Сибирский федеральный университет

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: Computer Science Online Conference, CSOC 2021

Год издания: 2021

Идентификатор DOI: 10.1007/978-3-030-77445-5_61

Ключевые слова: asset allocation problem, efficient frontier, investment portfolio, market model, markowitz model, value-at-risk

Аннотация: In this paper new approaches to synthesize investment portfolios are proposed based on the introduced alternative risk measures for portfolio. It is shown that the difference between the portfolio mean return and ‘low-mean’ return, the difference between the portfolio mean return and ‘value-at-risk’ return may be considered as alternative risk-measures. The algorithm for portfolio ‘low-mean’ return and ‘value-at-risk’ return evaluation is formulated. The experimental study of the traditional models and of the alternative risk measures has been performed on the basis of common stocks traded via Moscow Exchange. The feasible sets for the proposed models have been constructed. It has been shown that the feasible sets for the developed investment portfolio models are conceptually similar to the ‘mean – variance’ set of the Markowitz model. The efficient frontiers for the Markowitz model, for the Sharpe index model and for the proposed alternative risk measures have been constructed; the optimal weight distributions have been obtained. The efficiency of the portfolios based on traditional risk measures and of the portfolios with alternative risk measures have been evaluated and compared. It has been shown that models based on the ‘left-side’ risk measures have produced better results for the market under consideration. These findings confirm the validity of application of alternative measures for risk assessment under financial market conditions. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Журнал: Lecture Notes in Networks and Systems

Выпуск журнала: Vol. 229

Номера страниц: 673-693

ISSN журнала: 23673370

Издатель: Springer Science and Business Media Deutschland GmbH

• Malakhova A.A. (Siberian Federal University, Krasnoyarsk, Russian Federation)
• Sochneva E.N. (Siberian Federal University, Krasnoyarsk, Russian Federation)
• Yarkova S.A. (Krasnoyarsk Institute of Railway Transport - Branch of the Irkutsk State Transport University, Krasnoyarsk, Russian Federation)
• Yarkova A.V. (Peter the Great Saint Petersburg Polytechnic University, Saint Petersburg, Russian Federation)
• Starova O.V. (Siberian Federal University, Krasnoyarsk, Russian Federation)
• Kravtsov D.I. (Siberian Federal University, Krasnoyarsk, Russian Federation)
• Zyablikov D.V. (Siberian Federal University, Krasnoyarsk, Russian Federation)

• Scopus
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