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Questioning the news about economic growth: Sparse forecasting using thousands of news-based sentiment values
Methods for computing numerical standard errors: Review and application to Value-at-Risk estimation
Stress-testing with parametric models and Fully Flexible Probabilities
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).
Forecasting risk with Markov-switching GARCH models: A large-scale performance study
nse: Computation of numerical standard errors in R
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)).