Financial Risk Management with Bayesian Estimation of GARCH Models: Theory and Applications

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GARCH Models (2010, unknown): “Evaluation of VaR at horizons longer than 1, under GARCH dynamics, is discussed by Ardia (2008).”
Zero variance Markov chain Monte Carlo for Bayesian estimators (2010, methodology): “In the first stage a short MCMC simulation ([3]) is used to estimate the optimal parameters of the trial function (2000 sweeps after 1000 burn-in).”
Time-dependent series variance learning with recurrent mixture density networks (2013, methodology): “obtained by relevant research: α0 1⁄4 0:0108, α1 1⁄4 0:1531 and β1⁄4 0:80597 [2].”
Regime changes in Bitcoin GARCH volatility dynamics (2019, unknown): “Also, we note that for both the squared and absolute returns, the volatility persistence implied by the model is lower than for the single–regime model, indicating that the regime–switching specification is able to deal with the spurious integrated GARCH effect observed with single–regime models in ”
Forecasting risk with Markov-switching GARCH models:A large-scale performance study (2018, methodology): “Ardia, 2008; Marcucci, 2005). The initial studies of Markov-switching autoregressive heteroscedastic models applied to financial time series focused on ARCH specifications, and thus omitted a lagged value of the conditional variance in the variance equation (Cai, 1994; Hamilton & Susmel, 1994). The ”