Second Order Filter Distribution Approximations for Financial Time Series with Extreme Outliers
AbstractParticle Filters are now regularly used to obtain the filter distributions associated with state space financial time series. Most commonly used nowadays is the auxiliary particle filter method in conjunction with a first order Taylor expansion of the log-likelihood. We argue in this paper that for series such as stock returns, which exhibit fairly frequent and extreme outliers, filters based on this first order approximation can easily break down. However, an auxiliary particle filter based on the much more rarely used second order approximation appears to perform well in these circumstances. To detach the issue of algorithm design from problems related to model misspecification and parameter estimation, we demonstrate the lack of robustness of the first order approximation and the feasibility of a specific second order approximation using simulated data.
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Bibliographic InfoPaper provided by GEMF - Faculdade de Economia, Universidade de Coimbra in its series GEMF Working Papers with number 2005-11.
Length: 36 pages
Date of creation: 2005
Date of revision:
Publication status: Published in Journal of Business and Economic Statistics, 24(3): 329-337, 2006.
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Bayesian inference; Importance sampling; Particle filter; State space model; Stochastic volatility.;
Other versions of this item:
- Smith, J.Q. & Santos, Antonio A.F., 2006. "Second-Order Filter Distribution Approximations for Financial Time Series With Extreme Outliers," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 329-337, July.
- J. Q. Smith & António Santos, 2003. "Second Order Filter Distribution Approximations for Financial Time Series with Extreme Outlier," GEMF Working Papers 2003-03, GEMF - Faculdade de Economia, Universidade de Coimbra.
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- Yacine Aït-Sahalia, .
"Dynamic Equilibrium and Volatility in Financial Asset Markets,"
CRSP working papers
331, Center for Research in Security Prices, Graduate School of Business, University of Chicago.
- Ait-Sahalia, Yacine, 1998. "Dynamic equilibrium and volatility in financial asset markets," Journal of Econometrics, Elsevier, vol. 84(1), pages 93-127, May.
- Yacine Ait-Sahalia, 1996. "Dynamic Equilibrium and Volatility in Financial Asset Markets," NBER Working Papers 5479, National Bureau of Economic Research, Inc.
- Peter F. Christoffersen & Francis X. Diebold, 1998.
"How Relevant is Volatility Forecasting for Financial Risk Management?,"
New York University, Leonard N. Stern School Finance Department Working Paper Seires
98-080, New York University, Leonard N. Stern School of Business-.
- Peter F. Christoffersen & Francis X. Diebold, 2000. "How Relevant is Volatility Forecasting for Financial Risk Management?," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 12-22, February.
- Peter F. Christoffersen & Francis X. Diebold, 1997. "How Relevant is Volatility Forecasting for Financial Risk Management?," Center for Financial Institutions Working Papers 97-45, Wharton School Center for Financial Institutions, University of Pennsylvania.
- Peter F. Christoffersen & Francis X. Diebold, 1998. "How Relevant is Volatility Forecasting for Financial Risk Management?," NBER Working Papers 6844, National Bureau of Economic Research, Inc.
- Francis X. Diebold & Andrew Hickman & Atsushi Inoue & Til Schuermann, 1997. "Converting 1-Day Volatility to h-Day Volatitlity: Scaling by Root-h is Worse Than You Think," Center for Financial Institutions Working Papers 97-34, Wharton School Center for Financial Institutions, University of Pennsylvania.
- Andersen, Torben G. & Bollerslev, Tim & Lange, Steve, 1999. "Forecasting financial market volatility: Sample frequency vis-a-vis forecast horizon," Journal of Empirical Finance, Elsevier, vol. 6(5), pages 457-477, December.
- Pitt, Michael K. & Silva, Ralph dos Santos & Giordani, Paolo & Kohn, Robert, 2012. "On some properties of Markov chain Monte Carlo simulation methods based on the particle filter," Journal of Econometrics, Elsevier, vol. 171(2), pages 134-151.
- David N. DeJong & Hariharan Dharmarajan & Roman Liesenfeld & Guilherme Moura & Jean-Francois Richard, 2007.
"Efficient Likelihood Evaluation in State-Space Representations,"
374, University of Pittsburgh, Department of Economics, revised Dec 2008.
- David N. DeJong & Hariharan Dharmarajan & Roman Liesenfeld & Guilherme Moura & Jean-Francois Richard, 2009. "Efficient Likelihood Evaluation of State-Space Representations," Working Papers 2009/15, Czech National Bank, Research Department.
- DeJong, David Neil & Dharmarajan, Hariharan & Liesenfeld, Roman & Moura, Guilherme V. & Richard, Jean-François, 2009. "Efficient likelihood evaluation of state-space representations," Economics Working Papers 2009,02, Christian-Albrechts-University of Kiel, Department of Economics.
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