Journal of Time Series Analysis最新文献

I am delighted to welcome Shuping Shi to the editorial board of the Journal of Time Series Analysis. Shuping joins as an Associate Editor with effect from 1st March 2025.

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 Discovery Early Career Researcher Award from the Australian Research Council and was honored with the prestigious 2022 Young Economist Award by the Economic Society of Australia. Her research has been published in journals, including Review of Financial Studies, Journal of Econometrics, Management Science, International Economic Review, and Econometric Theory. She has been recognized among the top 2% most-cited economists globally in the latest annual report published by Standard University for 2024.

The author declares no conflicts of interest.

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.

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.

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.