Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation

By Robert F. Engle. 1982

Subfields: Statistics and Probability, Economics & Operations Research

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Citations in the corpus

GAN Inversion: A Survey (2021, background): “, each dimension in a vector variable is conditioned on the previous dimensions like ARMA [171] or ARCH [172], this is an autoregressive flow.”
FFORMA: Feature-based forecast model averaging (2020, methodology): “LM statistic was calculated based on the Lagrange Multiplier test of Engle (1982) for autoregressive conditional heteroscedasticity (ARCH).”
Deep Learning in Finance (2016, background): “For areas of finance applications, see Fama and French (1992, 2008), Engle (1982), Campbell, Lo, and MacKinley (1997), Singleton (2006), and Daniel and Titman (2006).”
Momentum has its moments (2015, background): “One possible cause for excess kurtosis is time-varying risk [see, for example, Engle (1982) and Bollerslev (1987)].”
Gaussian inference on certain long-range dependent volatility models (2003, methodology): “Indeed, since the introduction of the autoregressive conditional heteroskedasticity (ARCH) model of Engle (1982), a great deal of research has focused on modelling the volatility of financial asset returns as they feature very complicated dynamics, synthesized in a number of well-known stylized…”
Disagreement and Biases in Inflation Expectations (2006, unknown): “(17) Starting from Engle (1982), such ARCH models have been used extensively to estimate timevariations in the conditional variance of inflation.”
Estimating spot volatility with high-frequency financial data (2014, background): “Later on, researchers tended to estimate volatility in the context of the ARCH model (Engle (1982)), the GARCH model (Bollerslev (1986)), and their numerous variations.”
Heterogeneous Agent Models in Economics and Finance, In: Handbook of Computational Economics II: Agent-Based Computational Economics, edited by Leigh Tesfatsion and Ken Judd, Elsevier, Amsterdam 2006, pp.1109-1186 (2005, background): “In time series econometrics the class of (generalized) autoregressive conditional hetereroskedastic (G)ARCH-models, pioneered by Engle (1982), has become very popular to describe volatility clustering.”
Price reaction, volatility timing and funds’ performance during Covid-19 (2020, unknown): “…exposure of volatility clustering, we decided to modify our returns equation by adopting a GARCH-in-Mean approach following the same stance initially taken by Bollerslev et al. (1988), Engle (1982) and further followed and endorsed by Anyfantaki and Demos (2016), Dias (2017), Ng (1991) and others.”
Measuring nonlinear dependence in time‐series, a distance correlation approach (2012, methodology): “Prominent examples include Brillinger (1977), Engle (1982), Ashley et al. (1986), Priestley (1988), Tong (1990), Chen and Tsay (1993), Franses and van Dijk (2000), Fan and Yao (2003), Kantz and Schreiber (2000), Tjøstheim and Auestad (1994a, b) and Brockwell (2007), among others.”
Autoregressive Conditional Kurtosis (2005, methodology): “The standard ARCH and GARCH models introduced by Engle (1982) and Bollerslev (1986) respectively allow normally distributed disturbances to have time varying (conditional) variance.”
Asymptotic theory for multivariate GARCH processes (2003, unknown): “Similar conditions for identification can be set for higher order BEKK processes, see Engle and Kroner [14].”
Efficient estimation of a multivariate multiplicative volatility model (2010, background): “1 Introduction Modelling volatility is a big industry after the seminal work of Engle (1982) and Bollerlsev (1986).”