Empirical Analysis of Stochastic Volatility Model by Hybrid Monte Carlo Algorithm

Bayesian Inference of Stochastic volatility Model by Hybrid Monte Carlo

The hybrid Monte Carlo (HMC) algorithm is applied for the Bayesian inference of the stochastic volatility (SV) model. We use the HMC algorithm for the Markov chain Monte Carlo updates of volatility variables of the SV model. First we compute parameters of the SV model by using the artificial financial data and compare the results from the HMC algorithm with those from the Metropolis algorithm. ...

Monte Carlo Simulation of a Two-Factor Stochastic Volatility Model

Efficient Monte Carlo Simulation with Stochastic Volatility

Monte Carlo simulation is a powerful aid in many fields. In this thesis it is used for pricing of financial derivatives. Achieving accurate results with Monte Carlo is rather timeconsuming due to its slow convergence. However, there are ways to improve the accuracy of each simulation, for instance by reducing the inevitable discretization bias. In our financial context, this concerns the discre...

Hybrid, Multiscale Monte Carlo Algorithm for Simulating Stochastic Systems

INTRODUCTION Accurately relating the physiological outcome of a cell to the molecular events, based on an in silico analysis of intracellular networks, requires not only a precise knowledge of the network, but also an appropriate simulation technique. Traditionally, the methods used to temporally evolve intracellular networks have been deterministic in nature. However, deterministic methods can...

A Stochastic Monte Carlo Algorithm

We propose a Monte Carlo algorithm to promote Kennedy and Kuti’s linear accept/reject algorithm which accommodates unbiased stochastic estimates of the probability to an exact one. The probability upper bound violations are avoided by adopting the Metropolis accept/reject steps for both the dynamical and noise configurations and the lower bound violations can be absorbed into the observables. W...