5 research outputs found
Approaches Toward the Bayesian Estimation of the Stochastic Volatility Model with Leverage
The sampling efficiency of MCMC methods in Bayesian inference for stochastic
volatility (SV) models is known to highly depend on the actual parameter
values, and the effectiveness of samplers based on different parameterizations
varies significantly. We derive novel algorithms for the centered and the
non-centered parameterizations of the practically highly relevant SV model with
leverage, where the return process and innovations of the volatility process
are allowed to correlate. Moreover, based on the idea of
ancillarity-sufficiency interweaving (ASIS), we combine the resulting samplers
in order to guarantee stable sampling efficiency irrespective of the baseline
parameterization.We carry out an extensive comparison to already existing
sampling methods for this model using simulated as well as real world data
HitelesĂthetĹ‘ kĂ©sleltetett fĂĽggvĂ©nyek Ă©s alkalmazásaik
A diplomamunka során elĹ‘ször ismertetjĂĽk a hitelesĂthetĹ‘ kĂ©sleltetett fĂĽggvĂ©nyek (HKF) megĂ©rtĂ©sĂ©hez Ă©s használatához szĂĽksĂ©ges irodalmat, majd ez alapján implementálunk kĂ©t elĹ‘terjesztetett HKF-et. A kĂ©t HKF működĂ©sĂ©t Ă©s hatĂ©konyságát szimuláciĂłk segĂtsĂ©gĂ©vel hasonlĂtjuk össze. Az eredmĂ©nyeket grafikusan elemezzĂĽk
When it counts -- Econometric identification of the basic factor model based on GLT structures
Despite the popularity of factor models with sparse loading matrices, little
attention has been given to formally address identifiability of these models
beyond standard rotation-based identification such as the positive lower
triangular (PLT) constraint. To fill this gap, we review the advantages of
variance identification in sparse factor analysis and introduce the generalized
lower triangular (GLT) structures. We show that the GLT assumption is an
improvement over PLT without compromise: GLT is also unique but, unlike PLT, a
non-restrictive assumption. Furthermore, we provide a simple counting rule for
variance identification under GLT structures, and we demonstrate that within
this model class the unknown number of common factors can be recovered in an
exploratory factor analysis. Our methodology is illustrated for simulated data
in the context of post-processing posterior draws in Bayesian sparse factor
analysis
Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol
Stochastic volatility (SV) models are nonlinear state-space models that enjoy
increasing popularity for fitting and predicting heteroskedastic time series.
However, due to the large number of latent quantities, their efficient
estimation is non-trivial and software that allows to easily fit SV models to
data is rare. We aim to alleviate this issue by presenting novel
implementations of four SV models delivered in two R packages. Several unique
features are included and documented. As opposed to previous versions, stochvol
is now capable of handling linear mean models, heavy-tailed SV, and SV with
leverage. Moreover, we newly introduce factorstochvol which caters for
multivariate SV. Both packages offer a user-friendly interface through the
conventional R generics and a range of tailor-made methods. Computational
efficiency is achieved via interfacing R to C++ and doing the heavy work in the
latter. In the paper at hand, we provide a detailed discussion on Bayesian SV
estimation and showcase the use of the new software through various examples
Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol
Stochastic volatility (SV) models are nonlinear state-space models that enjoy increasing popularity for fitting and predicting heteroskedastic time series. However, due to the large number of latent quantities, their efficient estimation is non-trivial and software that allows to easily fit SV models to data is rare. We aim to alleviate this issue by presenting novel implementations of four SV models delivered in two R packages. Several unique features are included and documented. As opposed to previous versions, stochvol is now capable of handling linear mean models, heavy-tailed SV, and SV with leverage. Moreover, we newly introduce factorstochvol which caters for multivariate SV. Both packages offer a user-friendly interface through the conventional R generics and a range of tailor-made methods. Computational efficiency is achieved via interfacing R to C++ and doing the heavy work in the latter. In the paper at hand, we provide a detailed discussion on Bayesian SV estimation and showcase the use of the new software through various examples