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Novosyolov A.A.
Measuring risk aversion in Non-Linear Preference Models.
Proceedings of the 11th International Scientific School "Modeling and Analysis of Safety and Risk in Complex Systems
, St-Petersburg, 2011, 165-170.
Abstract
The paper introduces the so-called natural preference relation on sets of risks and studies a concept of risk aversion within this context. A representation theorem for natural preferences is provided, and a method of quantitative measurement of risk aversion is studied.
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Novosyolov A.A.
Measuring risk aversion
. Proceedings of the X International conference on financial and actuarial mathematics, and fire safety
, Krasnoyarsk, SFU, 2011, 309-313. (in Russian)
Abstract
In the paper we define some natural preference relations on a set of risks and study the concept of risk aversion in the framework of this model. We prove a theorem on representation natural preference relations by real functionals, and provide a method of quantitative expression of risk aversion.
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Novosyolov A.A.
A risk aversion functional.
Proceedings of the XV International conference on eventological mathematics and related topics
, Krasnoyarsk, SFU, 2011, 143-148. (in Russian)
Abstract
In the paper we study a risk aversion functional, which generalizes the numeric method introduced earlier. Some properties of the functional are established, and examples for coherent risk measures are provided.
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Novosyolov A.A.
Modeling randomness.
Risk management in credit organization
,
3
(2011), 3, 109-112. (in Russian)
Abstract
In this first material of the Quant Room series we introduce basic concepts of modeling randomness for management of credit organization. Principles of risk models validation are discussed, as well as basic concepts of risk, such as event, probability, random variable and distribution.
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Novosyolov A.A.
Control under risk,
Risk management in credit organization,
4
(2011), 3, 106-110. (in Russian)
Abstract
We describe basic principles of processes control under risk. The description uses a very basic situation of decision-making, when the probability model is represented by binomial distribution.
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