Journal of Time Series Analysis最新文献

{"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":"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}

{"title":"A Stochastic Tree for Bubble Asset Modelling and Pricing","authors":"Christian Gourieroux,&nbsp;Joann Jasiak","doi":"10.1111/jtsa.12801","DOIUrl":"https://doi.org/10.1111/jtsa.12801","url":null,"abstract":"<p>We introduce a new stochastic tree representation of a strictly stationary submartingale process for modelling, forecasting, and pricing speculative bubbles on commodity and cryptocurrency markets. The model is compared to other trees proposed in the literature on bubble asset modelling and stochastic volatility approximation. We show that the proposed model is an extension of the well-known Blanchard-Watson bubble. The model provides (quasi) closed-form pricing formulas for European options, which are derived and illustrated.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 5","pages":"932-944"},"PeriodicalIF":1.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12801","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767372","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":"Exact likelihood for inverse gamma stochastic volatility models","authors":"Roberto Leon-Gonzalez,&nbsp;Blessings Majoni","doi":"10.1111/jtsa.12795","DOIUrl":"https://doi.org/10.1111/jtsa.12795","url":null,"abstract":"<p>We obtain a novel analytic expression of the likelihood for a stationary inverse gamma stochastic volatility (SV) model. This allows us to obtain the maximum likelihood estimator for this nonlinear non-Gaussian state space model. Further, we obtain both the filtering and smoothing distributions for the inverse volatilities as mixtures of gammas, and therefore, we can provide the smoothed estimates of the volatility. We show that by integrating out the volatilities the model that we obtain has the resemblance of a GARCH in the sense that the formulas are similar, which simplifies computations significantly. The model allows for fat tails in the observed data. We provide empirical applications using exchange rates data for seven currencies and quarterly inflation data for four countries. We find that the empirical fit of our proposed model is overall better than alternative models for four countries currency data and for two countries inflation data.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 4","pages":"774-795"},"PeriodicalIF":1.2,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273224","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":"The Liquidity Uncertainty Premium Puzzle","authors":"Maria Flora,&nbsp;Ilaria Gianstefani,&nbsp;Roberto Renò","doi":"10.1111/jtsa.12802","DOIUrl":"https://doi.org/10.1111/jtsa.12802","url":null,"abstract":"<p>The puzzling negative relation between liquidity uncertainty and asset returns, originally put forward by Chordia, Subrahmanyam, and Anshuman (2001) and confirmed by the subsequent empirical literature up to date, is neither robust to the aggregation period, nor to the observation frequency used to compute the volatility of trading volume. We demonstrate that their procedure involves an estimation bias due to the persistence and skewness of volumes. When using an alternative approach based on high-frequency data to measure liquidity uncertainty, the relationship turns out to be positive. However, portfolio strategies based on liquidity uncertainty do not appear to be profitable.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 2","pages":"286-299"},"PeriodicalIF":1.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12802","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143244373","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":"Bubbles and crashes: A tale of quantiles","authors":"Efthymios G. Pavlidis","doi":"10.1111/jtsa.12794","DOIUrl":"https://doi.org/10.1111/jtsa.12794","url":null,"abstract":"<p>Periodically collapsing bubbles, if they exist, induce asymmetric dynamics in asset prices. In this article, I show that unit root quantile autoregressive models can approximate such dynamics by allowing the largest autoregressive root to take values below unity at low quantiles, which correspond to price crashes, and above unity at upper quantiles, that correspond to bubble expansions. On this basis, I employ two unit root tests based on quantile autoregressions to detect bubbles. Monte Carlo simulations suggest that the two tests have good size and power properties, and can outperform recursive least-squares-based tests. The merits of the two tests are further illustrated in three empirical applications that examine Bitcoin, US equity and US housing markets. In the empirical applications, special attention is given to the issue of controlling for economic fundamentals. The estimation results indicate the presence of asymmetric dynamics that closely match those of the simulated bubble processes.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 5","pages":"884-907"},"PeriodicalIF":1.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12794","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767899","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":"Modal volatility function","authors":"Aman Ullah,&nbsp;Tao Wang","doi":"10.1111/jtsa.12790","DOIUrl":"https://doi.org/10.1111/jtsa.12790","url":null,"abstract":"<p>We in this article propose a novel non-parametric estimator for the volatility function within a broad context that encompasses nonlinear time series models as a special case. The new estimator, built on the mode value, is designed to complement existing mean volatility measures to reveal distinct data features. We demonstrate that the suggested modal volatility estimator can be obtained asymptotically as well as if the conditional mean regression function were known, assuming observations are from a strictly stationary and absolutely regular process. Under mild regularity conditions, we establish that the asymptotic distributions of the resulting estimator align with those derived from independent observations, albeit with a slower convergence rate compared to non-parametric mean regression. The theory and practice of bandwidth selection are discussed. Moreover, we put forward a variance reduction technique for the modal volatility estimator to attain asymptotic relative efficiency while maintaining the asymptotic bias unchanged. We numerically solve the modal regression model with the use of a modified modal-expectation-maximization algorithm. Monte Carlo simulations are conducted to assess the finite sample performance of the developed estimation procedure. Two real data analyses are presented to further illustrate the newly proposed model in practical applications. To potentially enhance the accuracy of the bias term, we in the end discuss the extension of the method to local exponential modal estimation. We showcase that the suggested exponential modal volatility estimator shares the same asymptotic variance as the non-parametric modal volatility estimator but may exhibit a smaller bias.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 4","pages":"748-773"},"PeriodicalIF":1.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273228","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 analysis for financial bubble detection and surveillance","authors":"Ruike Wu,&nbsp;Shuping Shi,&nbsp;Jilin Wu","doi":"10.1111/jtsa.12791","DOIUrl":"https://doi.org/10.1111/jtsa.12791","url":null,"abstract":"<p>Understanding and monitoring financial bubbles is critical, as they can lead to market instability, asset price crashes, and economic downturns with widespread consequences. This article explores the usefulness of quantile regression (QR) technique in detecting and surveilling financial bubbles, encompassing both global testing and real-time monitoring. We demonstrate that the QR-based quantile unit root test, coupled with an optimal quantile selection technique, serves as an effective tool for a global bubble test without necessitating additional recursive techniques. Moreover, we propose two QR-based bubble monitoring techniques. We show that the monitoring statistics follow a random variate under the null hypothesis of no bubbles but diverge to positive infinity in the presence of a mildly explosive bubble, and hence consistently date the origination of a bubble. Monte Carlo simulations suggest that compared with their LS counterparts, in the presence of skewed distributions, the QR-based global test delivers substantially greater power, while the QR-based monitoring procedures offer higher bubble detection rate and more accurate dating of the bubble origination. As an illustration, we conduct a pseudo real-time monitoring exercise with the S&amp;P 500 composite index.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 5","pages":"908-931"},"PeriodicalIF":1.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767405","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":"Risk parity portfolio optimization under heavy-tailed returns and dynamic correlations","authors":"Marc S. Paolella,&nbsp;Paweł Polak,&nbsp;Patrick S. Walker","doi":"10.1111/jtsa.12792","DOIUrl":"https://doi.org/10.1111/jtsa.12792","url":null,"abstract":"<p>Risk parity portfolio optimization, using expected shortfall as the risk measure, is investigated when asset returns are fat-tailed and heteroscedastic with regime switching dynamic correlations. The conditional return distribution is modeled by an elliptical multi-variate generalized hyperbolic distribution, allowing for fast parameter estimation via an expectation-maximization algorithm, and a semi-closed form of the risk contributions. A new method for efficient computation of non-Gaussian risk parity weights sidesteps the need for numerical simulations or Cornish–Fisher-type approximations. Accounting for fat-tailed returns, the risk parity allocation is less sensitive to volatility shocks, thereby generating lower portfolio turnover, in particular during market turmoils such as the global financial crisis or the COVID shock. While risk parity portfolios are rather robust to the misuse of the Gaussian distribution, a sophisticated time series model can improve risk-adjusted returns, strongly reduces drawdowns during periods of market stress and enables to use a holistic risk model for portfolio and risk management.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 2","pages":"353-377"},"PeriodicalIF":1.2,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253854","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":"Mixed orthogonality graphs for continuous-time state space models and orthogonal projections","authors":"Vicky Fasen-Hartmann,&nbsp;Lea Schenk","doi":"10.1111/jtsa.12787","DOIUrl":"https://doi.org/10.1111/jtsa.12787","url":null,"abstract":"<p>In this article, we derive (local) orthogonality graphs for the popular continuous-time state space models, including in particular multivariate continuous-time ARMA (MCARMA) processes. In these (local) orthogonality graphs, vertices represent the components of the process, directed edges between the vertices indicate causal influences and undirected edges indicate contemporaneous correlations between the component processes. We present sufficient criteria for state space models to satisfy the assumptions of Fasen-Hartmann and Schenk (2024a) so that the (local) orthogonality graphs are well-defined and various Markov properties hold. Both directed and undirected edges in these graphs are characterised by orthogonal projections on well-defined linear spaces. To compute these orthogonal projections, we use the unique controller canonical form of a state space model, which exists under mild assumptions, to recover the input process from the output process. We are then able to derive some alternative representations of the output process and its highest derivative. Finally, we apply these representations to calculate the necessary orthogonal projections, which culminate in the characterisations of the edges in the (local) orthogonality graph. These characterisations are given by the parameters of the controller canonical form and the covariance matrix of the driving Lévy process.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"46 4","pages":"692-726"},"PeriodicalIF":1.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12787","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273478","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}