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Dynamic Factor Models

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Author Info
Christian Gourieroux
Joann Jasiak

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Abstract

This paper introduces nonlinear dynamic factor models for various applications related to risk analysis. Traditional factor models represent the dynamics of processes driven by movements of latent variables, called the factors. Our approach extends this setup by introducing factors defined as random dynamic parameters and stochastic autocorrelated simulators. This class of factor models can represent processes with time varying conditional mean, variance, skewness and excess kurtosis. Applications discussed in the paper include dynamic risk analysis, such as risk in price variations (models with stochastic mean and volatility), extreme risks (models with stochastic tails), risk on asset liquidity (stochastic volatility duration models), and moral hazard in insurance analysis. We propose estimation procedures for models with the marginal density of the series and factor dynamics parameterized by distinct subsets of parameters. Such a partitioning of the parameter vector found in many applications allows to simplify considerably statistical inference. We develop a two- stage Maximum Likelihood method, called the Finite Memory Maximum Likelihood, which is easy to implement in the presence of multiple factors. We also discuss simulation based estimation, testing, prediction and filtering.

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File URL: http://www.informaworld.com/openurl?genre=article&doi=10.1081/ETC-100106997&magic=repec&7C&7C8674ECAB8BB840C6AD35DC6213A474B5
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Publisher Info
Article provided by Taylor and Francis Journals in its journal Econometric Reviews.

Volume (Year): 20 (2001)
Issue (Month): 4 ()
Pages: 385-424
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Handle: RePEc:taf:emetrv:v:20:y:2001:i:4:p:385-424

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Related research
Keywords: Nonlinear dynamics; Factor models; Stochastic volatility; Moral hazard; Stable distributions; JEL+Number:+C22> JEL Number: C22; C32; G10; G12;

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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  2. Robinson, Peter M, 1982. "On the Asymptotic Properties of Estimators of Models Containing Limited Dependent Variables," Econometrica, Econometric Society, vol. 50(1), pages 27-41, January. [Downloadable!] (restricted)
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  5. Michael K Pitt & Neil Shephard, . "Filtering via simulation: auxiliary particle filters," Economics Papers 1997-W13, Economics Group, Nuffield College, University of Oxford. [Downloadable!]
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  16. Ghysels, Eric & Gourieroux, Christian & Jasiak, Joann, 2004. "Stochastic volatility duration models," Journal of Econometrics, Elsevier, vol. 119(2), pages 413-433, April. [Downloadable!] (restricted)
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  20. Dilip B. Madan & Frank Milne, 1992. "Contingent Claims Valued and Hedged by Pricing and Investing in a Basis," Working Papers 868, Queen's University, Department of Economics.
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Nour Meddahi & Éric Renault, 2000. "Temporal Aggregation of Volatility Models," CIRANO Working Papers 2000s-22, CIRANO. [Downloadable!]
  2. Luc Bauwens & Pierre Giot & Joachim Grammig & David Veredas, 2000. "A Comparison of Financial Duration Models via Density Forecasts," Econometric Society World Congress 2000 Contributed Papers 0810, Econometric Society. [Downloadable!]
    Other versions:
  3. Matthias Fengler & Wolfgang Härdle & Enno Mammen, 2005. "A Dynamic Semiparametric Factor Model for Implied Volatility String Dynamics," SFB 649 Discussion Papers SFB649DP2005-020, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany. [Downloadable!]
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