کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5098226 | 1478684 | 2015 | 21 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Robust measurement of (heavy-tailed) risks: Theory and implementation
ترجمه فارسی عنوان
اندازه گیری دقیق خطرات (سنگ های سنگین): نظریه و پیاده سازی
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
کنترل و بهینه سازی
چکیده انگلیسی
Every model presents an approximation of reality and thus modeling inevitably implies model risk. We quantify model risk in a non-parametric way, i.e., in terms of the divergence from a so-called nominal model. Worst-case risk is defined as the maximal risk among all models within a given divergence ball. We derive several new results on how different divergence measures affect the worst case. Moreover, we present a novel, empirical way built on model confidence sets (MCS) for choosing the radius of the divergence ball around the nominal model, i.e., for calibrating the amount of model risk. We demonstrate the implications of heavy-tailed risks for the choice of the divergence measure and the empirical divergence estimation. For heavy-tailed risks, the simulation of the worst-case distribution is numerically intricate. We present a Sequential Monte Carlo algorithm which is suitable for this task. An extended practical example, assessing the robustness of a hedging strategy, illustrates our approach.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Economic Dynamics and Control - Volume 61, December 2015, Pages 183-203
Journal: Journal of Economic Dynamics and Control - Volume 61, December 2015, Pages 183-203
نویسندگان
Judith C. Schneider, Nikolaus Schweizer,