Copula Methods for Forecasting Multivariate Time Series

By Andrew J. Patton. 2013

314 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

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Citations in the corpus

A dependent multiplier bootstrap for the sequential empirical copula process under strong mixing (2013, unknown): “The multiplier bootstrap of Rémillard and Scaillet (2009) that has been adopted as a resampling technique in the case of i.”
Copulas in Econometrics (2014, methodology): “We refer interested readers to Patton (2013) for a detailed review of copula-based methods for economic forecasting and for empirical examples illustrating some commonly used methods and to Fan et al. (2013) for a systematic treatment of partial identification and inference for parameters that…”
High-Dimensional Copula-Based Distributions with Mixed Frequency Data (2016, unknown): “See Patton (2012) for more details on implementing in-sample and out-of-sample tests for copula-based models. a rich set of models for studying the dependence of uncorrelated, but dependent, variables.”
Forecasting: theory and practice (2020, background): “…on copula introduction (Nelsen, 2006; Trivedi and Zimmer, 2007; Joe, 1997), copula modelling techniques (Durante and Sempi, 2015), vine copulas (Joe, 2014), review on frequentist approaches for copula-based forecasting (Patton, 2013), see the aforementioned references and the references therein.”
Vine Copula Specifications for Stationary Multivariate Markov Chains (2015, methodology): “See, for instance, either of the textbooks by Cherubini et al. (2004, 2012), or any of the three review articles by Patton (2009, 2012, 2013).”
Modeling Dependence in High Dimensions With Factor Copulas (2015, methodology): “…Christoffersen et al. (2012) combined 2For general reviews of copulas in economics and finance, see Cherubini, Luciano, and Vecchiato (2004) and Patton (2012). a skew t copula with a DCC model for conditional correlations in their study of 33 developed and emerging equity market indices, and…”
Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting (2023, background): “MTS forecasting benefits the decision-making of downstream tasks and is widely used in many fields including weather (Angryk et al., 2020), energy (Demirel et al., 2012), finance (Patton, 2013), etc.”
Extreme risk spillovers between crude oil and stock markets (2015, background): “The skewed t distribution is widely used to describe financial returns in the literate (e.g., Patton, 2013; Wen et al., 2012).”