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Conditional Monte Carlo estimation of quantile sensitives / Michael C. Fu in Management science, Vol. 55 N° 12 (Décembre 2009)

Public ISBD [article]  in Management science > Vol. 55 N° 12 (Décembre 2009) . - pp. 2019-2027 Titre : Conditional Monte Carlo estimation of quantile sensitives Type de document : texte imprimé Auteurs : Michael C. Fu, Auteur ; L. Jeff Hong, Auteur ; Jian-Qiang Hu, Auteur Article en page(s) : pp. 2019-2027 Note générale : Gestion Langues : Anglais (eng) Mots-clés : Quantiles  Value  at  risk  Credit  risk  Monte  Carlo  simulation  Gradient  estimation Index. décimale : 658 Organisation des entreprises. Techniques du commerce Résumé : Estimating quantile sensitivites is important in many optimizations, from hedging in financial engineering to service-level constraints in inventory control to more general chance constraints in stochastic programming. Recently, Hong (Hong, L. J. 2009. Estimating Quantile sensitivities. Oper. Res. 57 118-130) derived a batched infinitesimal perturbation analysis estimator for quantile sensitivities, and Liu and Hong (Liu, G., L. J. Hong. 2009. Kernel estimation of quantile sensitivities. Naval Res. Logist. 56 511-525) derived a kernel estimator. Both of these estimators are consistent with convergence rates bounded by n-1/3 and n-2/5 , respectively. In this paper, we use conditional Monte Carlo to derive a consistent quantile sensitivity estimator that improves upon these convergence rates and requires no batching or binning. We illustrate the new estimator using a simple but realistic portfolio credit risk example, for which the previous work is inapplicable. DEWEY : 658 ISSN : 0025-1909 [article] Conditional Monte Carlo estimation of quantile sensitives [texte imprimé] / Michael C. Fu, Auteur ; L. Jeff Hong, Auteur ; Jian-Qiang Hu, Auteur . - pp. 2019-2027. Gestion Langues : Anglais (eng) in Management science > Vol. 55 N° 12 (Décembre 2009) . - pp. 2019-2027 Mots-clés : Quantiles  Value  at  risk  Credit  risk  Monte  Carlo  simulation  Gradient  estimation Index. décimale : 658 Organisation des entreprises. Techniques du commerce Résumé : Estimating quantile sensitivites is important in many optimizations, from hedging in financial engineering to service-level constraints in inventory control to more general chance constraints in stochastic programming. Recently, Hong (Hong, L. J. 2009. Estimating Quantile sensitivities. Oper. Res. 57 118-130) derived a batched infinitesimal perturbation analysis estimator for quantile sensitivities, and Liu and Hong (Liu, G., L. J. Hong. 2009. Kernel estimation of quantile sensitivities. Naval Res. Logist. 56 511-525) derived a kernel estimator. Both of these estimators are consistent with convergence rates bounded by n-1/3 and n-2/5 , respectively. In this paper, we use conditional Monte Carlo to derive a consistent quantile sensitivity estimator that improves upon these convergence rates and requires no batching or binning. We illustrate the new estimator using a simple but realistic portfolio credit risk example, for which the previous work is inapplicable. DEWEY : 658 ISSN : 0025-1909

Robust simulation of global warming policies using the DICE model / Zhaolin Hu in Management science, Vol. 58 N° 12 (Décembre 2012) 

Public ISBD [article]  in Management science > Vol. 58 N° 12 (Décembre 2012) . - pp. 2190-2206 Titre : Robust simulation of global warming policies using the DICE model Type de document : texte imprimé Auteurs : Zhaolin Hu, Auteur ; Jing Cao, Auteur ; L. Jeff Hong, Auteur Année de publication : 2013 Article en page(s) : pp. 2190-2206 Note générale : Management Langues : Anglais (eng) Mots-clés : Environment  Global  warming  Programming  Semidefinite  Simulation  Applications Résumé : Integrated assessment models that combine geophysics and economics features are often used to evaluate and compare global warming policies. Because there are typically profound uncertainties in these models, a simulation approach is often used. This approach requires the distribution of the uncertain parameters clearly specified. However, this is typically impossible because there is often a significant amount of ambiguity (e.g., estimation error) in specifying the distribution. In this paper, we adopt the widely used multivariate normal distribution to model the uncertain parameters. However, we assume that the mean vector and covariance matrix of the distribution are within some ambiguity sets. We then show how to find the worst-case performance of a given policy for all distributions constrained by the ambiguity sets. This worst-case performance provides a robust evaluation of the policy. We test our algorithm on a famous integrated model of climate change, known as the Dynamic Integrated Model of Climate and the Economy (DICE model). We find that the DICE model is sensitive to the means and covariance of the parameters. Furthermore, we find that, based on the DICE model, moderately tight environmental policies robustly outperform the no controls policy and the famous aggressive policies proposed by Stern and Gore. ISSN : 0025-1909 En ligne : http://mansci.journal.informs.org/content/58/12/2190.abstract [article] Robust simulation of global warming policies using the DICE model [texte imprimé] / Zhaolin Hu, Auteur ; Jing Cao, Auteur ; L. Jeff Hong, Auteur . - 2013 . - pp. 2190-2206. Management Langues : Anglais (eng) in Management science > Vol. 58 N° 12 (Décembre 2012) . - pp. 2190-2206 Mots-clés : Environment  Global  warming  Programming  Semidefinite  Simulation  Applications Résumé : Integrated assessment models that combine geophysics and economics features are often used to evaluate and compare global warming policies. Because there are typically profound uncertainties in these models, a simulation approach is often used. This approach requires the distribution of the uncertain parameters clearly specified. However, this is typically impossible because there is often a significant amount of ambiguity (e.g., estimation error) in specifying the distribution. In this paper, we adopt the widely used multivariate normal distribution to model the uncertain parameters. However, we assume that the mean vector and covariance matrix of the distribution are within some ambiguity sets. We then show how to find the worst-case performance of a given policy for all distributions constrained by the ambiguity sets. This worst-case performance provides a robust evaluation of the policy. We test our algorithm on a famous integrated model of climate change, known as the Dynamic Integrated Model of Climate and the Economy (DICE model). We find that the DICE model is sensitive to the means and covariance of the parameters. Furthermore, we find that, based on the DICE model, moderately tight environmental policies robustly outperform the no controls policy and the famous aggressive policies proposed by Stern and Gore. ISSN : 0025-1909 En ligne : http://mansci.journal.informs.org/content/58/12/2190.abstract