Noising the GARCH Volatility: A Random Coefficient GARCH Model

Maximum Likelihood Estimation and Forecasting Performance of the GARCH Model Based on the Geometric Measure of Variation

Volatility models in the GARCH family typically build the conditional scale from an arithmetic average of squared or absolute deviations, an averaging rule that is disproportionately sensitive to large shocks and, through the triangular inequality, tends to systematically overstate the true average displacement of retu...

Bayesian inference, on-line forecasting and model choice for large VAR models with Cholesky stochastic volatility

This work introduces a Markov chain Monte Carlo (MCMC) kernel that mixes better than existing samplers at the same computational complexity, and introduces a Sequential Monte Carlo squared sampler, which delivers at every $t$ the one-step-ahead predictive density and the marginal likelihood of the data up to $t, and he...

Multivariate Stochastic Volatility under the Assumption of Stochastic Volatility of Volatility

Multivariate stochastic volatility (MSV) models typically allow volatility to evolve stochastically but do not explicitly account for stochastic volatility of volatility. The main aim of this paper is to develop nonparametric MSV models under the assumption of stochastic volatility of volatility. This study also aims t...

GARCH Model Based on the Geometric Measure of Variation

Every model in the GARCH family, from the original specification of Bollerslev (1986) to its many asymmetric, long-memory, and machine-learning-hybrid descendants, updates the conditional variance through an arithmetic average of squared or absolute deviations. That averaging rule rests on the one-sided triangular ineq...

Parameter Sensitivity Analysis of the He-ston Stochastic Volatility Model

Implied Probability Measures of Volatility ∗