Journal of Time Series Analysis最新文献

Robust Estimation and Inference for Time-Varying Unconditional Volatility 时变无条件波动的鲁棒估计与推理

IF 1.2 4区数学

Journal of Time Series Analysis Pub Date : 2026-10-12 Epub Date: 2025-11-13 DOI: 10.1111/jtsa.70034

Adam Lee, Rickard Sandberg, Genaro Sucarrat

{"title":"Robust Estimation and Inference for Time-Varying Unconditional Volatility","authors":"Adam Lee, Rickard Sandberg, Genaro Sucarrat","doi":"10.1111/jtsa.70034","DOIUrl":"https://doi.org/10.1111/jtsa.70034","url":null,"abstract":"We derive a general and robust estimator of a large class of parametric specifications of time-varying unconditional volatility of financial returns, both univariate and multivariate, and establish the Consistency and Asymptotic Normality (CAN) of the estimator. A number of well-known and widely used parametric specifications, for many of which asymptotic results have not been specifically established, are contained in the class. The estimator is robust in the sense that the exact specification of the conditional volatility dynamics need not be known or estimated, and in the sense that the stochastic component need not be strictly stationary. The latter is especially important in light of recent findings, which document that financial returns are frequently characterised by a non-stationary zero-process. Our estimator is also robust to the well-known “curse of dimensionality” in multivariate models due to its equation-by-equation nature. While our estimator does not require the exact specification of the conditional volatility dynamics to be known or estimated, our results imply that the scaled GARCH(1,1) specification is well-defined under both correct and incorrect specifications. So we provide methods for its estimation in a second step. Also, due to the assumptions we rely upon, our results extend directly to the Multiplicative Error Model (MEM) interpretation of volatility models. This means our results can also be applied to other non-negative processes like volume, duration, realised volatility, dividends, unemployment, and so on. Three numerical applications illustrate the versatility of our results.","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 6","pages":"1139-1152"},"PeriodicalIF":1.2,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.70034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149177062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Testing and Estimation of Change Point in ARMA Model With Heavy-Tailed G-GARCH Noises 含重尾G-GARCH噪声的ARMA模型变化点的检验与估计

IF 1.2 4区数学

Journal of Time Series Analysis Pub Date : 2026-10-12 Epub Date: 2025-12-18 DOI: 10.1111/jtsa.70039

Qiang Bai, Shiqing Ling, Ke Zhu

引用次数: 0

Optimal Linear Prediction With Functional Observations: Why You Can Use a Simple Post-Dimension Reduction Estimator 最优线性预测与功能观察：为什么你可以使用一个简单的后降维估计器

IF 1.2 4区数学

Journal of Time Series Analysis Pub Date : 2026-10-12 Epub Date: 2025-12-16 DOI: 10.1111/jtsa.70038

Won-Ki Seo

引用次数: 0

Autocorrelation Functions for Point-Process Time Series 点过程时间序列的自相关函数

IF 1.2 4区数学

Journal of Time Series Analysis Pub Date : 2026-10-12 Epub Date: 2025-08-29 DOI: 10.1111/jtsa.70018

Daniel Gervini

引用次数: 0

Estimating and Testing Multiple Structural Breaks in Nonparametric Regressions 非参数回归中多重结构断裂的估计与检验

IF 1.2 4区数学

Journal of Time Series Analysis Pub Date : 2026-10-12 Epub Date: 2025-12-08 DOI: 10.1111/jtsa.70037

Yiqiu Cao, Zhonghao Fu, Yongmiao Hong, Xia Wang, Xingtong Zhang

{"title":"Estimating and Testing Multiple Structural Breaks in Nonparametric Regressions","authors":"Yiqiu Cao, Zhonghao Fu, Yongmiao Hong, Xia Wang, Xingtong Zhang","doi":"10.1111/jtsa.70037","DOIUrl":"https://doi.org/10.1111/jtsa.70037","url":null,"abstract":"<div>\u0000 \u0000 We propose a novel approach for estimating and testing multiple structural breaks in nonparametric regressions using the discrete Fourier transform (DFT). By transforming the unknown regression function into the frequency domain via the DFT, we analyze structural breaks within a frequency-indexed pseudo generalized regression model. This approach circumvents the need for smoothed nonparametric estimation of the regression function. To detect structural breaks, we introduce a generalized sup-<math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>F</mi>\u0000 </mrow>\u0000 <annotation>$$ F $$</annotation>\u0000 </semantics></math> test, which has power against a class of local alternatives converging at the parametric rate. This test is asymptotically more powerful than existing smoothed nonparametric tests. Furthermore, we develop an information criterion and a sequential testing procedure to determine the appropriate number of breaks. Simulation studies demonstrate the superior finite sample performance of our proposed approach. In an empirical application, we apply our method to investigate the stability of the conditional capital asset pricing model, uncovering significant evidence of structural breaks in both factor loadings and pricing errors.\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 6","pages":"1153-1170"},"PeriodicalIF":1.2,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149177008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On Selection of Cross-Section Averages in Non-Stationary Environments 非平稳环境下截面平均值的选择

IF 1.2 4区数学

Journal of Time Series Analysis Pub Date : 2026-10-12 Epub Date: 2025-10-21 DOI: 10.1111/jtsa.70027

Jan Ditzen, Ovidijus Stauskas

引用次数: 0

A Note on Local Polynomial Regression for Time Series in Banach Spaces Banach空间中时间序列局部多项式回归的一个注记

IF 1.2 4区数学

Journal of Time Series Analysis Pub Date : 2026-10-12 Epub Date: 2025-11-10 DOI: 10.1111/jtsa.70033

Florian Heinrichs

引用次数: 0

Nonparametric Inference of Conditional Expectile Functions in Large-Scale Time Series Data With Improved Efficiency 大规模时间序列数据中条件期望函数的非参数推理。

IF 1.2 4区数学

Journal of Time Series Analysis Pub Date : 2026-10-12 Epub Date: 2025-10-08 DOI: 10.1111/jtsa.70021

Feipeng Zhang, Ping-Shou Zhong

{"title":"Nonparametric Inference of Conditional Expectile Functions in Large-Scale Time Series Data With Improved Efficiency","authors":"Feipeng Zhang, Ping-Shou Zhong","doi":"10.1111/jtsa.70021","DOIUrl":"10.1111/jtsa.70021","url":null,"abstract":"Expectile is a coherent and elicitable law-invariant risk measure widely applied in risk management. Existing methods based on iteratively reweighted least squares (IWLS) are not computationally efficient for large-scale sample sizes. To overcome the issue, we develop a direct nonparametric conditional expectile function estimator by inverting the local polynomial estimator of the conditional loss-gain function. The proposed estimator is computationally friendly and stable without using iterative algorithms that require computation with large-scale data in each iteration. We establish the asymptotic distribution of the proposed estimator. We further show that the proposed estimator has a smaller variance than the existing IWLS estimator and a smaller mean square error in various scenarios. Simulations confirm the computational and statistical efficiency of the proposed method. We further apply the proposed methods to an S&P500 data set to illustrate the computational time to estimate the conditional expectile-based value-at-risk (EVaR) and the precision in out-of-sample prediction.","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 6","pages":"1098-1112"},"PeriodicalIF":1.2,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13564231/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148957209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mode Meets Mean: A New Robust Volatility 模态满足均值：一种新的鲁棒波动率

IF 1.2 4区数学

Journal of Time Series Analysis Pub Date : 2026-10-12 Epub Date: 2025-09-23 DOI: 10.1111/jtsa.70022

Tao Wang

{"title":"Mode Meets Mean: A New Robust Volatility","authors":"Tao Wang","doi":"10.1111/jtsa.70022","DOIUrl":"https://doi.org/10.1111/jtsa.70022","url":null,"abstract":"<div>\u0000 \u0000 Ullah and Wang (Journal of Time Series Analysis, 46 (4), 748–773) introduced a novel nonparametric estimator for the volatility function that leverages the mode to complement traditional mean-based volatility measures, thereby highlighting unique features of the data. In this paper, we extend their framework to estimate traditional mean volatility by proposing a new approach, termed robust mode-oriented volatility. Our method treats the bandwidth parameter associate with the kernel objective function as a constant rather than a shrinkage parameter, prioritizing robustness and efficiency while maintaining its foundation in the mode-based framework. We demonstrate that under <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>α</mi>\u0000 </mrow>\u0000 <annotation>$$ alpha $$</annotation>\u0000 </semantics></math>-mixing time series dependence, the proposed robust estimator retains the same asymptotic distribution as estimators derived under independence assumptions, while achieving the convergence rate of nonparametric mean regression. Furthermore, we theoretically establish that efficiency gains over traditional mean-based estimation can be realized by appropriately adjusting the bandwidth, particularly in the presence of outliers or heavy-tailed distributions. In contrast to Ullah and Wang (Journal of Time Series Analysis, 46 (4), 748–773), which focused on capturing volatility structures specific to the mode, our approach broadens the applicability of their framework by integrating it with traditional mean volatility estimation. The discussions on bandwidth selection and numerical examples highlight the finite sample performance and practical advantages of our robust estimation procedure.\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 6","pages":"1198-1205"},"PeriodicalIF":1.2,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149177694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust CDF-Filtering of a Location Parameter 位置参数的鲁棒cdf滤波

IF 1.2 4区数学

Journal of Time Series Analysis Pub Date : 2026-10-12 Epub Date: 2025-11-02 DOI: 10.1111/jtsa.70026

Leopoldo Catania, Andrew C. Harvey, Alessandra Luati

{"title":"Robust CDF-Filtering of a Location Parameter","authors":"Leopoldo Catania, Andrew C. Harvey, Alessandra Luati","doi":"10.1111/jtsa.70026","DOIUrl":"https://doi.org/10.1111/jtsa.70026","url":null,"abstract":"This paper introduces a novel framework for designing robust filters associated with signal plus noise models having symmetric observation density. The filters are obtained by a recursion where the innovation term is a transform of the cumulative distribution function of the residuals. The latter downweights extreme values by construction and allows the filters to be analytically tractable. The updating scheme naturally arises as the solution of an optimization problem, where the objective function is a continuous version of the quantile check function, formerly employed as a proper scoring function for quantiles and used to construct robust minimum contrast estimators. Stationarity, ergodicity and invertibility are derived under minimal assumptions and preserved under different parametric specifications. Estimation is carried out by the method of maximum likelihood and the asymptotic theory is developed under misspecification. As an illustration, the new filters are applied to brain scan data and compared across Gaussian, Student-t, Cauchy and Logistic density specifications, with alternative methods. Additional results include a novel class of score-driven models and a subgaussian density suitable for robust filtering and modelling, arising as the infinite sum of independent non-identically distributed uniform random variables.","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 6","pages":"1113-1123"},"PeriodicalIF":1.2,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.70026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149177286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0