177,635 research outputs found
Structural changes in large economic datasets: A nonparametric homogeneity test
No full textThis paper proposes a Bayesian nonparametric homogeneity test for distributional changes. We provide an asymptotic approximation of the Bayes factor and show that it is related to the Shannon entropy. The proposed test is suitable for large high-dimensional datasets which otherwise require time-consuming computation for posterior approximation. An analysis on the FRED-QD macroeconomic dataset shows the ability of the test to detect relevant structural changes in the US economy
Ruthenium Carbonyl 1,4-diaza-1,3-butadiene (r-dab) Complexes - A Theoretical and Experimental Investigation of the Electronic-structure of Ru2(CO)4(r-dab)(mu-CO) and Ru2(CO)4(r-dab)(mu-hc=ch)
No full textThe electronic structure of two binuclear ruthenium clusters containing the 8e donor 1,4-diaza-l,3-butadiene (R-DAB)
ligand [Ru, (CO) , (R-DAB) (p-CO) and Ru,(CO),(R-DAB)(pHC=CH)] is for the first t ime discussed by using SCF first
principle discrete variational ( D V ) X a calculations and gas-phase U V photoelectron (PE) spectroscopy. Despite t h e shor t
metal-metal interatomic distance, the bonding scheme of both molecules is dominated by the absence of any direct metal-metal
bond. On the contrary, the importance of a strong multicentered interaction involving the p-bridged ligands has been emphasized.
As f a r a s the R -DAB moiety is concerned, theoretical results indicated a poorer involvement of n + and n- linear combinations
of nitrogen lone pairs than rj* a n d r2 levels in metals-R-DAB interactions. Looking into t h e na tur e of such a r interaction
it has been found t h a t t h e R u a tom of the metallacycle f r agment is mostly involved in M - r3* back-bonding while r2 - M donation mainly involves t h e second R u a to
Parallel Sequential Monte Carlo for Efficient Density Combination: The Deco Matlab Toolbox
No full textThis paper presents the MATLAB package DeCo (density combination) which is based on the paper by Billio, Casarin, Ravazzolo, and van Dijk (2013) where a constructive Bayesian approach is presented for combining predictive densities originating from different models or other sources of information. The combination weights are time-varying and may depend on past predictive forecasting performances and other learning mechanisms. The core algorithm is the function DeCo which applies banks of parallel Sequential Monte Carlo algorithms to filter the time-varying combination weights. The DeCo procedure has been implemented both for standard CPU computing
and for graphical process unit (GPU) parallel computing. For the GPU implementation we use the MATLAB parallel computing toolbox and show how to use general purposes GPU computing almost effortless. This GPU implementation comes with a speed up of the execution time up to seventy times compared to a standard CPU MATLAB mplementation on a multicore CPU. We show the use of the package and the computational gain of the GPU version, through some simulation experiments and empirical applications
Simulation Methods for Nonlinear and Non-Gaussian Models in Finance, Premio SIE
No full textFinancial variables, such as asset returns in international stock and bond markets or interest rates in the liquidity market, often exhibit a heterogeneous time evolution, with a unconditional density characterised by heavy tails, skewness, multimodality and time changing volatility. Through an empirical study, all these features appear clearly in some financial indexes sampled with
monthly frequency and become more evident when data are collected with a higher frequency (i.e. weekly, daily or intra-day frequencies). Gaussian distribution and linear dynamic assumptions reveal unsatisfactory in many financial applications like asset pricing, risk measurement and management. Nonlinear and non-Gaussian models have been introduced in finance in order to come to more attractive results. Many stochastic models are now available as alternatives to the linear and Gaussian ones. But all of them are generally difficult to handle and represent challenging problems in applied mathematics. Some recent works (see for example Doucet, Freitas and Gordon [7], Robert and Casella [9] and Del Moral [6]) highlight the ability of the Monte Carlo simulation methods in solving optimisation and integration problems, which arise in treating complex probabilistic models and suggest moreover a Bayesian approach to optimal decision and inference making.
Within the simulation based inference framework the Bayesian approach has been widely applied in many recent studies, due to the natural way the Monte Carlo approximation can enter into
the inference procedure. The Bayesian framework accounts for prior information about the parameters and allows to treat complex models, such as mixtures of distributions, stochastic
volatility and stochastic trend models. For an introduction to the basic and more advanced simulation methods we refer the interested reader to Robert and Casella [9], Doucet, Freitas
and Gordon [7] and Liu [8]
Solution Manual for Selected Problems, The Bayesian Choice, 2nd Ed. and Paperback Ed., C. P. Robert.Springer Verlag
No full textBayesian Inference for Generalised Markov Switching Stochastic Volatility Models
No full textWe study a Markov switching stochastic volatility model with heavy tail innovations in the
observable process. Due to the economic interpretation of the hidden volatility regimes,
these models have many nancial applications like asset allocation, option pricing and risk
management. The Markov switching process is able to capture clustering e ects and jumps
in volatility. Heavy tail innovations account for extreme variations in the observed process.
Accurate modelling of the tails is important when estimating quantiles is the major interest
like in risk management applications. Moreover we follow a Bayesian approach to ltering
and estimation, focusing on recently developed simulation based ltering techniques, called
Particle Filters. Simulation based lters are recursive techniques, which are useful when
assuming non-linear and non-Gaussian latent variable models and when processing data
sequentially. They allow to update parameter estimates and state ltering as new observations
become available.ou
Bayesian Monte Carlo Filtering for Stochastic Volatility Models
Get PDFModelling of the fi nancial variable evolution represents an important issue in financial econometrics. Stochastic dynamic models allow to describe more accurately many features of
the financial variables, but often there exists a trade-off between the modelling accuracy and
the complexity. Moreover the degree of complexity is increased by the use of latent factors
which are usually introduced in time series analysis, in order to capture the heterogeneous
time evolution of the observed process. The presence of unobserved components makes
the maximum likelihood inference more difficult to apply. Thus the Bayesian approach is
preferable since it allows to treat general state space models and makes easier the simulation
based approach to parameters estimation and latent factors filtering. The main aim of this
work is to produce an updated review of Bayesian inference approaches for latent factor
models. Moreover, we provide a review of simulation based filtering methods in a Bayesian
perspective focusing, through some examples, on stochastic volatility models.ou
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