Journal of Forecasting最新文献

The modeling of high-frequency data that qualify financial asset transactions has been an area of relevant interest among statisticians and econometricians—above all, the analysis of time series of financial durations. Autoregressive conditional duration (ACD) models have been the main tool for modeling financial transaction data, where duration is usually defined as the time interval between two successive events. These models are usually specified in terms of a time-varying mean (or median) conditional duration. In this paper, a new extension of ACD models is proposed which is built on the basis of log-symmetric distributions reparametrized by their quantile. The proposed quantile log-symmetric conditional duration autoregressive model allows us to model different percentiles instead of the traditionally used conditional mean (or median) duration. We carry out an in-depth study of theoretical properties and practical issues, such as parameter estimation using maximum likelihood method and diagnostic analysis based on residuals. A detailed Monte Carlo simulation study is also carried out to evaluate the performance of the proposed models and estimation method in retrieving the true parameter values as well as to evaluate a form of residuals. Finally, we derive a semiparametric intraday value-at-risk (IVaR) model and then the proposed models are applied to two price duration data sets.

In this paper, we investigate whether the bond markets contain important information that can improve the accuracy of stock market volatility forecasts in China. We use realized volatility (RV) implemented by different maturity treasury bond futures contracts to predict the Chinese stock market volatility. Our work is based on the heterogeneous autoregressive (HAR) framework. Empirical results show that the volatility of treasury bond contracts with longer maturities (especially 10 years) has the best effect on predicting the Chinese stock market volatility, both in sample and out of sample. Two machine learning methods, the scaled principal component analysis (SPCA) and the least absolute shrinkage and selection operator (lasso), are also more effective than the HAR benchmark model's prediction. Finally, mean–variance investors can achieve substantial economic gains by allocating their investment portfolios based on volatility forecasts after introducing treasury bond futures volatility.

Predicting crowd flows in smart cities poses a significant challenge for the intelligent transportation system (ITS). Traffic management and behavioral analysis are crucial and have garnered considerable attention from researchers. However, accurately and timely predicting crowd flow is difficult due to various complex factors, including dependencies on recent crowd flow and neighboring regions. Existing studies often focus on spatial–temporal dependencies but neglect to model the relationship between crowd flow in distant areas. In our study, we observe that the daily flow of each region remains relatively consistent, and certain regions, despite being far apart, exhibit similar flow patterns, indicating a strong correlation between them. In this paper, we proposed a novel Multiscale Adaptive Graph-Gated Network (MSAGGN) model. The main components of MSAGGN can be divided into three major parts: (1) To capture the parallel periodic learning architecture through a layer-wise gated mechanism, a layer-wise functional approach is employed to modify gated mechanism, establishing parallel skip periodic connections to effectively manage temporal and external factor information at each time interval; (2) a graph convolutional-based adaptive mechanism that effectively captures crowd flow traffic data by considering dynamic spatial–temporal correlations; and (3) we proposed a novel intelligent channel encoder (ICE). The task of this block is to capture citywide spatial–temporal correlation along external factors to preserve correlation for distant regions with external elements. To integrate spatio-temporal flexibility, we introduce the adaptive transformation module. We assessed our model's performance by comparing it with previous state-of-the-art models and conducting experiments using two real-world datasets for evaluation.

This study examines the role of the US climate policy uncertainty (CPU) index in forecasting the equity premium, employing shrinkage methods such as LASSO and elastic net (ENet) to dynamically select predictors from a dataset spanning April 1987 to December 2022. Alongside CPU, other uncertainty predictors like economic policy uncertainty (EPU), geopolitical risk (GPR), and the volatility index (VIX) are considered to assess their complementary roles in out-of-sample (OOS) equity premium forecasting. The results reveal that while CPU alone cannot consistently predict the equity premium, it provides crucial complementary information when combined with other predictors, leading to a statistically significant OOS $��{�R�}^{�2�}�$ of 1.231%. The relationship between CPU and the equity premium is time varying, with a stronger influence observed during periods of economic downturn or heightened uncertainty, as demonstrated by wavelet coherence analysis. This study also identifies CPU's significant impact on industry-specific returns, particularly in climate-sensitive sectors, offering valuable insights for investment strategies and risk management in an era of increasing CPU.

Multivariate time series analysis uncovers the intricate relationships among multiple variables, which plays a vital role in areas such as policy-making and business decision-making. This paper employs a reduced rank regression model to investigate a robust estimation method for multivariate time series data using an $��{�\ell �}_{�1�}�$ penalty. The goal is to achieve rapid parameter estimation while ensuring robustness in the analysis of time series data. This study provides a detailed description of the solution process and examines the theoretical properties of the proposed method. To evaluate its effectiveness, the proposed model is compared with full-rank regression and the multivariate regression with covariance estimation (MRCE) method through simulations, as well as an analysis of the Sceaux household electric power consumption data. The results indicate that the proposed model performs well.