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Second Order Filter Distribution Approximations for Financial Time Series with Extreme Outliers. (2005). Santos, Antonio ; Smith, J. Q..
In: GEMF Working Papers.
RePEc:gmf:wpaper:2005-11.

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  1. Data-driven particle Filters for particle Markov Chain Monte Carlo. (2016). McCabe, Brendan ; Leung, Patrick ; Martin, Gael M ; Forbes, Catherine S.
    In: Monash Econometrics and Business Statistics Working Papers.
    RePEc:msh:ebswps:2016-17.

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References

References cited by this document

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  4. Financial Risk Measurement for Financial Risk Management. (2012). Diebold, Francis ; Christoffersen, Peter ; Bollerslev, Tim ; Andersen, Torben.
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  6. Non-Linear Volatility Modeling of Economic and Financial Time Series Using High Frequency Data. (2011). Matei, Marius.
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  7. Modeling intraday volatility: A new consideration. (2011). Chu, Carlin C. F., ; Lam, K. P..
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  10. Bayesian non-parametric signal extraction for Gaussian time series. (2010). Macaro, Christian .
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  11. Forecast Evaluation of Explanatory Models of Financial Variability. (2009). Sucarrat, Genaro.
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  12. Volatility Models: from Conditional Heteroscedasticity to Cascades at Multiple Horizons. (2009). Subbotin, Alexandre .
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  13. Are RiskMetrics forecasts good enough? Evidence from 31 stock markets. (2009). McMillan, David G. ; Kambouroudis, Dimos .
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  14. Modeling and forecasting crude oil markets using ARCH-type models. (2009). cheong, chin.
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  17. General to specific modelling of exchange rate volatility : a forecast evaluation. (2008). Sucarrat, Genaro ; Bauwens, Luc.
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  53. What a Difference a Day Makes: On the Common Market Microstructure of Trading Days. (1998). Pohlmeier, Winfried ; Hess, Dieter ; Gerhard, Frank .
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