Journal of Time Series Analysis最新文献

{"title":"Testing Mean Stability of Heteroskedastic Time Series","authors":"Violetta Dalla,&nbsp;Liudas Giraitis,&nbsp;Peter C. B. Phillips","doi":"10.1111/jtsa.12840","DOIUrl":"https://doi.org/10.1111/jtsa.12840","url":null,"abstract":"<p>Time series models are often fitted to the data without preliminary checks for stability of the mean and variance, conditions that may not hold in much economic and financial data, particularly over long periods. Ignoring such shifts may result in fitting models with spurious dynamics that lead to unsupported and controversial conclusions about time dependence, causality, and the effects of unanticipated shocks. In spite of what may seem as obvious differences between a time series of independent variates with changing variance and a stationary conditionally heteroskedastic (GARCH) process, such processes may be hard to distinguish in applied work using basic time series diagnostic tools. We develop and study some practical and easily implemented statistical procedures to test the mean and variance stability of uncorrelated and serially dependent time series. Application of the new methods to analyze the volatility properties of stock market returns leads to some unexpectedly surprising findings concerning the advantages of modeling time-varying changes in unconditional variance.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 1","pages":"182-200"},"PeriodicalIF":1.0,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12840","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145706456","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":"Inverse Autocovariance Estimates","authors":"Jiang Wang,&nbsp;Dimitris N. Politis","doi":"10.1111/jtsa.12832","DOIUrl":"https://doi.org/10.1111/jtsa.12832","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;p&gt;The notion of the inverse autocovariance function (iacf) for stationary time series was introduced by W. Cleveland in the 1970s who proposed two ways to estimate it: one way is to fit an autoregressive (AR) model to the data and use the fitted model's inverse autocovariance as the iacf estimator, and the other method is via a kernel-smoothed spectral density estimator. Consistency of the iacf estimator at a fixed lag was subsequently proved by R.J. Bhansali in the 1980s based on a linear time series condition. In this article, we relax the linearity assumption and provide sufficient conditions for the consistency of the iacf estimator. We further consider the problem of estimating the vector consisting of the iacf at lags up to &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;n&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ n $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, based on a sample of size &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;n&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ n $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;. We propose several competing estimators of the iacf vector and study their convergence. In addition, we discuss the difficult problem of choosing the order &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;p&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ p $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; of a fitted AR model, and provide some alternative ways to approach it. Finally, we consider the inverse autocovariance matrix, i.e., the &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;n&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ n $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; by &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;n&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ n $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; Toeplitz matrix with &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;i&lt;/mi&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mi&gt;j&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ i,j $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; element given by the iacf at lag &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;i&lt;/mi&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mi&gt;j&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ i-j $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;; we propose an estimator and investigate its consistency properties. Numerical simulations illustrate the finite sample performance of all iacf estimators, including the estimators of the order &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;p&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ p $$&lt;/a","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 1","pages":"233-249"},"PeriodicalIF":1.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145706296","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":"Blockwise Empirical Likelihood and Efficiency for Markov Chains","authors":"Ursula U. Müller,&nbsp;Anton Schick,&nbsp;Wolfgang Wefelmeyer","doi":"10.1111/jtsa.12825","DOIUrl":"https://doi.org/10.1111/jtsa.12825","url":null,"abstract":"<div>\u0000 \u0000 <p>Suppose we observe an ergodic Markov chain on an arbitrary state space. The usual nonparametric estimator of a linear functional of the stationary distribution is the empirical estimator. If the stationary distribution obeys finitely many known linear constraints, we can improve the empirical estimator by empirical likelihood weights. Since the observations are dependent, an optimal choice of weights is determined by weighting averages over disjoint blocks of observations with slowly increasing length. We show that the improved empirical estimator is efficient. We also introduce two additively corrected empirical estimators that are asymptotically equivalent to the weighted empirical estimator, hence also efficient.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 1","pages":"174-181"},"PeriodicalIF":1.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145706413","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":"Gaussian Approximation for Lag-Window Estimators and the Construction of Confidence Bands for the Spectral Density","authors":"Jens-Peter Kreiß,&nbsp;Anne Leucht,&nbsp;Efstathios Paparoditis","doi":"10.1111/jtsa.12826","DOIUrl":"https://doi.org/10.1111/jtsa.12826","url":null,"abstract":"<div>\u0000 \u0000 <p>In this article, we consider the construction of simultaneous confidence bands for the spectral density of a stationary time series using a Gaussian approximation for classical lag-window spectral density estimators evaluated at the set of all positive Fourier frequencies. The Gaussian approximation opens up the possibility to verify asymptotic validity of a multiplier bootstrap procedure and, even further, to derive the corresponding rate of convergence. A small simulation study sheds light on the finite sample properties of this bootstrap proposal.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 1","pages":"90-105"},"PeriodicalIF":1.0,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145706404","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":"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":"On the Optimal Prediction of Extreme Events in Heavy-Tailed Time Series With Applications to Solar Flare Forecasting","authors":"Victor Verma,&nbsp;Stilian Stoev,&nbsp;Yang Chen","doi":"10.1111/jtsa.12820","DOIUrl":"https://doi.org/10.1111/jtsa.12820","url":null,"abstract":"<p>The prediction of extreme events in time series is a fundamental problem arising in many financial, scientific, engineering, and other applications. We begin by establishing a general Neyman–Pearson-type characterization of optimal extreme event predictors in terms of density ratios. This yields new insights and several closed-form optimal extreme event predictors for additive models. These results naturally extend to time series, where we study optimal extreme event prediction for both light- and heavy-tailed autoregressive and moving average models. Using a uniform law of large numbers for ergodic time series, we establish the asymptotic optimality of an empirical version of the optimal predictor for autoregressive models. Using multivariate regular variation, we obtain an expression for the optimal extremal precision in heavy-tailed infinite moving averages, which provides theoretical bounds on the ability to predict extremes in this general class of models. We address the important problem of predicting solar flares by applying our theory and methodology to a state-of-the-art time series consisting of solar soft x-ray flux measurements. Our results demonstrate the success and limitations in solar flare forecasting of long-memory autoregressive models and long-range-dependent, heavy-tailed FARIMA models.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 1","pages":"25-42"},"PeriodicalIF":1.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12820","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145706470","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":"Event-Day Options","authors":"Jonathan H. Wright","doi":"10.1111/jtsa.12819","DOIUrl":"https://doi.org/10.1111/jtsa.12819","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper considers new options on Treasury futures than expire each Wednesday and Friday. I examine the variances implied by these options as of the night before expiration, and compare the variances just before FOMC days and employment report days with the variances on other Tuesdays or Thursdays, respectively. This can be used to measure the risk-neutral interest rate uncertainty associated with FOMC announcements and employment reports. I can also compare the average physical and risk-neutral uncertainty. Lastly, I construct options-implied densities on the eve of FOMC and employment report days.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 6","pages":"1085-1097"},"PeriodicalIF":1.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145284954","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":"An Automatic Multi-Scale Test for Serial Correlation of High-Dimensional Time Series","authors":"Bingbing Zhang,&nbsp;Mengya Liu","doi":"10.1111/jtsa.12815","DOIUrl":"10.1111/jtsa.12815","url":null,"abstract":"<div>\u0000 \u0000 <p>This article proposes an automatic multi-scale test for detecting serial correlation of high-dimensional time series (HDTS) from the perspective of time-frequency analysis. Three theoretical tools fuel the construction and implementation of the test, including the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>L</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {L}_2 $$</annotation>\u0000 </semantics></math>-norm, the maximum overlap discrete wavelet transform (MODWT) and a Bayesian Information Criterion (BIC)-like penalty term. The three accomplish, in turn, data dimensionality reduction, scale-by-scale correlation time-frequency analysis and data-driven selection of the optimal number of scales, thus completing the implementation of our testing principle. Under some mild conditions, the limiting null distribution of the proposed test is proved to be chi-square with degrees of freedom 1, and the testing power of our test is analyzed in theory under a general alternative hypothesis. The finite-sample performance of the automatic multi-scale test is demonstrated through a series of simulations. A study of stock returns in different trading sectors is implemented as a practical application.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 2","pages":"433-444"},"PeriodicalIF":1.0,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146139904","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}