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Discretised Non-Linear Filtering for Dynamic Latent Variable Models: with Application to Stochastic Volatility

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Author Info

  • Scott I. White
  • Adam E. Clements
  • Stan Hurn

Abstract

Filtering techniques are often applied to the estimation of dynamic latent variable models. However, these techniques are often based on a set assumptions which restrict models to be specified in a linear state-space form. Numerical filtering techniques have been propsed that avoid invoking such restrictive assumptions, thus permitting a wider class of latent variable models to be considered. This paper proposes an accurate yet computationally efficient numerical filtering algorithm (based on a discretisation of the state space) for estimating the general class of dynamic latent variable models. The empirical performance of this algorithm is considered within the context of the stochastic volatility model. It is found that the proposed algorithm outperforms a number of accepted procedures in terms of volatility forecasti

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Bibliographic Info

Paper provided by Econometric Society in its series Econometric Society 2004 Australasian Meetings with number 46.

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Date of creation: 11 Aug 2004
Date of revision:
Handle: RePEc:ecm:ausm04:46

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Keywords: Non-linear filtering; latent variable models; stochastic volatility; volatilitry forecasting;

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References

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  1. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 69-87, January.
  2. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999. "(Understanding, Optimizing, Using and Forecasting) Realized Volatility and Correlation," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-061, New York University, Leonard N. Stern School of Business-.
  3. GHYSELS, Eric & HARVEY, Andrew & RENAULT, Eric, 1995. "Stochastic Volatility," CORE Discussion Papers 1995069, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  4. Neil Shephard, 2005. "Stochastic volatility," Economics Series Working Papers 2005-W17, University of Oxford, Department of Economics.
  5. Andersen, Torben G, 1996. " Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility," Journal of Finance, American Finance Association, vol. 51(1), pages 169-204, March.
  6. Harvey, Andrew & Ruiz, Esther & Shephard, Neil, 1994. "Multivariate Stochastic Variance Models," Review of Economic Studies, Wiley Blackwell, vol. 61(2), pages 247-64, April.
  7. Tauchen, George E & Pitts, Mark, 1983. "The Price Variability-Volume Relationship on Speculative Markets," Econometrica, Econometric Society, vol. 51(2), pages 485-505, March.
  8. Torben G. Andersen & Tim Bollerslev & Nour Meddahi, 2002. "Correcting the Errors: A Note on Volatility Forecast Evaluation Based on High-Frequency Data and Realized Volatilities," CIRANO Working Papers 2002s-91, CIRANO.
  9. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2001. "Modeling and Forecasting Realized Volatility," NBER Working Papers 8160, National Bureau of Economic Research, Inc.
  10. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-55, January.
  11. Watanabe, Toshiaki, 1999. "A Non-linear Filtering Approach to Stochastic Volatility Models with an Application to Daily Stock Returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(2), pages 101-21, March-Apr.
  12. Ruiz, Esther, 1994. "Quasi-maximum likelihood estimation of stochastic volatility models," Journal of Econometrics, Elsevier, vol. 63(1), pages 289-306, July.
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Cited by:
  1. Adam Clements & Scott White, 2005. "Non-linear filtering with state dependant transition probabilities: A threshold (size effect) SV model," School of Economics and Finance Discussion Papers and Working Papers Series 191, School of Economics and Finance, Queensland University of Technology.
  2. Ralph D. Snyder & Gael M. Martin & Phillip Gould & Paul D. Feigin, 2007. "An Assessment of Alternative State Space Models for Count Time Series," Monash Econometrics and Business Statistics Working Papers 4/07, Monash University, Department of Econometrics and Business Statistics.
  3. Adam Clements & Scott White, 2005. "Nonlinear Filtering for Stochastic Volatility Models with Heavy Tails and Leverage," School of Economics and Finance Discussion Papers and Working Papers Series 192, School of Economics and Finance, Queensland University of Technology.
  4. Ralf Becker & Adam Clements & Christopher Coleman-Fenn, 2009. "Forecast performance of implied volatility and the impact of the volatility risk premium," NCER Working Paper Series 45, National Centre for Econometric Research.

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