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Fractional stochastic volatility model 分数随机波动模型
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2024-05-17 DOI: 10.1111/jtsa.12749
Shuping Shi, Xiaobin Liu, Jun Yu
{"title":"Fractional stochastic volatility model","authors":"Shuping Shi, Xiaobin Liu, Jun Yu","doi":"10.1111/jtsa.12749","DOIUrl":"https://doi.org/10.1111/jtsa.12749","url":null,"abstract":"This article introduces a discrete‐time fractional stochastic volatility model (FSV) based on fractional Gaussian noise. The new model includes the standard stochastic volatility model as a special case and has the same limit as the fractional integrated stochastic volatility (FISV) model, which is the continuous‐time fractional Ornstein–Uhlenbeck process. A simulated maximum likelihood method, which maximizes the time‐domain log‐likelihood function calculated by the importance sampling technique, and a frequency‐domain quasi maximum likelihood method (or quasi Whittle) are employed to estimate the model parameters. Simulation studies suggest that, while both estimation methods can accurately estimate the model, the simulated maximum likelihood method outperforms the quasi Whittle method. As an illustration, we fit the FSV and FISV models with the proposed estimation techniques to the S&P 500 composite index over a sample period spanning 45 years.","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141059316","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 a matrix‐valued autoregressive model 关于矩阵值自回归模型
IF 0.9 4区 数学
Journal of Time Series Analysis Pub Date : 2024-05-15 DOI: 10.1111/jtsa.12748
S. Yaser Samadi, Lynne Billard
{"title":"On a matrix‐valued autoregressive model","authors":"S. Yaser Samadi, Lynne Billard","doi":"10.1111/jtsa.12748","DOIUrl":"https://doi.org/10.1111/jtsa.12748","url":null,"abstract":"Many data sets in biology, medicine, and other biostatistical areas deal with matrix‐valued time series. The case of a single univariate time series is very well developed in the literature; and single multi‐variate series (i.e., vector time series) though less well studied have also been developed. A class of matrix time series models is introduced for dealing with situations where there are multiple sets of multi‐variate time series data. Explicit expressions for a matrix autoregressive model along with its cross‐autocorrelation functions are derived. Stationarity conditions are also provided. Least squares estimators and maximum likelihood estimators of the model parameters and their asymptotic properties are derived. Results are illustrated through simulation studies and a real data application.","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140975333","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
Threshold Network GARCH Model 阈值网络 GARCH 模型
IF 1.2 4区 数学
Journal of Time Series Analysis Pub Date : 2024-05-13 DOI: 10.1111/jtsa.12743
Yue Pan, Jiazhu Pan
{"title":"Threshold Network GARCH Model","authors":"Yue Pan,&nbsp;Jiazhu Pan","doi":"10.1111/jtsa.12743","DOIUrl":"10.1111/jtsa.12743","url":null,"abstract":"<p>Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model and its variations have been widely adopted in the study of financial volatilities, while the extension of GARCH-type models to high-dimensional data is always difficult because of over-parameterization and computational complexity. In this article, we propose a multi-variate GARCH-type model that can simplify the parameterization by utilizing the network structure that can be appropriately specified for certain types of high-dimensional data. The asymmetry in the dynamics of volatilities is also considered as our model adopts a threshold structure. To enable our model to handle data with extremely high dimension, we investigate the near-epoch dependence (NED) of our model, and the asymptotic properties of our quasi-maximum-likelihood-estimator (QMLE) are derived from the limit theorems for NED random fields. Simulations are conducted to test our theoretical results. At last we fit our model to log-returns of four groups of stocks and the results indicate that bad news is not necessarily more influential on volatility if the network effects are considered.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12743","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140933060","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
Existence of a Periodic and Seasonal INAR Process 存在一个周期性和季节性的 INAR 进程
IF 1.2 4区 数学
Journal of Time Series Analysis Pub Date : 2024-05-13 DOI: 10.1111/jtsa.12746
Márton Ispány, Pascal Bondon, Valdério Anselmo Reisen, Paulo Roberto Prezotti Filho
{"title":"Existence of a Periodic and Seasonal INAR Process","authors":"Márton Ispány,&nbsp;Pascal Bondon,&nbsp;Valdério Anselmo Reisen,&nbsp;Paulo Roberto Prezotti Filho","doi":"10.1111/jtsa.12746","DOIUrl":"10.1111/jtsa.12746","url":null,"abstract":"<p>A spectral criterion involving the model parameters is given for the existence and uniqueness of a periodically correlated and seasonal non-negative integer-valued autoregressive process. The structure of the mean and covariance functions of the periodically stationary distribution of the model is derived using its implicit state-space representation. Two infinite series representations for the process, the moving average, and the immigrant generation, are established. Based on the latter representation, a novel and parallelizable simulation method is proposed to generate the process.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12746","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140933149","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
A new portmanteau test for predictive regression models with possible embedded endogeneity 针对可能存在嵌入内生性的预测回归模型的新波特曼检验
IF 1.2 4区 数学
Journal of Time Series Analysis Pub Date : 2024-05-12 DOI: 10.1111/jtsa.12745
Yao Rao, Yawen Fan, Huimin Ao, Xiaohui Liu
{"title":"A new portmanteau test for predictive regression models with possible embedded endogeneity","authors":"Yao Rao,&nbsp;Yawen Fan,&nbsp;Huimin Ao,&nbsp;Xiaohui Liu","doi":"10.1111/jtsa.12745","DOIUrl":"10.1111/jtsa.12745","url":null,"abstract":"<p>In the widely used predictive regression model, any possible serial correlation in innovations leads to estimation bias and statistical inference distortions. Hence, it is important to pretest the existence of such serial correlation. Nevertheless, in the presence of embedded endogeneity, which is a common problem in the predictive regression setting, traditional serial correlation tests such as Box–Pierce (BP) and Ljung–Box (LB) tests are found to perform poorly. Motivated by this, we develop a new portmanteau test in this article as a pretest for serial correlation in predictive regression under possible embedded endogeneity. This test is based on the sample splitting idea and the jackknife empirical likelihood method. The asymptotic distribution of the proposed test has been derived, and the Monte Carlo simulations confirm good finite sample performances. As an illustration, we apply our proposed test in pretesting the serial correlation in predictive regression, where financial variables are used to predict the excess return of S&amp;P 500.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140933103","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
Inference for calendar effects in microstructure noise 微观结构噪声中的日历效应推断
IF 1.2 4区 数学
Journal of Time Series Analysis Pub Date : 2024-05-05 DOI: 10.1111/jtsa.12744
Yingwen Tan, Zhiyuan Zhang
{"title":"Inference for calendar effects in microstructure noise","authors":"Yingwen Tan,&nbsp;Zhiyuan Zhang","doi":"10.1111/jtsa.12744","DOIUrl":"10.1111/jtsa.12744","url":null,"abstract":"<p>We develop a statistical inference procedure for the ubiquitous calendar effects in microstructure noise using high frequency data. This is, to the best of our knowledge, the first inference theory ever built for <i>noise calendar effect</i> under the general semi-martingale-plus-noise setup for prices contaminated with non-stationary, endogenous, and serially dependent microstructure noise. We devise a noise-calendar-effect estimator by an appropriately scaled average of high-frequency returns that precede a time of day across a large number of trading days. Feasible central limit theorem for the estimator is established under a joint infill and long-span asymptotics. Monte Carlo simulations corroborate our theoretical findings. An empirical study on the high-frequency data of the e-mini S&amp;P 500 futures and a Chinese stock demonstrates that the noise calendar effect has undergone significant changes over time for the latter, yet remains stable for the former.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140887034","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
Spectral Density Estimation for a Class of Spectrally Correlated Processes 一类频谱相关过程的频谱密度估计
IF 1.2 4区 数学
Journal of Time Series Analysis Pub Date : 2024-04-26 DOI: 10.1111/jtsa.12742
Anna E. Dudek, Bartosz Majewski, Antonio Napolitano
{"title":"Spectral Density Estimation for a Class of Spectrally Correlated Processes","authors":"Anna E. Dudek,&nbsp;Bartosz Majewski,&nbsp;Antonio Napolitano","doi":"10.1111/jtsa.12742","DOIUrl":"10.1111/jtsa.12742","url":null,"abstract":"<p>We study the estimation problem of the spectral density function for harmonizable non-stationary processes. More precisely, we consider spectrally correlated processes whose spectral measure has the support contained in the union of unknown lines with possibly non-unit slopes. We propose the frequency-smoothed periodogram along the estimated support line as an estimator of the spectral density function. We show the mean-square consistency of the proposed estimator. Additionally, we discuss the estimation of the support line in a specific model with its applications in locating a moving source. Finally, we present simulations confirming the proven results.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140806114","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
Quasi-Likelihood Estimation in Volatility Models for Semi-Continuous Time Series 半连续时间序列波动模型中的准概率估计
IF 1.2 4区 数学
Journal of Time Series Analysis Pub Date : 2024-04-17 DOI: 10.1111/jtsa.12741
Šárka Hudecová, Michal Pešta
{"title":"Quasi-Likelihood Estimation in Volatility Models for Semi-Continuous Time Series","authors":"Šárka Hudecová,&nbsp;Michal Pešta","doi":"10.1111/jtsa.12741","DOIUrl":"10.1111/jtsa.12741","url":null,"abstract":"<p>Time series containing non-negligible portion of possibly dependent zeros, whereas the remaining observations are positive, are considered. They are regarded as GARCH processes consisting of non-negative values. Our first aim lies in estimation of the omnibus model parameters taking into account the semi-continuous distribution. The hurdle distribution together with dependent zeros cause that the classical GARCH estimation techniques fail. Two different quasi-likelihood approaches are employed. Both estimators are proved to be strongly consistent and asymptotically normal. The second goal consists in the proposed predictions with bootstrap add-ons. The considered class of models can be reformulated as multiplicative error models. The empirical properties are illustrated in a simulation study, which demonstrates computational efficiency of the employed methods. The developed techniques are presented through an actuarial problem concerning insurance claims.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12741","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140610649","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
Inference in Coarsened Time Series via Generalized Method of Moments 通过广义矩法推断粗化时间序列
IF 1.2 4区 数学
Journal of Time Series Analysis Pub Date : 2024-04-10 DOI: 10.1111/jtsa.12740
Man Fai Ip, Kin Wai Chan
{"title":"Inference in Coarsened Time Series via Generalized Method of Moments","authors":"Man Fai Ip,&nbsp;Kin Wai Chan","doi":"10.1111/jtsa.12740","DOIUrl":"10.1111/jtsa.12740","url":null,"abstract":"<p>We study statistical inference procedures in coarsened time series through the generalized method of moments. A new model for the coarsened time series via multiple potential outcomes is proposed. It can be naturally extended for inferring multi-variate coarsened time series. We show that this framework generates a general class of estimators. It neatly generalizes the classical Horvitz–Thompson estimator for handling coarsened time series data. Asymptotic properties, including consistency and limiting distribution, of the proposed estimators are investigated. Estimators of the optimal weight matrix and the long-run covariance matrix are also derived. In particular, confidence intervals of the mean function of the potential outcome as a function of coarsening index can be constructed. A real-data application on air quality in the USA is investigated.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12740","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140603304","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
Bootstrap prediction inference of nonlinear autoregressive models 非线性自回归模型的引导预测推断
IF 1.2 4区 数学
Journal of Time Series Analysis Pub Date : 2024-04-01 DOI: 10.1111/jtsa.12739
Kejin Wu, Dimitris N. Politis
{"title":"Bootstrap prediction inference of nonlinear autoregressive models","authors":"Kejin Wu,&nbsp;Dimitris N. Politis","doi":"10.1111/jtsa.12739","DOIUrl":"10.1111/jtsa.12739","url":null,"abstract":"<p>The nonlinear autoregressive (NLAR) model plays an important role in modeling and predicting time series. One-step ahead prediction is straightforward using the NLAR model, but the multi-step ahead prediction is cumbersome. For instance, iterating the one-step ahead predictor is a convenient strategy for linear autoregressive (LAR) models, but it is suboptimal under NLAR. In this article, we first propose a simulation and/or bootstrap algorithm to construct optimal point predictors under an <span></span><math>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>L</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow></math> or <span></span><math>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>L</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow></math> loss criterion. In addition, we construct bootstrap prediction intervals in the multi-step ahead prediction problem; in particular, we develop an asymptotically valid quantile prediction interval as well as a pertinent prediction interval for future values. To correct the undercoverage of prediction intervals with finite samples, we further employ predictive – as opposed to fitted – residuals in the bootstrap process. Simulation and empirical studies are also given to substantiate the finite sample performance of our methods.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140564371","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
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