12 research outputs found

    Extremes and Robustness: A Contradiction?

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    Stochastic models play an important role in the analysis of data in many different fields, including finance and insurance. Many models are estimated by procedures that lose their good statistical properties when the underlying model slightly deviates from the assumed one. Robust statistical methods can improve the data analysis process of the skilled analyst and provide him with useful additional information. For this anniversary issue, we discuss some aspects related to robust estimation in the context of extreme value theory (EVT). Using real data and simulations, we show how robust methods can improve the quality of EVT data analysis by providing information on influential observations, deviating substructures and possible mis-specification of a model while guaranteeing good statistical properties over a whole set of underlying distributions around the assumed on

    The 10B(p,α)7Be S(E)-factor from 5 keV to 1.5 MeV using the Trojan Horse Method

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    The 10 B(p, α ) 7 Be reaction is the main responsible for the 10 B destruction in stellar interior [1]. In such environments this p-capture process occurs at a Gamow energy of 10 keV and takes places mainly through a resonant state (Ex = 8.701 MeV) of the compound 11 C nucleus. Thus a resonance right in the region of the Gamow peak is expected to significantly influence the behavior of the astrophysical S(E)-factor. The 10 B(p, α ) 7 Be reaction was studied via the Trojan Horse Method (THM) applied to the 2 H( 10 B, α 7 Be)n in order to extract the astrophysical S(E)-factor in a wide energy range from 5 keV to 1.5 MeV

    Robust tests of predictive accuracy

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    We propose robust counterparts to tests of equal forecast accuracy such as those proposed by Diebold and Mariano (1995) and West (1996). We illustrate the robustness problem and evaluate the size and the power properties of the classical and robust tests under various types of deviations from model assumptions. The new robust test has a correct size and larger power across a wide spectrum of distributions including in particular heavy-tailed distribution

    Stock and bond return predictability: the discrimination power of model selection criteria

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    The discrimination power of well-known model selection criteria is analyzed when the R-squared is low as in typical asset return predictability studies. It turns out that the discrimination power is low in this situation and this may explain, already in a simple i.i.d. setup, why often in-sample predictability, but no out-of-sample predictability is found. In particular it is possible to give another interpretation to the results of the well-cited Bossaerts and Hillion (Rev. Financial Stud. 12 (1999) 405–428) study. As a consequence, model selection criteria are put in a testing framework and a bootstrap-based procedure is proposed as a diagnostic tool to construct the class of models which are statistically indistinguishable from the best model chosen by a model selection criterion. In an empirical illustration the Pesaran and Timmerman (J. Finance 50 (1995) 1201–1228) results are reanalyzed and it turns out that in this case this class of models can be large. Finally it is shown that similar problems arise in a more hidden way in the context of recent model uncertainty studies using Bayesian model selection criteria

    Tavola rotonda. Umanesimo del management attraverso gli occhi dell'altro

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    Tavola rotonda raccoglie undici francobolli: brevi sguardi, attraverso gli occhi dell’altro, che studiosi e practitioner di differente scaturigine gettano sul tema dell’umanesimo del management. Tavola rotonda tematizza l’ampia e articolata riflessione intorno ad una concezione dell’azione organizzativa non appiattita su posizioni dogmatiche e precostituite. Tavola rotonda guarda alla formazione manageriale in tempi di crisi strutturale. Tavola rotonda schiude al dibattito lanciato dalla Collana punto org per leggere la realtà e scrivere i modi per interpretarla
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