Model Selection and Adaptive Markov chain Monte Carlo for Bayesian Cointegrated VAR model

This paper develops a matrix-variate adaptive Markov chain Monte Carlo (MCMC) methodology for Bayesian Cointegrated Vector Auto Regressions (CVAR). We replace the popular approach to sampling Bayesian CVAR models, involving griddy Gibbs, with an automated efficient alternative, based on the Adaptive Metropolis algorithm of Roberts and Rosenthal, (2009). Developing the adaptive MCMC framework for Bayesian CVAR models allows for efficient estimation of posterior parameters in significantly higher dimensional CVAR series than previously possible with existing griddy Gibbs samplers. For a n-dimensional CVAR series, the matrix-variate posterior is in dimension $3n^2 + n$, with significant correlation present between the blocks of matrix random variables. We also treat the rank of the CVAR model as a random variable and perform joint inference on the rank and model parameters. This is achieved with a Bayesian posterior distribution defined over both the rank and the CVAR model parameters, and inference is made via Bayes Factor analysis of rank. Practically the adaptive sampler also aids in the development of automated Bayesian cointegration models for algorithmic trading systems considering instruments made up of several assets, such as currency baskets. Previously the literature on financial applications of CVAR trading models typically only considers pairs trading (n=2) due to the computational cost of the griddy Gibbs. We are able to extend under our adaptive framework to $n >> 2$ and demonstrate an example with n = 10, resulting in a posterior distribution with parameters up to dimension 310. By also considering the rank as a random quantity we can ensure our resulting trading models are able to adjust to potentially time varying market conditions in a coherent statistical framework.Comment: to appear journal Bayesian Analysi

Scaling analysis of FLIC fermion actions

The Fat Link Irrelevant Clover (FLIC) fermion action is a variant of the $O(a)$-improved Wilson action where the irrelevant operators are constructed using smeared links. While the use of such smearing allows for the use of highly improved definitions of the field strength tensor $F_{\mu\nu},$ we show that the standard 1-loop clover term with a mean field improved coefficient $c_{\rm sw}$ is sufficient to remove the $O(a)$ errors, avoiding the need for non-perturbative tuning. This result enables efficient dynamical simulations in QCD with the FLIC fermion action.Comment: 5 pages, 3 figure

Isolating the Roper Resonance in Lattice QCD

We present results for the first positive parity excited state of the nucleon, namely, the Roper resonance ($N^{{{1/2}}^{+}}$=1440 MeV) from a variational analysis technique. The analysis is performed for pion masses as low as 224 MeV in quenched QCD with the FLIC fermion action. A wide variety of smeared-smeared correlation functions are used to construct correlation matrices. This is done in order to find a suitable basis of operators for the variational analysis such that eigenstates of the QCD Hamiltonian may be isolated. A lower lying Roper state is observed that approaches the physical Roper state. To the best of our knowledge, the first time this state has been identified at light quark masses using a variational approach.Comment: 7pp, 4 figures; minor typos corrected and one Ref. adde

Preconditioning Maximal Center Gauge with Stout Link Smearing in SU(3)

Center vortices are studied in SU(3) gauge theory using Maximal Center Gauge (MCG) fixing. Stout link smearing and over-improved stout link smearing are used to construct a preconditioning gauge field transformation, applied to the original gauge field before fixing to MCG. We find that preconditioning successfully achieves higher gauge fixing maxima. We observe a reduction in the number of identified vortices when preconditioning is used, and also a reduction in the vortex-only string tension.Comment: 9 pages, 4 figure

Baryon spectroscopy from lattice QCD.

This thesis investigates the spectrum of baryon resonances in quenched lattice QCD.Thesis (Ph.D.) -- University of Adelaide, School of Chemistry and Physics, 200

Model Selection and Adaptive Markov chain Monte Carlo for Bayesian Cointegrated VAR model

This paper develops a matrix-variate adaptive Markov chain Monte Carlo (MCMC) methodology for Bayesian Cointegrated Vector Auto Regressions (CVAR). We replace the popular approach to sampling Bayesian CVAR models, involving griddy Gibbs, with an automated efficient alternative, based on the Adaptive Metropolis algorithm of Roberts and Rosenthal, (2009). Developing the adaptive MCMC framework for Bayesian CVAR models allows for efficient estimation of posterior parameters in significantly higher dimensional CVAR series than previously possible with existing griddy Gibbs samplers. For a n-dimensional CVAR series, the matrix-variate posterior is in dimension $3n^2 + n$, with significant correlation present between the blocks of matrix random variables. We also treat the rank of the CVAR model as a random variable and perform joint inference on the rank and model parameters. This is achieved with a Bayesian posterior distribution defined over both the rank and the CVAR model parameters, and inference is made via Bayes Factor analysis of rank. Practically the adaptive sampler also aids in the development of automated Bayesian cointegration models for algorithmic trading systems considering instruments made up of several assets, such as currency baskets. Previously the literature on financial applications of CVAR trading models typically only considers pairs trading (n=2) due to the computational cost of the griddy Gibbs. We are able to extend under our adaptive framework to $n >> 2$ and demonstrate an example with n = 10, resulting in a posterior distribution with parameters up to dimension 310. By also considering the rank as a random quantity we can ensure our resulting trading models are able to adjust to potentially time varying market conditions in a coherent statistical framework.