Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series

By Andrew S. Harvey. 2013

365 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/b883e5d4da1da895af4ca1697cc9f2171c9c7cbb

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Citations in the corpus

Information Theoretic Optimality of Observation Driven Time Series Models (2014, neutral): “If we consider the Student’s t GAS volatility model of Creal et al. (2013) and Harvey (2013), the functions ξδf ,δy(f̃t, yt) and ζδf ,δy(f̃t, yt) are not uniformly bounded.”
Conditional Euro Area Sovereign Default Risk (2014, neutral): “Earlier applications of the GHST distribution to financial and economic data include, for example, Mencı́a and Sentana (2005), Hu (2005), and Aas and Haff (2006).”
Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time Series Models (2016, positive): “We adopt the autoregressive score framework of Creal et al. (2011, 2013) and Harvey (2013) to introduce a time-varying ft.”
The effects of a “black swan” event (COVID-19) on herding behavior in cryptocurrency markets (2021, positive): “And finally, time-varying correlation among selected markets in terms of herding is estimated by means of the timevarying parameter copula models (GAS models with conditional multivariate Student–t distribution and time–varying correlations as in Creal et al., 2011, 2013; Harvey, 2013).”
Forecasting: theory and practice (2020, background): “• Non-linear and non-Gaussian: TAR, ARCH, GARCH, Stochastic Volatility (Durbin and Koopman, 2012), Dynamic Conditional Score (Harvey, 2013), Generalised Autoregressive Score (Creal et al., 2013), multiplicative exponential smoothing, multiplicative unobserved components.”
Forecasting risk with Markov-switching GARCH models:A large-scale performance study (2018, background): “…by Laurent, Lecourt, and Palm (2016), this effect can be captured in a SR framework through the use of generalized autoregressive score (GAS) models (also referred to as dynamic conditional score (DCS) models) with fat tails, as was introduced by Creal, Koopman, and Lucas (2013) and Harvey (2013).”
Value-at-risk and expected shortfall in cryptocurrencies’ portfolio: a vine copula–based approach (2019, neutral): “Two approaches that have been successfully used to model and forecast the daily volatility of cryptocurrencies in a univariate context are the robust GARCH model of Boudt, Danielsson, and Laurent (2013) and the GAS model of Creal, Koopman, and Lucas (2013) and Harvey (2013).”
New HEAVY Models for Fat-Tailed Realized Covariances and Returns (2017, methodology): “A number of these features are discussed for the univariate case in Harvey (2013).”
Time‐series models with an EGB2 conditional distribution (2014, positive): “Following Harvey and Sucarrat (2014), the model can re-formulated as yt = (εt−με) exp(λtpt−1), t = 1, .”
Time‐Varying Transition Probabilities for Markov Regime Switching Models (2017, neutral): “E-mail: s.j.koopman@vu.nl Copyright © 2016 John Wiley & Sons Ltd Creal et al. (2011, 2013) and Harvey (2013).”
Volatility Modeling with a Generalized t Distribution (2017, critical): “3 The condition jaj 1 in Lemmas 9 and 10 of Harvey (2013) is not sufficient.”
Maximum Likelihood Estimation for Generalized Autoregressive Score Models (2014, positive): “We present our results for the class of generalized autoregressive score (GAS) models which are developed by Creal et al. (2011, 2013) and Harvey (2013).”
Count Time Series: A Methodological Review (2021, background): “There is a close connection between GLARMA and scoredriven models (sometimes referred to as generalized AR score (GAS) and dynamic conditional score (DCS) models) that are well described in Harvey (2013) and Creal, Koopman, and Lucas (2013) (see also http://www.gasmodel.com for further references).”
Time-Varying Systemic Risk: Evidence From a Dynamic Copula Model of CDS Spreads (2018, methodology): “(Harvey 2013 and Harvey and Sucarrat 2014 proposed a similar method for modeling time-varying parameters, which they called a “dynamic conditional score,” or “DCS,” model.)”