Estimation of realized stochastic volatility models using Hamiltonian Monte Carlo-Based methods

This study develops and compares performance of Hamiltonian Monte Carlo (HMC) and Riemann manifold Hamiltonian Monte Carlo (RMHMC) samplers with that of multi-move Metropolis-Hastings sampler to estimate stochastic volatility (SV) and realized SV models with asymmetry effect. In terms of inefficiency factor, empirical results show that the RMHMC sampler give the best performance for estimating parameters, followed by multi-move Metropolis-Hastings sampler. In particular, it is also shown that RMHMC sampler offers a greater advantage in the mixing property of latent volatility chains and in the computational time than HMC sampler.