Journal of Time Series Analysis最新文献

{"title":"Special Issue in Honour of Stephen J. Taylor: Guest Editors' Introduction","authors":"Torben G. Andersen, Kim Christensen, Ingmar Nolte","doi":"10.1111/jtsa.70014","DOIUrl":"https://doi.org/10.1111/jtsa.70014","url":null,"abstract":"<p>Stephen John Taylor was born in England in 1954 and dedicated his career to research in Financial Econometrics. He obtained an MA in Mathematics from Trinity College, University of Cambridge, UK, an MA and a PhD in Operational Research from Lancaster University, UK, in the 1970s. From 1977 until his emeritation in 2020, as Professor of Finance in the Accounting and Finance Department at Lancaster University, he held positions as Lecturer in Operational Research (1977–88), Lecturer in Finance (1988-89), Reader in Finance (1989–93) and Professor of Finance (1993–2020) at Lancaster. Stephen is a key authority in the area of Time Series Econometrics, especially regarding Stochastic Volatility and Option Pricing modelling. He has published more than 60 papers in the broader areas of Finance and Econometrics including in top journals such as the <i>Journal of Econometrics, Journal of Financial & Quantitative Analysis</i> and <i>Journal of Financial Econometrics</i>. Stephen has been cited extensively with more than 14,000 google-scholar citations as of 2025, and he has contributed to the careers of over 20 PhD students and numerous co-authors.</p><p>Stephen was one of the very first contributors to the European Finance Association and a founding member of the Society of Financial Econometrics. His work has inspired generations of scholars in the area, and he is referenced in the Engle and Granger 2003 Nobel Prize review. His Taylor (<span>1982</span>) paper, introducing stochastic volatility models, is arguably his most prominent work, and it has been re-printed three times. Stephen's influential books <i>Modelling Financial Time Series</i> (<span>1986</span>) and <i>Asset Pric Dynamics, Volatility and Prediction</i> (<span>2011</span>) have been adopted globally over the last few decades as key readings for courses in Time Series Analysis and Financial Econometrics at top universities and thereby shaped the field by teaching generations of students.</p><p>The Centre for Financial Econometrics, Asset Markets and Macroeconomic Policy at Lancaster University, UK, hosted a Financial Econometrics Conference to mark Stephen Taylor's Retirement in 2023 with over 100 international participants coming together to celebrate Stephen's career and contributions. We are honoured to serve as guest editors for this special issue in the <i>Journal of Time Series Analysis</i> dedicated to Stephen Taylor with papers solicited from the conference submissions and then undergoing the journal's rigorous review process. This special issue contains eight papers on the latest topics in Time Series Econometrics building on and reflecting on Stephen's earlier work in the area and one paper by Stephen himself on his latest work concerning market microstructure noise components.</p><p>Stephen's paper focuses on the differential impact of discreteness versus other (residual) components of microstructure noise (MN) and how to draw inference about their size and statistical pr","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 6","pages":"1029-1031"},"PeriodicalIF":1.0,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.70014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145284538","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}

{"title":"Editorial Announcement","authors":"Robert Taylor","doi":"10.1111/jtsa.70008","DOIUrl":"https://doi.org/10.1111/jtsa.70008","url":null,"abstract":"<p>I am delighted to welcome Dr Ke-Li Xu to the editorial board of the Journal of Time Series Analysis. Ke-Li joins as an Associate Editor with effect from 1^st July 2025.</p><p>Ke-Li obtained his PhD from Yale University in 2007 and is currently Professor of Economics at Indiana University Bloomington, a position he has held since 2021. The main theme of his research is to design statistical estimation and inference methods for economic models that accommodate features such as endogeneity, nonlinearity, heterogeneity, and persistence, without imposing strong constraints on the underlying data generating process. Before joining Indiana University, Ke-Li held positions at Texas A&amp;M University and at the University of Alberta, Canada. Ke-Li is a Fellow of the <i>Journal of Econometrics</i> and a recipient of the <i>Multa Scripsit</i> Award from <i>Econometric Theory</i>. He is currently an Associate Editor of the <i>Journal of Business and Economic Statistics</i> and of <i>Econometric Reviews</i>. He also served as a Panelist for the National Science Foundation (NSF), Economics Program.</p><p>The author declares no conflicts of interest.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 5","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767409","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}

{"title":"Spatiotemporal Heterogeneity Learning: Generalized SpatioTemporal Semi-Varying Coefficient Models With Structure Identification","authors":"Zhiling Gu,&nbsp;Xinyi Li,&nbsp;Guannan Wang,&nbsp;Lily Wang","doi":"10.1111/jtsa.70002","DOIUrl":"10.1111/jtsa.70002","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper proposes a class of Generalized SpatioTemporal Semi-Varying Coefficient Models (GST-SVCMs) with structure identification to enhance the detection and interpretation of spatiotemporal heterogeneity in factors influencing response variables. The proposed framework effectively distinguishes between spatiotemporally varying and constant effects, addressing a key limitation of current modeling approaches. By identifying and separating these components, the GST-SVCM structure identification method improves both computational efficiency and the statistical power of downstream analyses. The estimators of constant coefficients and varying coefficient functions are consistent, and the estimators of the constant coefficients are asymptotically normal, facilitating reliable statistical inference. Extensive Monte Carlo simulations demonstrate that the proposed method accurately identifies the true model structure and significantly improves prediction accuracy compared to purely varying coefficient models that do not incorporate structure identification. To further refine model granularity, we extend GST-SVCMs by introducing the Hierarchical Spatiotemporal Varying Coefficient Model (HSTVCM) with automatic structure identification, which decomposes effects into spatial, temporal, and spatiotemporal components for more precise structure identification. The practical utility of the proposed methodologies is validated through an application to particulate matter (PM) data, providing insights into the influence of meteorological factors on PM levels and determining whether these effects exhibit true spatiotemporal variation.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 1","pages":"8-24"},"PeriodicalIF":1.0,"publicationDate":"2025-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145543629","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}

{"title":"Decoupling Interday and Intraday Volatility Dynamics With Price Durations","authors":"Yifan Li,&nbsp;Ingmar Nolte,&nbsp;Sandra Nolte,&nbsp;Shifan Yu","doi":"10.1111/jtsa.12849","DOIUrl":"https://doi.org/10.1111/jtsa.12849","url":null,"abstract":"<p>This article introduces a novel framework for volatility estimation based on price durations with an adaptive price change threshold. This innovation allows us to disentangle daily and intraday volatility dynamics from price durations, which greatly simplifies the parametric modelling of price durations and hence leads to more accurate volatility estimators. Simulation results demonstrate superior finite-sample performance of our duration-based estimators for both spot and integrated volatility compared with some established methods. An empirical application based on intraday data for the SPDR S&amp;P 500 ETF highlights the improved forecasting accuracy of our integrated volatility estimator within a standard daily volatility forecasting framework. Furthermore, an intraday analysis of spot volatility estimation shows that our method can capture the immediate and substantial impact of FOMC news announcements on market volatility.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 6","pages":"1224-1250"},"PeriodicalIF":1.0,"publicationDate":"2025-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12849","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145284969","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}

{"title":"The Dynamic, the Static, and the Weak: Factor Models and the Analysis of High-Dimensional Time Series","authors":"Matteo Barigozzi,&nbsp;Marc Hallin","doi":"10.1111/jtsa.12837","DOIUrl":"https://doi.org/10.1111/jtsa.12837","url":null,"abstract":"<div>\u0000 \u0000 <p>Several fundamental and closely interconnected issues related to factor models are reviewed and discussed: dynamic versus static loadings, rate-strong versus rate-weak factors, the concept of weakly common component recently introduced by Gersing, the irrelevance of cross-sectional ordering and the assumption of cross-sectional exchangeability, the impact of undetected strong factors, and the problem of combining common and idiosyncratic forecasts. Conclusions all point to the advantages of the General Dynamic Factor Model approach of Forni, Hallin, Lippi, and Reichlin over the widely used Static Approximate Factor Model introduced by Chamberlain and Rothschild.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 1","pages":"201-219"},"PeriodicalIF":1.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145706455","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}

{"title":"Recent Developments in Time-Series Methods for Detecting Bubbles and Crashes: Guest Editors' Introduction","authors":"David I. Harvey,&nbsp;Stephen J. Leybourne","doi":"10.1111/jtsa.70003","DOIUrl":"https://doi.org/10.1111/jtsa.70003","url":null,"abstract":"<p>Financial and economic bubbles, along with their inevitable crashes, can have a significant impact on investment outcomes and a country's macroeconomic performance. From historical speculative behavior to modern asset price surges, identifying and measuring these phenomena has posed a crucial challenge for investors and policymakers alike. Examples such as the Dot-Com bubble that originated in the mid-1990s, the US housing market bubble of the late 1990s and early 2000s, and the Bitcoin price bubbles since the mid-2010s, underscore the need for robust econometric methods to detect the presence and timing of bubble behavior, either historically or in real time.</p><p>Time series modeling of bubble processes originated with methods to detect explosive behavior in asset prices. Diba and Grossman (<span>1988</span>) proposed using left-tailed unit root tests applied to the levels and first differences of prices, recognizing that differences of bubble processes are not stationary. In a now seminal paper on bubble testing, Phillips et al. (<span>2011</span>) [PWY] proposed a more direct approach, basing their test on recursive right-tailed unit root tests, with the alternative of explosive autoregression acting as a model of the bubble dynamics. Phillips et al. (<span>2015</span>) [PSY] extended the PWY approach to a doubly recursive test, which improves performance in a setting of multiple bubbles and crashes. In addition to methods of bubble detection, PWY and PSY also introduced techniques for dating the timing of a bubble's onset and collapse.</p><p>Building on the initial work of PWY and PSY, a rich literature has evolved in the area of detecting and dating bubbles and crashes, with developments in both econometric methodology and breadth of empirical application. Methodological contributions over the last ten years have been made in a variety of directions; these include, inter alia, alternative approaches to improve test power, methods to increase the accuracy of bubble and crash date estimates, procedures to robustify inference to features such as heteroskedasticity and/or jumps, and techniques to allow rapid detection of bubbles and crashes in real time. Applications have been made to a wide range of series, covering stocks, housing, metals, agricultural commodities, energy and cryptocurrencies, among others. The papers in this Special Issue continue the development of this important research area, and provide contributions to both methodology and empirical analysis.</p><p>We are very grateful to all of the authors who have contributed to this special issue. We would also like to thank the anonymous reviewers for their detailed comments and helpful feedback on each paper. Special thanks go to Robert Taylor, Editor-in-Chief of the journal, for commissioning the special issue, as well as to Priscilla Goldby for her invaluable help throughout the editorial process.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 5","pages":"811-813"},"PeriodicalIF":1.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.70003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144768053","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}

{"title":"A Novel Test for the Presence of Local Explosive Dynamics","authors":"F. Blasques,&nbsp;S. J. Koopman,&nbsp;G. Mingoli,&nbsp;S. Telg","doi":"10.1111/jtsa.70001","DOIUrl":"https://doi.org/10.1111/jtsa.70001","url":null,"abstract":"<p>In economics and finance, speculative bubbles take the form of locally explosive dynamics that eventually collapse. We propose a test for the presence of speculative bubbles in the context of mixed causal-noncausal autoregressive processes. The test exploits the fact that bubbles are anticipative, that is, they are generated by an extreme shock in the forward-looking dynamics. In particular, the test uses both path-level deviations and growth rates to assess the presence of a bubble of a given duration and size, at any moment in time. We show that the distribution of the test statistic can be either analytically determined or numerically approximated, depending on the error distribution. Size and power properties of the test are analyzed in controlled Monte Carlo experiments. An empirical application is presented for a monthly oil price index. It demonstrates the ability of the test to detect bubbles and to provide valuable insights in terms of risk assessments in the spirit of Value-at-Risk.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 5","pages":"966-980"},"PeriodicalIF":1.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144768121","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}

{"title":"Estimation and Inference for Higher-Order Stochastic Volatility Models With Leverage","authors":"Md. Nazmul Ahsan,&nbsp;Jean-Marie Dufour,&nbsp;Gabriel Rodriguez-Rondon","doi":"10.1111/jtsa.12851","DOIUrl":"https://doi.org/10.1111/jtsa.12851","url":null,"abstract":"<p>Statistical inference—estimation and testing—for stochastic volatility models is challenging and computationally expensive. This problem is compounded when leverage effects are allowed. We propose efficient, simple estimators for higher-order stochastic volatility models with leverage [SVL<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mi>p</mi>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 <annotation>$$ (p) $$</annotation>\u0000 </semantics></math>], based on a small number of moment equations derived from ARMA representations associated with SVL models, along with the possibility of using “winsorization” to improve stability and efficiency (W-ARMA estimators). The asymptotic distributional theory of the estimators is derived. The computationally simple estimators proposed allow one to easily perform simulation-based (possibly exact) tests, such as Monte Carlo tests (MCTS) or bootstrap procedures. In simulation experiments, we show that: (1) the proposed W-ARMA estimators dominate alternative estimators (including Bayesian estimators) in terms of bias, root-mean-square error, and computation time; (2) local and maximized Monte Carlo tests based on W-ARMA estimators yield good control of the size and power of LR-type tests; (3) taking into account leverage improves volatility forecasting. The methods developed are applied to daily returns for three major stock indices (S&amp;P 500, Dow Jones, Nasdaq), confirming the superiority of SVL<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mi>p</mi>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 <annotation>$$ (p) $$</annotation>\u0000 </semantics></math> models over competing conditional volatility models in terms of forecast accuracy.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 6","pages":"1064-1084"},"PeriodicalIF":1.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12851","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145284653","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}

{"title":"Editorial Announcement: Addendum to Journal of Time Series Analysis Distinguished Authors 2023","authors":"Robert Taylor","doi":"10.1111/jtsa.12854","DOIUrl":"https://doi.org/10.1111/jtsa.12854","url":null,"abstract":"<p>In addition to the list of Distinguished Authors announced in Volume 45 Issue 1 (January 2024), the <i>Journal of Time Series Analysis</i> is very pleased to welcome <b>Robert Lund</b> to the list of <i>Journal of Time Series Analysis Distinguished Authors</i> for 2023 based on his publications in the Journal appearing up to and including Volume 44, Issues 5–6 (September–November 2023).</p><p>We apologise to Robert for his omission from the original list which was due to an administrative error.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 5","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12854","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767840","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}

{"title":"Sequential Detector Statistics for Speculative Bubbles","authors":"Jörg Breitung,&nbsp;Max Diegel","doi":"10.1111/jtsa.12845","DOIUrl":"https://doi.org/10.1111/jtsa.12845","url":null,"abstract":"<p>We propose a heteroskedasticity-robust locally best invariant (LBI) statistic to test the hypothesis of a unit root against the alternative of an explosive root associated with speculative bubbles. Compared to existing alternatives such as Dickey-Fuller type tests, the LBI statistic has a standard limiting distribution and greater power, particularly in the empirically relevant scenario of a moderately explosive root. Further refinements, such as the point-optimal linear test, approach the power envelope remarkably closely. To detect bubbles with an unknown starting date, we consider sequential (CUSUM) schemes based on constant and time-varying boundary functions, where the exponentially weighted CUSUM detector with a constant boundary function turns out to be most powerful. We also propose a simple method for date-stamping the start of the bubble consistently. Finally, we illustrate our methods using two empirical examples.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 5","pages":"829-845"},"PeriodicalIF":1.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12845","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767365","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}