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DTSTART:20230326T010000
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DTSTART;TZID=Europe/Helsinki:20231109T100000
DTEND;TZID=Europe/Helsinki:20231109T110000
DTSTAMP:20260726T105604
CREATED:20230725T113028Z
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SUMMARY:Paolo GORGI (Amsterdam University) "Conditional score residuals and diagnostic analysis of serial dependence in time series models"
DESCRIPTION:Finance & Financial Econometrics : \nTime: 10.00 am\nDate: 09th of Novembre 2023\nRoom 3001 \nPaolo GIORGI (Amsterdam University) “Conditional score residuals and diagnostic analysis of serial dependence in time series models” \nAbstract : This paper introduces conditional score residuals and it provides a general framework for the diagnostic analysis of time series models. Conditional score residuals encompass standard definitions of residuals that are typically used in time series models. ARMA residuals\, squared residuals and Pearson residuals are special cases of conditional score residuals when the conditional distribution of the model belongs to the exponential family. Instead\, conditional score residuals provide an alternative definition of residuals when the conditional distribution is not of the exponential type. A key feature of conditional score residuals is that they account for the shape of the conditional distribution. This feature leads to more reliable and powerful diagnostic tools for testing residual autocorrelation. Furthermore\, they can be employed in complex models where it may not be clear how to define residuals. The asymptotic properties of the empirical autocorrelation function of conditional score residuals are formally derived. The practical relevance of the proposed framework is illustrated for heavy-tailed GARCH models. Monte Carlo and empirical results support the finding that conditional score residuals are more reliable in testing residual autocorrelation\, when compared to squared residuals. Finally\, it is shown how a diagnostic analysis can be designed for dynamic copula models. \n  \n\nOrganizers:\n\nJean-Michel ZAKOIAN (CREST) \nSponsors:\nCREST \n
URL:https://crest.science/event/paolo-giorgi-amsterdam-university-t-b-a/
CATEGORIES:Finance-Insurance,Financial Econometrics,Seminars
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DTSTART;TZID=Europe/Helsinki:20231109T110000
DTEND;TZID=Europe/Helsinki:20231109T120000
DTSTAMP:20260726T105604
CREATED:20230725T113242Z
LAST-MODIFIED:20231108T141511Z
UID:15291-1699527600-1699531200@crest.science
SUMMARY:Jordi LLORENS-TERRAZAS (UPF Barcelona) "An Oracle Inequality for Multivariate Dynamic Quantile Forecasting."
DESCRIPTION:Finance & Financial Econometrics : \nTime: 11.00 am\nDate: 09th of Novembre 2023\nRoom 3001 \nJordi LLORENS-TERRAZAS (UPF Barcelona) “An Oracle Inequality for Multivariate Dynamic Quantile Forecasting” \nAbstract : I derive an oracle inequality for a family of possibly misspecified multivariate conditional autoregressive quantile models. The family includes standard specifications for (nonlinear) quantile prediction proposed in the literature. This inequality is used to establish that the predictor that minimizes the in-sample average check loss achieves the best out-of-sample performance within its class at a near optimal rate\, even when the model is fully misspecified. An empirical application to backtesting global Growth-at-Risk shows that a combination of the generalized autoregressive conditionally heteroscedastic model and the vector autoregression for Value-at-Risk performs best out-of-sample in terms of the check loss. \n\nOrganizers:\n\nJean-Michel ZAKOIAN (CREST) \nSponsors:\nCREST \n
URL:https://crest.science/event/jordi-llorens-terrazas-upf-barcelona-t-b-a/
CATEGORIES:Finance-Insurance,Financial Econometrics,Seminars
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