Journal of Time Series Analysis最新文献

{"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":"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":"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}

{"title":"Speculative Bubbles in the Recent AI Boom: Nasdaq and the Magnificent Seven","authors":"Rerotlhe B. Basele,&nbsp;Peter C. B. Phillips,&nbsp;Shuping Shi","doi":"10.1111/jtsa.12835","DOIUrl":"https://doi.org/10.1111/jtsa.12835","url":null,"abstract":"<div>\u0000 \u0000 <p>The recent artificial intelligence (AI) boom covers a period of rapid innovation and wide adoption of AI intelligence technologies across diverse industries. These developments have fueled an unprecedented frenzy in the Nasdaq, with AI-focused companies experiencing soaring stock prices that raise concerns about speculative bubbles and real-economy consequences. Against this background, this study investigates the formation of speculative bubbles in the Nasdaq stock market with a specific focus on the so-called Magnificent Seven (Mag-7) individual stocks during the AI boom, spanning the period from January 2017 to January 2025. We apply the real-time PSY bubble detection methodology of Phillips et al. (2015a, 2015b) while controlling for market and industry factors for individual stocks. Confidence intervals to assess the degree of speculative behavior in asset price dynamics are calculated using the near-unit root approach of Phillips (2023). The findings reveal the presence of speculative bubbles in the Nasdaq stock market and across all Mag-7 stocks. Nvidia and Microsoft experience the longest speculative periods over January 2017–December 2021, while Nvidia and Tesla show the fastest rates of explosive behavior. Speculative bubbles persist in the market and in six of the seven stocks (excluding Apple) from December 2022 to January 2025. Near-unit-root inference indicates mildly explosive dynamics for Nvidia and Tesla (2017–2021) and local-to-unity near explosive behavior for all assets in both periods.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 5","pages":"814-828"},"PeriodicalIF":1.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767966","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":"Editorial Announcement","authors":"Robert Taylor","doi":"10.1111/jtsa.12829","DOIUrl":"https://doi.org/10.1111/jtsa.12829","url":null,"abstract":"<p>I am delighted to welcome Shuping Shi to the editorial board of the <i>Journal of Time Series Analysis</i>. Shuping joins as an Associate Editor with effect from 1st March 2025.</p><p>Shuping Shi is a Professor in the Department of Economics at Macquarie University, Australia. She specialises in Financial Econometrics, Time Series Analysis, and Applied Economics, with expertise in bubble detection, non-stationary (explosive) processes, intraday high-frequency drift detection, long memory and rough volatility models, and time-varying Granger causality tests. She received the 2020 <i>Discovery Early Career Researcher Award</i> from the Australian Research Council and was honored with the prestigious <i>2022 Young Economist Award</i> by the Economic Society of Australia. Her research has been published in journals, including <i>Review of Financial Studies</i>, <i>Journal of Econometrics</i>, <i>Management Science</i>, <i>International Economic Review</i>, and <i>Econometric Theory</i>. She has been recognized among the top 2% most-cited economists globally in the latest annual report published by Standard University for 2024.</p><p>The author declares no conflicts of interest.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 3","pages":"401"},"PeriodicalIF":1.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12829","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143801511","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 Monitoring for Changes in GARCH(1,1) Models Without Assuming Stationarity","authors":"Lajos Horváth,&nbsp;Lorenzo Trapani,&nbsp;Shixuan Wang","doi":"10.1111/jtsa.12824","DOIUrl":"https://doi.org/10.1111/jtsa.12824","url":null,"abstract":"<p>In this article, we develop two families of sequential monitoring procedure to (timely) detect changes in the parameters of a GARCH(1,1) model. Our statistics can be applied irrespective of whether the historical sample is stationary or not, and indeed without previous knowledge of the regime of the observations before and after the break. In particular, we construct our detectors as the CUSUM process of the quasi-Fisher scores of the log likelihood function. To ensure timely detection, we then construct our boundary function (exceeding which would indicate a break) by including a weighting sequence which is designed to shorten the detection delay in the presence of a changepoint. We consider two types of weights: a lighter set of weights, which ensures timely detection in the presence of changes occurring “early, but not too early” after the end of the historical sample; and a heavier set of weights, called “Rényi weights” which is designed to ensure timely detection in the presence of changepoints occurring very early in the monitoring horizon. In both cases, we derive the limiting distribution of the detection delays, indicating the expected delay for each set of weights. Our methodologies can be applied for a general analysis of changepoints in GARCH(1,1) sequences; however, they can also be applied to detect changes from stationarity to explosivity or vice versa, thus allowing to check for “volatility bubbles”, upon applying tests for stationarity before and after the identified break. Our theoretical results are validated via a comprehensive set of simulations, and an empirical application to daily returns of individual stocks.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 5","pages":"981-996"},"PeriodicalIF":1.0,"publicationDate":"2025-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12824","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767906","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":"High-Frequency Instruments and Identification-Robust Inference for Stochastic Volatility Models","authors":"Md. Nazmul Ahsan,&nbsp;Jean-Marie Dufour","doi":"10.1111/jtsa.12812","DOIUrl":"https://doi.org/10.1111/jtsa.12812","url":null,"abstract":"<p>We introduce a novel class of stochastic volatility models, which can utilize and relate many high-frequency realized volatility (RV) measures to latent volatility. Instrumental variable methods provide a unified framework for estimation and testing. We study parameter inference problems in the proposed framework with nonstationary stochastic volatility and exogenous predictors in the latent volatility process. Identification-robust methods are developed for a joint hypothesis involving the volatility persistence parameter and the autocorrelation parameter of the composite error (or the noise ratio). For inference about the volatility persistence parameter, projection techniques are applied. The proposed tests include Anderson-Rubin-type tests and their point-optimal versions. For distributional theory, we provide finite-sample tests and confidence sets for Gaussian errors, establish exact Monte Carlo test procedures for non-Gaussian errors (possibly heavy-tailed), and show asymptotic validity under weaker assumptions. Simulation results show that the proposed tests outperform the asymptotic test regarding size and exhibit excellent power in empirically realistic settings. The proposed inference methods are applied to IBM's price and option data (2009–2013). We consider 175 different instruments (IVs) spanning 22 classes and analyze their ability to describe the low-frequency volatility. IVs are compared based on the average length of the proposed identification-robust confidence intervals. The superior instrument set mostly comprises 5-min HF realized measures, and these IVs produce confidence sets which show that the volatility process is nearly unit-root. In addition, we find RVs with higher frequency yield wider confidence intervals than RVs with slightly lower frequency, indicating that these confidence intervals adjust to absorb market microstructure noise. Furthermore, when we consider irrelevant or weak IVs (jumps and signed jumps), the proposed tests produce unbounded confidence intervals. We also find that both RV and BV measures produce almost identical confidence intervals across all 14 subclasses, confirming that our methodology is robust in the presence of jumps. Finally, although jumps contain little information regarding the low-frequency volatility, we find evidence that there may be a nonlinear relationship between jumps and low-frequency volatility.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 2","pages":"216-234"},"PeriodicalIF":1.2,"publicationDate":"2025-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12812","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143252359","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: Journal of Time Series Analysis Distinguished Authors 2024","authors":"Robert Taylor","doi":"10.1111/jtsa.12816","DOIUrl":"https://doi.org/10.1111/jtsa.12816","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, 1/2 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 one-year free online subscription to the Journal to mark the award. They 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), Volume 44 Issue 1 (January 2023), and Volume 45 Issue 1 (January 2024), the <i>Journal of Time Series Analysis</i> is very pleased to welcome <b>Konstantinos Fokianos</b> to the list of <i>Journal of Time Series Analysis Distinguished Authors</i> for 2024, based on his publications in the Journal appearing up to and including Volume 45 Issue 6 (November 2024).</p><p>The author declares no conflicts of interest.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 2","pages":"213"},"PeriodicalIF":1.2,"publicationDate":"2025-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12816","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143252357","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}