nep-ets New Economics Papers
on Econometric Time Series
Issue of 2008‒10‒13
six papers chosen by
Yong Yin
SUNY at Buffalo

  1. Matrix-State Particle Filter for Wishart Stochastic Volatility Processes By Roberto Casarin; Domenico sartore
  2. Modeling Volatility Spillovers between the Variabilities of US Ingflation and Output: the UECCC GARCH Model By Christian Conrad; Menelaos Karanasos
  3. Particle Filters for Markov-Switching Stochastic-Correlation Models By Gianni Amisano; Roberto Casarin
  4. Identifying Business Cycle Turning Points with Sequential Monte Carlo Methods By Monica Billio; Roberto Casarin
  5. Fitting vast dimensional time-varying covariance models By Robert F. Engle; Neil Shephard; Kevin Sheppard
  6. Discrete time-series models when counts are unobservable By T M Christensen; A S Hurn; K A Lindsay

  1. By: Roberto Casarin; Domenico sartore
    Abstract: 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.
    Date: 2008
  2. By: Christian Conrad (University of Heidelberg, Department of Economics); Menelaos Karanasos (Economics and Finance, Brunel University)
    Abstract: This paper employs the unrestricted extended constant conditional correlation GARCH specification proposed in Conrad and Karanasos (2008) to examine the intertemporal relationship between the uncertainties of inflation and output growth in the US. We find that inflation uncertainty effects output variability positively, while output variability has a negative effect on inflation uncertainty.
    Keywords: Bivariate GARCH process, negative volatility feedback, inflation uncertainty, output variability
    JEL: C32 C51 E31
    Date: 2008–09
  3. By: Gianni Amisano; Roberto Casarin
    Abstract: This work deals with multivariate stochastic volatility models that account for time-varying stochastic correlation between the observable variables. We focus on the bivariate models. A contribution of the work is to introduce Beta and Gamma autoregressive processes for modelling the correlation dynamics. Another contribution f our work is to allow the parameter of the correlation process to be governed by a Markov-switching process. Finally we propose a simulation-based Bayesian approach, called regularised sequential Monte Carlo. This framework is suitable for on-line estimation and the model selection.
    Date: 2008
  4. By: Monica Billio; Roberto Casarin
    Abstract: We apply sequential Monte Carlo (SMC) to the detection of turning points in the business cycle and to the evaluation of useful statistics employed in business cycle analysis. The proposed nonlinear filtering method is very useful for sequentially estimating the latent variables and the parameters of nonlinear and non-Gaussian time-series models, such as the Markov-switching (MS) models studied in this work. We show how to combine SMC with Monte Carlo Markov Chain for estimating time series models with MS latent factors. We illustrate the effectiveness of the methodology and measure, in a full Bayesian and realtime context, the ability of a pool of MS models to identify turning points in the European economic activity. We also compare our results with the business cycle datation existing in the literature and provide a sequential evaluation of the forecast accuracy of the competing MS models.
    Date: 2008
  5. By: Robert F. Engle; Neil Shephard; Kevin Sheppard
    Abstract: Building models for high dimensional portfolios is important in risk management and asset allocation. Here we propose a novel and fast way of estimating models of time-varying covariances that overcome an undiagnosed incidental parameter problem which has troubled existing methods when applied to hundreds or even thousands of assets. Indeed we can handle the case where the cross-sectional dimension is larger than the time series one. The theory of this new strategy is developed in some detail, allowing formal hypothesis testing to be carried out on these models. Simulations are used to explore the performance of this inference strategy while empirical examples are reported which show the strength of this method. The out of sample hedging performance of various models estimated using this method are compared.
    Keywords: ARCH Models, Composite Likelihood, Dynamic Conditional Correlations, Incidental Parameters, Quasi-Likelihood, Time-Varying Covariances
    JEL: C14 C32
    Date: 2008
  6. By: T M Christensen (QUT); A S Hurn (QUT); K A Lindsay (University of Glasgow)
    Abstract: Count data in economics have traditionally been modeled by means of integer-valued autoregressive models. Consequently, the estimation of the parameters of these models and their asymptotic properties have been well documented in the literature. The models comprise a description of the survival of counts generally in terms of a binomial thinning process and an independent arrivals process usually specified in terms of a Poisson distribution. This paper extends the existing class of models to encompass situations in which counts are latent and all that is observed is the presence or absence of counts. This is a potentially important modification as many interesting economic phenomena may have a natural interpretation as a series of ‘events’ that are driven by an underlying count process which is unobserved. Arrivals of the latent counts are modeled either in terms of the Poisson distribution, where multiple counts may arrive in the sampling interval, or in terms of the Bernoulli distribution, where only one new arrival is allowed in the same sampling interval. The models with latent counts are then applied in two practical illustrations, namely, modeling volatility in financial markets as a function of unobservable ‘news’ and abnormal price spikes in electricity markets being driven by latent ‘stress’.
    Keywords: Integer-valued autoregression, Poisson distribution, Bernoulli distribution, latent factors, maximum likelihood estimation
    JEL: C13 C25 C32
    Date: 2008–09–15

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