Frontiers in Time Series and Financial Econometrics: An overview ☆

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 Levy-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.

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[2]  Q. Yao,et al.  High dimensional stochastic regression with latent factors, endogeneity and nonlinearity , 2015 .

[3]  Guodong Li,et al.  A new hyperbolic GARCH model , 2015 .

[4]  Robert A. Jarrow,et al.  Specification Tests of Calibrated Option Pricing Models , 2013 .

[5]  Michael McAleer,et al.  Forecasting co-volatilities via factor models with asymmetry and long memory in realized covariance☆ , 2014 .

[6]  Lajos Horvath,et al.  Testing for independence between functional time series , 2014 .

[7]  Dong Li,et al.  Asymptotic inference in multiple-threshold double autoregressive models , 2015 .

[8]  N. Chan,et al.  LASSO estimation of threshold autoregressive models , 2015 .

[9]  Patrick W. Saart,et al.  A misspecification test for multiplicative error models of non-negative time series processes , 2015 .

[10]  Ching-Kang Ing,et al.  Toward optimal model averaging in regression models with time series errors , 2015, 1503.06401.

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[12]  Kung-Sik Chan,et al.  Quasi-likelihood estimation of a threshold diffusion process , 2015 .

[13]  H. Tong.  Threshold models in time series analysis—Some reflections , 2015 .

[14]  P. Robinson,et al.  Refinements in maximum likelihood inference on spatial autocorrelation in panel data , 2015 .

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[16]  Y. Tse,et al.  Intraday Value-at-Risk: An asymmetric autoregressive conditional duration approach , 2015 .

[17]  Paul Waltman,et al.  A Threshold Model , 1974 .

[18]  Zongwu Cai,et al.  Functional index coefficient models with variable selection , 2015 .

[19]  Prediction of Lévy-driven CARMA processes , 2015 .

[20]  Hwai-Chung Ho.  Sample quantile analysis for long-memory stochastic volatility models , 2015 .

[21]  Rong Chen,et al.  Generalized ARMA Models with Martingale Difference Errors , 2015 .

[22]  Ruey S. Tsay,et al.  High Dimensional Dynamic Stochastic Copula Models , 2014 .

[23]  C. Hsiao,et al.  Statistical Inference for Panel Dynamic Simultaneous Equations Models , 2014 .

[24]  Min Chen,et al.  Sign-based portmanteau test for ARCH-type models with heavy-tailed innovations , 2015 .

[25]  Mike K. P. So,et al.  Statistical Inference of Conditional Quantiles in Nonlinear Time Series Models , 2013 .