation results and a data example.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12728","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138825453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
High-Frequency-Based Volatility Model with Network Structure 基于网络结构的高频波动率模型
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-12-03 DOI: 10.1111/jtsa.12726
Huiling Yuan, Kexin Lu, Guodong Li, Junhui Wang
{"title":"High-Frequency-Based Volatility Model with Network Structure","authors":"Huiling Yuan,&nbsp;Kexin Lu,&nbsp;Guodong Li,&nbsp;Junhui Wang","doi":"10.1111/jtsa.12726","DOIUrl":"10.1111/jtsa.12726","url":null,"abstract":"<p>This paper introduces a novel multi-variate volatility model that can accommodate appropriately defined network structures based on low-frequency and high-frequency data. The model offers substantial reductions in the number of unknown parameters and computational complexity. The model formulation, along with iterative multi-step-ahead forecasting and targeting parameterization are discussed. Quasi-likelihood functions for parameter estimation are proposed and their asymptotic properties are established. A series of simulation studies are carried out to assess the performance of parameter estimation in finite samples. Furthermore, a real data analysis demonstrates that the proposed model outperforms the existing volatility models in prediction of future variances of daily return and realized measures.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12726","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138539083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Asymptotic Normality of Bias Reduction Estimation for Jump Intensity Function in Financial Markets 金融市场跳跃强度函数的渐近正态性估计
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-11-14 DOI: 10.1111/jtsa.12727
Yuping Song, Min Zhu, Jiawei Qiu
{"title":"Asymptotic Normality of Bias Reduction Estimation for Jump Intensity Function in Financial Markets","authors":"Yuping Song,&nbsp;Min Zhu,&nbsp;Jiawei Qiu","doi":"10.1111/jtsa.12727","DOIUrl":"10.1111/jtsa.12727","url":null,"abstract":"<p>Continuous-time diffusion models with jumps, especially the jump intensity coefficient, can depict the impact of sudden and large shocks to financial markets. It is possible to disentangle, from the discrete observations, the contributions given by the jumps and those by the diffusion part through threshold functions. Based on this threshold technique, we employ non-parametric local linear threshold estimator for the unknown jump intensity function of a semimartingale with jumps. The asymptotic normality of our estimator is provided in the presence of finite activity jumps under certain regular conditions. The finite-sample performance for the underlying estimator has been shown through a Monte Carlo experiment and an empirical analysis on high frequency returns of indexes in the USA and China.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138543575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Non-crossing quantile double-autoregression for the analysis of streaming time series data 用于分析流式时间序列数据的非交叉量级双自回归
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-10-11 DOI: 10.1111/jtsa.12725
Rong Jiang, Siu Kai Choy, Keming Yu
{"title":"Non-crossing quantile double-autoregression for the analysis of streaming time series data","authors":"Rong Jiang,&nbsp;Siu Kai Choy,&nbsp;Keming Yu","doi":"10.1111/jtsa.12725","DOIUrl":"10.1111/jtsa.12725","url":null,"abstract":"<p>Many financial time series not only have varying structures at different quantile levels and exhibit the phenomenon of conditional heteroscedasticity at the same time but also arrive in the stream. Quantile double-autoregression is very useful for time series analysis but faces challenges with model fitting of streaming data sets when estimating other quantiles in subsequent batches. This article proposes a renewable estimation method for quantile double-autoregression analysis of streaming time series data due to its ability to break with storage barrier and computational barrier. Moreover, the proposed flexible parametric structure of the quantile function enables us to predict any interested quantile value without quantile curve crossing problem or keeping the desirable monotone property of the conditional quantile function. The proposed methods are illustrated using current data and the summary statistics of historical data. Theoretically, the proposed statistic is shown to have the same asymptotic distribution as the standard version computed on an entire data stream with the data batches pooled into one data set, without additional condition. Simulation studies and an empirical example are presented to illustrate the finite sample performance of the proposed methods.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12725","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136209908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Test of change point versus long-range dependence in functional time series 检验功能时间序列中的变化点与长程依赖性
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-09-20 DOI: 10.1111/jtsa.12723
Changryong Baek, Piotr Kokoszka, Xiangdong Meng
{"title":"Test of change point versus long-range dependence in functional time series","authors":"Changryong Baek,&nbsp;Piotr Kokoszka,&nbsp;Xiangdong Meng","doi":"10.1111/jtsa.12723","DOIUrl":"10.1111/jtsa.12723","url":null,"abstract":"<p>In the context of functional time series, we propose a significance test to distinguish between short memory with a change point and long range dependence. The test is based on coefficients of projections onto an optimal direction that captures the dependence structure of the latent stationary functions that are not observable due to a potential change point. The optimal direction must be estimated as well. The test statistic is constructed using the local Whittle estimator applied to these coefficients. It has standard normal distribution under the null hypothesis (change point) and diverges to infinity under the alternative (long range dependence). The article includes asymptotic theory, a simulation study and an application to curve-valued time series derived from intraday asset prices.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136375945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multiple change point detection under serial dependence: Wild contrast maximisation and gappy Schwarz algorithm 序列依赖下的多变化点检测：野性对比度最大化和加普-施瓦茨算法
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-09-18 DOI: 10.1111/jtsa.12722
Haeran Cho, Piotr Fryzlewicz
{"title":"Multiple change point detection under serial dependence: Wild contrast maximisation and gappy Schwarz algorithm","authors":"Haeran Cho,&nbsp;Piotr Fryzlewicz","doi":"10.1111/jtsa.12722","DOIUrl":"10.1111/jtsa.12722","url":null,"abstract":"<p>We propose a methodology for detecting multiple change points in the mean of an otherwise stationary, autocorrelated, linear time series. It combines solution path generation based on the wild contrast maximisation principle, and an information criterion-based model selection strategy termed gappy Schwarz algorithm. The former is well-suited to separating shifts in the mean from fluctuations due to serial correlations, while the latter simultaneously estimates the dependence structure and the number of change points without performing the difficult task of estimating the level of the noise as quantified e.g. by the long-run variance. We provide modular investigation into their theoretical properties and show that the combined methodology, named WCM.gSa, achieves consistency in estimating both the total number and the locations of the change points. The good performance of WCM.gSa is demonstrated via extensive simulation studies, and we further illustrate its usefulness by applying the methodology to London air quality data.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12722","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135207798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Editorial announcement: Journal of Time Series Analysis Distinguished Authors 2023 编辑公告：时间序列分析期刊》2023 年杰出作者
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-09-17 DOI: 10.1111/jtsa.12724
Robert Taylor
{"title":"Editorial announcement: Journal of Time Series Analysis Distinguished Authors 2023","authors":"Robert Taylor","doi":"10.1111/jtsa.12724","DOIUrl":"10.1111/jtsa.12724","url":null,"abstract":"<p>In recognition of authors who have made significant contributions to this Journal, the <i>Journal of Time Series Analysis</i> runs a scheme to honour those authors by naming them as a <i>Journal of Time Series Analysis Distinguished Author</i>. The qualifying criterion for this award is 3.5 points where authors are awarded 1 point for each single-authored article, ½ point for each double-authored article, 1/3 point for each triple-authored article, and so on, that they have published in the <i>Journal of Time Series Analysis</i> since its inception. Distinguished Authors are entitled to a 1-year free on-line subscription to the Journal to mark the award, and will also receive a certificate commemorating the award.</p><p>In addition to the lists of Distinguished Authors announced previously in Volume 41 issue 4 (July 2020), Volume 42 Issue 1 (January 2021), Volume 43 Issue 1 (January 2022), and Volume 44 Issue 1 (January 2023), the <i>Journal of Time Series Analysis</i> is very pleased to welcome</p><p><b>Suhasini Subba Rao</b></p><p>to the list of <i>Journal of Time Series Analysis Distinguished Authors</i> for 2023 based on her publications in the Journal appearing up to and including Volume 44 Issues 5–6 (September–November 2023).</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12724","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135259542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Smooth transition moving average models: Estimation, testing, and computation 平滑过渡移动平均模型：估计、测试和计算
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-09-07 DOI: 10.1111/jtsa.12721
Xinyu Zhang, Dong Li
{"title":"Smooth transition moving average models: Estimation, testing, and computation","authors":"Xinyu Zhang,&nbsp;Dong Li","doi":"10.1111/jtsa.12721","DOIUrl":"10.1111/jtsa.12721","url":null,"abstract":"<p>The article introduces a new subclass of nonlinear moving average model, called the smooth transition moving average (STMA) model, and studies its probabilistic properties. It is shown that, under some mild conditions, the least squares estimation (LSE) is strongly consistent and asymptotically normal. A powerful score-based goodness-of-fit test for the STMA model is presented. A different parametrization from the classical one is applied to numerically improve the identification and estimation of this model. Simulation studies are conducted to assess the performance of the LSE and the score-based test in finite samples. The results are illustrated with an application to the weekly exchange rate of the USA Dollar to the British Pound.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44267129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Local Whittle estimation with (quasi-)analytic wavelets (拟)解析小波的局部Whittle估计
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2023-09-04 DOI: 10.1111/jtsa.12719
Sophie Achard, Irène Gannaz
{"title":"Local Whittle estimation with (quasi-)analytic wavelets","authors":"Sophie Achard,&nbsp;Irène Gannaz","doi":"10.1111/jtsa.12719","DOIUrl":"10.1111/jtsa.12719","url":null,"abstract":"<p>In the general setting of long-memory multivariate time series, the long-memory characteristics are defined by two components. The long-memory parameters describe the autocorrelation of each time series. And the long-run covariance measures the coupling between time series, with general phase parameters. It is of interest to estimate the long-memory, long-run covariance and general phase parameters of time series generated by this wide class of models although they are not necessarily Gaussian nor stationary. This estimation is thus not directly possible using real wavelets decomposition or Fourier analysis. Our purpose is to define an inference approach based on a representation using quasi-analytic wavelets. We first show that the covariance of the wavelet coefficients provides an adequate estimator of the covariance structure including the phase term. Consistent estimators based on a local Whittle approximation are then proposed. Simulations highlight a satisfactory behavior of the estimation on finite samples on multivariate fractional Brownian motions. An application on a real neuroscience dataset is presented, where long-memory and brain connectivity are inferred.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12719","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42534099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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