Publication: ABC and Hamiltonian Monte-Carlo methods in COGARCH models
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2016-01
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Abstract
The analysis of financial series, assuming calendar effects and unequally spaced times over continuous time, can be studied by means of COGARCH models based on Lévy processes. In order to estimate the COGARCH model parameters, we propose to use two different Bayesian approaches. First, we suggest to use a Hamiltonian Montecarlo (HMC) algorithm that improves the performance of standard MCMC methods. Secondly, we introduce an Approximate Bayesian Computational (ABC) methodology which allows to work with analytically infeasible or computationally expensive likelihoods. After a simulation and comparison study for both methods, HMC and ABC, we apply them to model the behaviour of some NASDAQ time series and we discuss the results.
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Approximate Bayesian Computation methods (ABC), Bayesian inference, COGARCH model, Continuous-time GARCH process, Hamiltonian Monte Carlo methods (HMC), Lévy process