Journal of Time Series Analysis最新文献

{"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}

{"title":"Time Series for QFFE: Special Issue of the Journal of Time Series Analysis","authors":"Christian Francq,&nbsp;Christophe Hurlin,&nbsp;Sébastien Laurent,&nbsp;Jean-Michel Zakoian","doi":"10.1111/jtsa.12814","DOIUrl":"https://doi.org/10.1111/jtsa.12814","url":null,"abstract":"<p>QFFE stands for Quantitative Finance and Financial Econometrics conference, an event organized by Sébastien Laurent in Marseille every year since 2018. Each year there are two keynote speakers and two guest speakers, and around 60 selected papers are presented. The program for next year and previous years can be found here. The conference is preceded by a spring school, which offers doctoral students, post-doc, and young academics the opportunity to attend doctoral-level courses.</p><p>The QFFE conference is part of the ANR-funded project MLEforRisk (ANR-21-CE26-0007), which stands for Machine Learning and Econometrics for Risk Measurement in Finance. The project seeks to enhance our understanding of the advantages and limitations of integrating econometric methods with machine learning for measuring financial risks. This multidisciplinary initiative bridges the fields of finance and financial econometrics, bringing together a team of junior and senior researchers with expertise in management, economics, applied mathematics, and data science. The project aims to advance both theoretical insights and practical applications, fostering innovation at the intersection of these disciplines.</p><p>Since financial data such as stock prices, interest rates, and exchange rates are observed over time, time series analysis is crucial in finance. Finance professionals and academics often rely on fundamental time series models, such as ARMA, as well as essential time series techniques such as spectral analysis. Financial researchers are therefore naturally attracted to any new developments in time series. Econometricians have also developed new time series models and methods to capture the specificities of financial data. Contributions of econometricians include cointegration and error correction models, GARCH and stochastic volatility models, score-driven models, VAR models, Markov switching models, non-causal models, simulation-based inference, state space models, and Kalman filters, realized volatility measures, the Black–Scholes model, and factor models. The field of application of all these time series models and techniques is obviously not limited to finance. The aim of this special issue is to present some recent examples of the interface between time series analysis and finance.</p><p>We are very grateful to these authors. We would also like to thank the anonymous reviewers for their valuable review and feedback, which helped to improve the quality of this special issue. Special thanks go to Robert Taylor, Editor-in-Chief of the <i>Journal of Time Series Analysis</i>, for supporting this project, as well as to Priscilla Goldby for her invaluable help.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 2","pages":"214-215"},"PeriodicalIF":1.2,"publicationDate":"2025-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12814","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143252358","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":"Simultaneous Estimation of Stable Parameters for Multiple Autoregressive Processes From Datasets of Nonuniform Sizes","authors":"Johannes Lederer,&nbsp;Rainer von Sachs","doi":"10.1111/jtsa.12806","DOIUrl":"https://doi.org/10.1111/jtsa.12806","url":null,"abstract":"<div>\u0000 \u0000 <p>We develop a finite-sample theory for estimating the coefficients and for the prediction of multiple stable autoregressive processes that (i) share an unknown lag order but (ii) can differ in their sample sizes. Our technique is based on penalisation similar to hierarchical, overlapping group-Lasso but requires a new mathematical set-up to accommodate (i) and (ii). The set-up differs from existing work considerably, for example, in that we estimate the common lag order directly from the data rather than using extrinsic criteria. We prove that the estimated autoregressive processes enjoy stability, and we establish rates for both the estimation and prediction error that can outmatch the known rates in our setting. Our insights on lag selection and stability are also of interest in the case of individual autoregressive processes.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 2","pages":"345-363"},"PeriodicalIF":1.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146139589","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":"Simultaneous Estimation of Stable Parameters for Multiple Autoregressive Processes From Datasets of Nonuniform Sizes","authors":"Johannes Lederer,&nbsp;Rainer von Sachs","doi":"10.1111/jtsa.12806","DOIUrl":"https://doi.org/10.1111/jtsa.12806","url":null,"abstract":"<div>\u0000 \u0000 <p>We develop a finite-sample theory for estimating the coefficients and for the prediction of multiple stable autoregressive processes that (i) share an unknown lag order but (ii) can differ in their sample sizes. Our technique is based on penalisation similar to hierarchical, overlapping group-Lasso but requires a new mathematical set-up to accommodate (i) and (ii). The set-up differs from existing work considerably, for example, in that we estimate the common lag order directly from the data rather than using extrinsic criteria. We prove that the estimated autoregressive processes enjoy stability, and we establish rates for both the estimation and prediction error that can outmatch the known rates in our setting. Our insights on lag selection and stability are also of interest in the case of individual autoregressive processes.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 2","pages":"345-363"},"PeriodicalIF":1.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146139588","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":"Panel Threshold Mixed Data Sampling Models With a Covariate-Dependent Threshold","authors":"Lixiong Yang,&nbsp;I-Po Chen,&nbsp;Chingnun Lee,&nbsp;Yihang Ye","doi":"10.1111/jtsa.12813","DOIUrl":"https://doi.org/10.1111/jtsa.12813","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper introduces a panel threshold mixed data sampling model with a covariate-dependent threshold and unobserved individual-specific threshold effects (PTMIDAS-CDT), in which we allow for a covariate-dependent threshold effect in the relationship between dependent and independent variables sampled at different frequencies, and allow for unobserved individual-specific threshold effects. Based on the Chamberlain–Mundlak correlated random effects (CRE) device and Markov chain Monte Carlo (MCMC) technique, we develop the estimator of model parameters and suggest test statistics for threshold effect, threshold constancy, the equal weighting scheme, and unobserved individual-specific threshold effects. We establish the asymptotic properties of the proposed estimator in the small-threshold-effect framework and derive the limiting distributions of the suggested test statistics. Monte Carlo simulations are conducted to examine the performance properties of the estimation and testing procedures. The simulation results point out that the estimation procedure works well in finite samples, and the test statistics have good size and power properties. The model is illustrated with an application to the nexus between climate change and economic growth.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 2","pages":"414-432"},"PeriodicalIF":1.0,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146136193","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":"Panel Threshold Mixed Data Sampling Models With a Covariate-Dependent Threshold","authors":"Lixiong Yang,&nbsp;I-Po Chen,&nbsp;Chingnun Lee,&nbsp;Yihang Ye","doi":"10.1111/jtsa.12813","DOIUrl":"https://doi.org/10.1111/jtsa.12813","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper introduces a panel threshold mixed data sampling model with a covariate-dependent threshold and unobserved individual-specific threshold effects (PTMIDAS-CDT), in which we allow for a covariate-dependent threshold effect in the relationship between dependent and independent variables sampled at different frequencies, and allow for unobserved individual-specific threshold effects. Based on the Chamberlain–Mundlak correlated random effects (CRE) device and Markov chain Monte Carlo (MCMC) technique, we develop the estimator of model parameters and suggest test statistics for threshold effect, threshold constancy, the equal weighting scheme, and unobserved individual-specific threshold effects. We establish the asymptotic properties of the proposed estimator in the small-threshold-effect framework and derive the limiting distributions of the suggested test statistics. Monte Carlo simulations are conducted to examine the performance properties of the estimation and testing procedures. The simulation results point out that the estimation procedure works well in finite samples, and the test statistics have good size and power properties. The model is illustrated with an application to the nexus between climate change and economic growth.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 2","pages":"414-432"},"PeriodicalIF":1.0,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146136228","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":"Quantile Regression Estimation for Poisson Autoregressive Models","authors":"Danshu Sheng,&nbsp;Dehui Wang","doi":"10.1111/jtsa.12811","DOIUrl":"https://doi.org/10.1111/jtsa.12811","url":null,"abstract":"<div>\u0000 \u0000 <p>Estimating conditional quantiles plays a crucial role in modern risk management and other various applications. However, the quantile regression (QR) estimation of Poisson autoregressive (PAR) models, count-type models, remain an unresolved challenge. In this study, we propose a novel approach that employs a jittering smoothing method and a novel transformation strategy to convert this complex problem into an easily implementable quantile regression problem for continuous-type regression models. The asymptotic theory of the estimator is derived under some regularity conditions and the applications to four popular and classical PAR models are considered. Additionally, a novel <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>h</mi>\u0000 </mrow>\u0000 <annotation>$$ h $$</annotation>\u0000 </semantics></math>-step prediction method (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>h</mi>\u0000 </mrow>\u0000 <annotation>$$ h $$</annotation>\u0000 </semantics></math>-QRF) is developed to forecast the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>h</mi>\u0000 </mrow>\u0000 <annotation>$$ h $$</annotation>\u0000 </semantics></math>-step conditional distribution. The finite sample performance of the method is examined, and its advantages over existing methods are illustrated by simulation studies and an empirical application to the daily stock volume dataset of Technofirst.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 2","pages":"378-413"},"PeriodicalIF":1.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146139234","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":"Quantile Regression Estimation for Poisson Autoregressive Models","authors":"Danshu Sheng,&nbsp;Dehui Wang","doi":"10.1111/jtsa.12811","DOIUrl":"https://doi.org/10.1111/jtsa.12811","url":null,"abstract":"<div>\u0000 \u0000 <p>Estimating conditional quantiles plays a crucial role in modern risk management and other various applications. However, the quantile regression (QR) estimation of Poisson autoregressive (PAR) models, count-type models, remain an unresolved challenge. In this study, we propose a novel approach that employs a jittering smoothing method and a novel transformation strategy to convert this complex problem into an easily implementable quantile regression problem for continuous-type regression models. The asymptotic theory of the estimator is derived under some regularity conditions and the applications to four popular and classical PAR models are considered. Additionally, a novel <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>h</mi>\u0000 </mrow>\u0000 <annotation>$$ h $$</annotation>\u0000 </semantics></math>-step prediction method (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>h</mi>\u0000 </mrow>\u0000 <annotation>$$ h $$</annotation>\u0000 </semantics></math>-QRF) is developed to forecast the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>h</mi>\u0000 </mrow>\u0000 <annotation>$$ h $$</annotation>\u0000 </semantics></math>-step conditional distribution. The finite sample performance of the method is examined, and its advantages over existing methods are illustrated by simulation studies and an empirical application to the daily stock volume dataset of Technofirst.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 2","pages":"378-413"},"PeriodicalIF":1.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146139235","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 Improved Procedure for Retrospectively Dating the Emergence and Collapse of Bubbles","authors":"Mohitosh Kejriwal,&nbsp;Linh Nguyen,&nbsp;Pierre Perron","doi":"10.1111/jtsa.12810","DOIUrl":"https://doi.org/10.1111/jtsa.12810","url":null,"abstract":"<div>\u0000 \u0000 <p>This article proposes a new ordinary least squares (OLS)-based procedure for retrospectively dating the emergence and collapse of bubbles. We first consider a data generating process that entails a switch from a unit root regime to an explosive regime followed by a collapse and subsequent return to unit root behavior. We demonstrate analytically that the standard OLS estimates are inconsistent and date both the origination and implosion points with a delay in large samples. A simple modification that involves omitting the residual corresponding to the implosion date is shown to yield consistent estimates. We also develop an efficient dating algorithm that can accommodate a framework with multiple bubbles. The algorithm exploits the explicit form of the unit root restrictions to directly embed them into the recursive optimization problem which obviates the need to rely on an iterative scheme that requires initial values. Extensive simulation experiments indicate that our proposed procedure typically delivers estimates with lower bias and root mean squared error relative to competing alternatives. An empirical illustration is included.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 5","pages":"867-883"},"PeriodicalIF":1.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767663","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}