Frontiers in Time Series and Financial Econometrics: An overview
时间序列与金融计量经济学前沿：概览AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确列举时间序列方法、金融协波动预测及用于协波动预测的因子模型。
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时间序列与金融计量经济学前沿：概览
- 研究问题
- 这篇专刊概览关注时间序列与金融计量的哪些研究前沿，以及所收论文如何围绕波动、预测和模型推断展开？
- 方法与思路
- 摘要以研究主题清单介绍专刊内容，涵盖协波动预测、因子模型、阈值模型、条件分位数及多类模型检验。
- 材料陈述的贡献
- 摘要所述贡献是提供专刊研究议题的整体导览，展示多个方向的方法进展，并未提出统一的新估计方法。
- 阅读关注点
- 阅读时应区分概览的主题介绍与各篇论文的实际成果，并分别核对感兴趣方法的适用条件和证据范围。
- 阅读边界
- 仅据所给作者／机构库版本摘要，未核对刊本变化；主题清单不能支持对各方法的定理、效果或优越性作判断。
原文摘要
Two of the fastest growing frontiers in econometrics and quantitative finance are time series and financial econometrics. Significant theoretical contributions to financial econometrics have been made by experts in statistics, econometrics, mathematics, and time series analysis. The purpose of this special issue of the journal on “Frontiers in Time Series and Financial Econometrics” is to highlight several areas of research by leading academics in which novel methods have contributed significantly to time series and financial econometrics, including forecasting co-volatilities via factor models with asymmetry and long memory in realized covariance, prediction of Lévy-driven CARMA processes, functional index coefficient models with variable selection, LASSO estimation of threshold autoregressive models, high dimensional stochastic regression with latent factors, endogeneity and nonlinearity, sign-based portmanteau test for ARCH-type models with heavy-tailed innovations, toward optimal model averaging in regression models with time series errors, high dimensional dynamic stochastic copula models, a misspecification test for multiplicative error models of non- negative time series processes, sample quantile analysis for long-memory stochastic volatility models, testing for independence between functional time series, statistical inference for panel dynamic simultaneous equations models, specification tests of calibrated option pricing models, asymptotic inference in multiple-threshold double autoregressive models, a new hyperbolic GARCH model, intraday value-at-risk: an asymmetric autoregressive conditional duration approach, refinements in maximum likelihood inference on spatial autocorrelation in panel data, statistical inference of conditional quantiles in nonlinear time series models, quasi-likelihood estimation of a threshold diffusion process, threshold models in time series analysis - some reflections, and generalized ARMA models with martingale difference errors.