288 research outputs found
Matrix-State Particle Filter for Wishart Stochastic Volatility Processes
This work deals with multivariate stochastic volatility models, which account for a time-varying variance-covariance structure of the observable variables. We focus on a special class of models recently proposed in the literature and assume that the covariance matrix is a latent variable which follows an autoregressive Wishart process. We review two alternative stochastic representations of the Wishart process and propose Markov-Switching Wishart processes to capture different regimes in the volatility level. We apply a full Bayesian inference approach, which relies upon Sequential Monte Carlo (SMC) for matrix-valued distributions and allows us to sequentially estimate both the parameters and the latent variables.Multivariate Stochastic Volatility; Matrix-State Particle Filters; Sequential Monte Carlo; Wishart Processes, Markov Switching.
Bayesian nonparametric sparse VAR models
High dimensional vector autoregressive (VAR) models require a large number of
parameters to be estimated and may suffer of inferential problems. We propose a
new Bayesian nonparametric (BNP) Lasso prior (BNP-Lasso) for high-dimensional
VAR models that can improve estimation efficiency and prediction accuracy. Our
hierarchical prior overcomes overparametrization and overfitting issues by
clustering the VAR coefficients into groups and by shrinking the coefficients
of each group toward a common location. Clustering and shrinking effects
induced by the BNP-Lasso prior are well suited for the extraction of causal
networks from time series, since they account for some stylized facts in
real-world networks, which are sparsity, communities structures and
heterogeneity in the edges intensity. In order to fully capture the richness of
the data and to achieve a better understanding of financial and macroeconomic
risk, it is therefore crucial that the model used to extract network accounts
for these stylized facts.Comment: Forthcoming in "Journal of Econometrics" ---- Revised Version of the
paper "Bayesian nonparametric Seemingly Unrelated Regression Models" ----
Supplementary Material available on reques
Beta-Product Poisson-Dirichlet Processes
Time series data may exhibit clustering over time and, in a multiple time
series context, the clustering behavior may differ across the series. This
paper is motivated by the Bayesian non--parametric modeling of the dependence
between the clustering structures and the distributions of different time
series. We follow a Dirichlet process mixture approach and introduce a new
class of multivariate dependent Dirichlet processes (DDP). The proposed DDP are
represented in terms of vector of stick-breaking processes with dependent
weights. The weights are beta random vectors that determine different and
dependent clustering effects along the dimension of the DDP vector. We discuss
some theoretical properties and provide an efficient Monte Carlo Markov Chain
algorithm for posterior computation. The effectiveness of the method is
illustrated with a simulation study and an application to the United States and
the European Union industrial production indexes
Bayesian Nonparametric Calibration and Combination of Predictive Distributions
We introduce a Bayesian approach to predictive density calibration and
combination that accounts for parameter uncertainty and model set
incompleteness through the use of random calibration functionals and random
combination weights. Building on the work of Ranjan, R. and Gneiting, T. (2010)
and Gneiting, T. and Ranjan, R. (2013), we use infinite beta mixtures for the
calibration. The proposed Bayesian nonparametric approach takes advantage of
the flexibility of Dirichlet process mixtures to achieve any continuous
deformation of linearly combined predictive distributions. The inference
procedure is based on Gibbs sampling and allows accounting for uncertainty in
the number of mixture components, mixture weights, and calibration parameters.
The weak posterior consistency of the Bayesian nonparametric calibration is
provided under suitable conditions for unknown true density. We study the
methodology in simulation examples with fat tails and multimodal densities and
apply it to density forecasts of daily S&P returns and daily maximum wind speed
at the Frankfurt airport.Comment: arXiv admin note: text overlap with arXiv:1305.2026 by other author
Efficient Gibbs Sampling for Markov Switching GARCH Models
We develop efficient simulation techniques for Bayesian inference on
switching GARCH models. Our contribution to existing literature is manifold.
First, we discuss different multi-move sampling techniques for Markov Switching
(MS) state space models with particular attention to MS-GARCH models. Our
multi-move sampling strategy is based on the Forward Filtering Backward
Sampling (FFBS) applied to an approximation of MS-GARCH. Another important
contribution is the use of multi-point samplers, such as the Multiple-Try
Metropolis (MTM) and the Multiple trial Metropolize Independent Sampler, in
combination with FFBS for the MS-GARCH process. In this sense we ex- tend to
the MS state space models the work of So [2006] on efficient MTM sampler for
continuous state space models. Finally, we suggest to further improve the
sampler efficiency by introducing the antithetic sampling of Craiu and Meng
[2005] and Craiu and Lemieux [2007] within the FFBS. Our simulation experiments
on MS-GARCH model show that our multi-point and multi-move strategies allow the
sampler to gain efficiency when compared with single-move Gibbs sampling.Comment: 38 pages, 7 figure
Interacting multiple -- Try algorithms with different proposal distributions
We propose a new class of interacting Markov chain Monte Carlo (MCMC) algorithms designed for increasing the efficiency of a modified multiple-try Metropolis (MTM) algorithm. The extension with respect to the existing MCMC literature is twofold. The sampler proposed extends the basic MTM algorithm by allowing different proposal distributions in the multipletry generation step. We exploit the structure of the MTM algorithm with different proposal distributions to naturally introduce an interacting MTM mechanism (IMTM) that expands the class of population Monte Carlo methods and builds connections with the rapidly expanding world of adaptive MCMC. We show the validity of the algorithm and discuss the choice of the selection weights and of the different proposals. We provide numerical studies which show that the new algorithm can perform better than the basic MTM algorithm and that the interaction mechanism allows the IMTM to efficiently explore the state space.Interacting Monte Carlo, Markov chain Monte Carlo, Multiple-try Metropolis, Population Monte Carlo
Italian Equity Funds: Efficiency and Performance Persistence
Have Italian mutual funds been able to generate “extra-return”? Were some of them able to persistently beat the competitors? In this paper we address these questions and provide a detailed and systematic performance and return persistence analysis of the Italian equity mutual funds. We show that, in general, fund managers have not been able to score extra-performances and only few managers had stock picking ability or market timing ability. This evidence is consistent with the market efficiency hypothesis. Moreover, concerning performance persistence, first, we cannot trace out the hot-hand phenomenon on raw returns. The no persistence effect is fairly robust to: the performance measure, the temporal lag and the different methodology employed for testing persistence. Second, there has not been long-run persistence on risk-adjusted returns (we find a weak evidence of the reversal effect). Finally, the past performance displays weak evidence of the hot-hand effect on risk-adjusted returns on four-month using cross-section tests. However, as soon as we analyse yearly intervals any evidence of persistence disappears.Mutual funds, Performance evaluation
Bayesian Inference on Dynamic Models with Latent Factors
In time series analysis, latent factors are often introduced to model the heterogeneous time evolution of the observed processes. The presence of unobserved components makes the maximum likelihood estimation method more difficult to apply. A Bayesian approach can sometimes be preferable since it permits to treat general state space models and makes easier the simulation based approach to parameters estimation and latent factors filtering. The paper examines economic time series models in a Bayesian perspective focusing, through some examples, on the extraction of the business cycle components. We briefly review some general univariate Bayesian dynamic models and discuss the simulation based techniques, such as Gibbs sampling, adaptive importance sampling and finally suggest the use of the particle filter, for parameter estimation and latent factor extraction.Bayesian Dynamic Models, Simulation Based Inference, Particle Filters, Latent Factors, Business Cycle
Bayesian Inference for Mixtures of Stable Distributions.
In many different fields such as hydrology, telecommunications, physics of condensed matter and finance, the gaussian model results unsatisfactory and reveals difficulties in fitting data with skewness, heavy tails and multimodality. The use of stable distributions allows for modelling skewness and heavy tails but gives rise to inferential problems related to the estimation of the stable distributions' parameters. Some recent works have proposed characteristic function based estimation method and MCMC simulation based estimation techniques like the MCMC-EM method and the Gibbs sampling method in a full Bayesian approach. The aim of this work is to generalise the stable distribution framework by introducing a model that accounts also for multimodality. In particular we introduce a stable mixture model and a suitable reparametrisation of the mixture, which allow us to make inference on the mixture parameters. We use a full Bayesian approach and MCMC simulation techniques for the estimation of the posterior distribution. Finally we propose some applications of stable mixtures to financial data.Mixture model; Stable distributions; Gibbs sampling; Bayesian inference;
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