Современные риск-системы
Статьи 2008 года

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Статьи 2008 года

  1. A.Novosyolov, D.Satchkov Global term structure modelling using principal component analysis. Journal of Asset Management (2008) 9, 1, 49-60.
    Abstract.
    Principal component analysis (PCA) is a technique commonly applied to the interest rate markets to describe yield curve dynamics in a parsimonious manner. Despite an increase in global investing and the growing interconnectedness of the international markets, PCA has not been widely applied to decomposing joint structure of global yield curves. Our objective is to describe the joint structure with a model that can potentially be used for scenario analysis and for estimating the risk of interest rate-sensitive portfolios. In this study, we examine three variations of the PCA technique to decompose global yield curve and interest rate implied volatility structure. We conclude that global yield curve structure can be described with 15–20 factors, whereas implied volatility structure requires at least 20 global factors. The procedure that we identify as preferable is a two-step PCA, with local curves decomposed in the first step and combined local PCs decomposed into a joint structure (PCA of PCs) in the second step. This procedure has a key advantage in that it makes any scenario analysis more meaningful by keeping local PCA factors, which have important economic interpretations as shift, twist and butterfly moves of the yield curve.
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  2. A.Novosyolov Measuring Risk. Reliability and Risk Analysis: Theory and Applications (2008) 1, 4, 115-119.
    Abstract.
    Problem of representation of human preferences among uncertain outcomes by functionals (risk measures) is being considered in the paper. Some known risk measures are presented: expected utility, distorted probability and value-at-risk. Properties of the measures are stated and interrelations between them are established. A number of methods for obtaining new risk measures from known ones are also proposed: calculating mixtures and extremal values over given families of risk measures.
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  3. Novosyolov A.A. Generators of distorted probability functionals. Proceedings of the 8th International Scientific School "Modelling and Analysis of Safety and Risk in Complex Systems", St.-Petersburg, 2008, 290-294.
    Abstract
    The concept of coherent risk measure is defined axiomatically, and every such measure may be represented by a cone of admissible risks or a family of probability distributions. Similar representations are valid for a partial case of coherent risk measures, the so called distorted probability functionals. In the present paper we discuss the specific representation of distorted probability functionals by families of probability measures, and use it to clarify the specific position of distorted probability functionals among coherent risk measures.
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  4. Novosyolov A.A. Higher order stochastic dominance in option pricing and insurance. Proceedings of the 8th International Scientific School "Modelling and Analysis of Safety and Risk in Complex Systems", St.-Petersburg, 2008, 77-82.
    Abstract
    The paper contains derivation of integral and asymptotic representations for complementary distribution functions. A few examples illustrate direct and dual second order stochastic dominance in terms of insurance and option pricing.
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  5. Новоселов А.А. Некоторые свойства функции относительного среднего. Труды VII Всероссийской конференции по финансово-актуарной математике и смежным вопросам, т.1, Красноярск, СФУ, 2008, 180-186.
    Abstract
    Для описания предпочтений на множестве рисков обычно задается некоторый функционал на множестве описателей распределений рисков, либо непосредственно на множестве описателей задается отношение (частичного) порядка. Примером последнего способа является задание стохастического доминирования различных порядков \cite{nov2002}, в качестве описателей при этом используются интегральные функции распределения. В \cite{CasconKeatingShadwick2003} введено понятие функции "Омега", которая также является описателем распределения (правда, только в случае конечного математического ожидания). Эта функция имеет вид отношения двух интегралов, связанных с вычислением среднего значения, и в настоящей работе называется функцией относительного среднего. В работе исследуются некоторые свойства функции относительного среднего, изучается связь порождаемого ей порядка со стохастическим доминированием, рассматриваются статистические свойства оценки функции по наблюдениям, приводятся примеры применения в задачах принятия решений.
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Начало Введение Лекции Загрузка Стресс Публикации Иллюстрации Справочник Избранное Глоссарий Ссылки Доска Контакт
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