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Ardia, David
Résultat de la recherche
Markov-switching GARCH models in R: The MSGARCH package
2019, Ardia, David, Bluteau, Keven, Boudt, Kris, Catania, Leopoldo, Trottier, Denis-Alexandre
Methods for computing numerical standard errors: Review and application to Value-at-Risk estimation
2018-7, Ardia, David, Bluteau, Keven, Hoogerheide, Lennart
Stress-testing with parametric models and Fully Flexible Probabilities
2017-1, Ardia, David, Bluteau, Keven
We propose a simple methodology to simulate scenarios from a parametric risk model while accounting for stress-test views via fully flexible probabilities (Meucci, 2010, 2013).
Questioning the news about economic growth: Sparse forecasting using thousands of news-based sentiment values
2019, Ardia, David, Bluteau, Keven, Boudt, Kris
Forecasting risk with Markov-switching GARCH models: A large-scale performance study
2018, Ardia, David, Bluteau, Keven, Boudt, Kris, Catania, Leopoldo
Regime changes in Bitcoin GARCH volatility dynamics
2019, Ardia, David, Bluteau, Keven, Ruede, Maxime
nse: Computation of numerical standard errors in R
2017-2, Ardia, David, Bluteau, Keven
nse is an R package (R Core Team (2016)) for computing the numerical standard error (NSE), an estimate of the standard deviation of a simulation result if the simulation experiment were to be repeated many times. The package provides a set of wrappers around several R packages, which give access to more than thirty estimators, including batch means estimators (Geyer (1992 Section 3.2)), initial sequence estimators (Geyer (1992 Equation 3.3)), spectrum at zero estimators (Heidelberger and Welch (1981),Flegal and Jones (2010)), heteroskedasticity and autocorrelation consistent (HAC) kernel estimators (Newey and West (1987),Andrews (1991),Andrews and Monahan (1992),Newey and West (1994),Hirukawa (2010)), and bootstrap estimators Politis and Romano (1992),Politis and Romano (1994),Politis and White (2004)).