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Forecast combination under heavy-tailed errors
Cheng, Gang; Wang, Sicong; Yang, Yuhong - In: Econometrics : open access journal 3 (2015) 4, pp. 797-824
Forecast combination has been proven to be a very important technique to obtain accurate predictions for various applications in economics, finance, marketing and many other areas. In many applications, forecast errors exhibit heavy-tailed behaviors for various reasons. Unfortunately, to our...
Persistent link: https://www.econbiz.de/10011411497
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Goodness-of-fit tests for copulas of multivariate time series
Rémillard, Bruno - In: Econometrics : open access journal 5 (2017) 1, pp. 1-23
In this paper, we study the asymptotic behavior of the sequential empirical process and the sequential empirical copula … being jointly estimated, then the empirical copula process behaves as if the innovations were observed; a remarkable …
Persistent link: https://www.econbiz.de/10011654178
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Copula-based factor models for multivariate asset returns
Ivanov, Eugen; Min, Aleksey; Ramsauer, Franz - In: Econometrics : open access journal 5 (2017) 2, pp. 1-24
Recently, several copula-based approaches have been proposed for modeling stationary multivariate time series. All of … copula autoregressive (COPAR) approach to model the dependence of unobserved multivariate factors resulting from two dynamic … copula models for stationary multivariate time series. An empirical study illustrates the forecasting superiority of our …
Persistent link: https://www.econbiz.de/10011654435
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Detecting location shifts during model selection by step-indicator saturation
Castle, Jennifer; Doornik, Jurgen A.; Hendry, David F.; … - In: Econometrics : open access journal 3 (2015) 2, pp. 240-264
To capture location shifts in the context of model selection, we propose selecting significant step indicators from a saturating set added to the union of all of the candidate variables. The null retention frequency and approximate non-centrality of a selection test are derived using a...
Persistent link: https://www.econbiz.de/10011297656
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Evaluating Approximate Point Forecasting of Count Processes
Homburg, Annika; Weiß, Christian H.; Alwan, Layth C.; … - In: Econometrics : open access journal 7 (2019) 3/30
In forecasting count processes, practitioners often ignore the discreteness of counts and compute forecasts based on Gaussian approximations instead. For both central and non-central point forecasts, and for various types of count processes, the performance of such approximate point forecasts is...
Persistent link: https://www.econbiz.de/10012161530
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Asymmetry and leverage in conditional volatility models
McAleer, Michael - In: Econometrics : open access journal 2 (2014) 3, pp. 145-150
The three most popular univariate conditional volatility models are the generalized autoregressive conditional heteroskedasticity (GARCH) model of Engle (1982) and Bollerslev (1986), the GJR (or threshold GARCH) model of Glosten, Jagannathan and Runkle (1992), and the exponential GARCH (or...
Persistent link: https://www.econbiz.de/10010417180
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Selection criteria in regime switching conditional volatility models
Chuffart, Thomas - In: Econometrics : open access journal 3 (2015) 2, pp. 289-316
A large number of nonlinear conditional heteroskedastic models have been proposed in the literature. Model selection is crucial to any statistical data analysis. In this article, we investigate whether the most commonly used selection criteria lead to choice of the right specification in a...
Persistent link: https://www.econbiz.de/10011297653
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Fixed and long time span jump tests : new Monte Carlo and empirical evidence
Cheng, Mingmian; Swanson, Norman R. - In: Econometrics : open access journal 7 (2019) 1/13, pp. 1-32
Numerous tests designed to detect realized jumps over a fixed time span have been proposed and extensively studied in the financial econometrics literature. These tests differ from “long time span tests” that detect jumps by examining the magnitude of the jump intensity parameter in the data...
Persistent link: https://www.econbiz.de/10012025640
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Jointly modeling autoregressive conditional mean and variance of non-negative valued time series
Kawakatsu, Hiroyuki - In: Econometrics : open access journal 7 (2019) 4/48, pp. 1-19
This paper considers observation driven models with conditional mean and variance dynamics for non-negative valued time series. The motivation is to relax the restriction imposed on the higher order moment dynamics in standard multiplicative error models driven only by the conditional mean...
Persistent link: https://www.econbiz.de/10012160740
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Estimation of realized asymmetric stochastic volatility models using Kalman filter
Asai, Manabu - In: Econometrics : open access journal 11 (2023) 3, pp. 1-13
Despite the growing interest in realized stochastic volatility models, their estimation techniques, such as simulated maximum likelihood (SML), are computationally intensive. Based on the realized volatility equation, this study demonstrates that, in a finite sample, the quasi-maximum likelihood...
Persistent link: https://www.econbiz.de/10014425668