Computational Management Science最新文献

Nested Benders’s decomposition of capacity-planning problems for electricity systems with hydroelectric and renewable generation 有水力发电和可再生能源发电的电力系统容量规划问题的嵌套本德斯分解法

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Computational Management Science Pub Date : 2024-01-13 DOI: 10.1007/s10287-023-00469-9

K. Yagi, R. Sioshansi

引用次数: 0

Preconditioning meets biased compression for efficient distributed optimization 预处理与有偏差的压缩相结合，实现高效的分布式优化

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Computational Management Science Pub Date : 2023-12-24 DOI: 10.1007/s10287-023-00496-6

Vitali Pirau, Aleksandr Beznosikov, Martin Takáč, Vladislav Matyukhin, A. Gasnikov

引用次数: 0

Affiliations based bibliometric analysis of publications on parkinson’s disease 基于附属机构的帕金森病出版物文献计量分析

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Computational Management Science Pub Date : 2023-12-21 DOI: 10.1007/s10287-023-00495-7

Fuad Aleskerov, O. Khutorskaya, Viacheslav Yakuba, Anna Stepochkina, Ksenia Zinovyeva

引用次数: 0

Addressing the economic and demographic complexity via a neural network approach: risk measures for reverse mortgages 通过神经网络方法应对经济和人口复杂性：反向抵押贷款的风险措施

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Computational Management Science Pub Date : 2023-12-08 DOI: 10.1007/s10287-023-00491-x

E. Di Lorenzo, G. Piscopo, M. Sibillo

引用次数: 0

Potts game on graphs: static equilibria 图上的波特斯博弈：静态均衡

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Computational Management Science Pub Date : 2023-12-05 DOI: 10.1007/s10287-023-00490-y

A. Leonidov

引用次数: 0

A multiobjective optimization approach for threshold determination in extreme value analysis for financial time series 金融时间序列极值分析中阈值确定的多目标优化方法

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Computational Management Science Pub Date : 2023-11-17 DOI: 10.1007/s10287-023-00488-6

C. C. Chu, Simon S. W. Li

引用次数: 0

A constrained swarm optimization algorithm for large-scale long-run investments using Sharpe ratio-based performance measures 基于夏普比率绩效指标的大规模长期投资约束群优化算法

Computational Management Science Pub Date : 2023-11-08 DOI: 10.1007/s10287-023-00483-x

Massimiliano Kaucic, Filippo Piccotto, Gabriele Sbaiz

{"title":"A constrained swarm optimization algorithm for large-scale long-run investments using Sharpe ratio-based performance measures","authors":"Massimiliano Kaucic, Filippo Piccotto, Gabriele Sbaiz","doi":"10.1007/s10287-023-00483-x","DOIUrl":"https://doi.org/10.1007/s10287-023-00483-x","url":null,"abstract":"Abstract We study large-scale portfolio optimization problems in which the aim is to maximize a multi-moment performance measure extending the Sharpe ratio. More specifically, we consider the adjusted for skewness Sharpe ratio, which incorporates the third moment of the returns distribution, and the adjusted for skewness and kurtosis Sharpe ratio, which exploits in addition the fourth moment. Further, we account for two types of real-world trading constraints. On the one hand, we impose stock market restrictions through cardinality, buy-in thresholds, and budget constraints. On the other hand, a turnover threshold restricts the total allowed amount of trades in the rebalancing phases. To deal with these asset allocation models, we embed a novel hybrid constraint-handling procedure into an improved dynamic level-based learning swarm optimizer. A repair operator maps candidate solutions onto the set characterized by the first type of constraints. Then, an adaptive $$ell _1$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> -exact penalty function manages turnover violations. The focus of the paper is to highlight the importance of including higher-order moments in the performance measures for long-run investments, in particular when the market is turbulent. We carry out empirical tests on two worldwide sets of assets to illustrate the scalability and effectiveness of the proposed strategies, and to evaluate the performance of our investments compared to the strategy maximizing the Sharpe ratio.","PeriodicalId":46743,"journal":{"name":"Computational Management Science","volume":"8 32","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135391242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Decentralized saddle-point problems with different constants of strong convexity and strong concavity 具有不同强凸性和强凹性常数的分散鞍点问题

Computational Management Science Pub Date : 2023-11-06 DOI: 10.1007/s10287-023-00485-9

Dmitry Metelev, Alexander Rogozin, Alexander Gasnikov, Dmitry Kovalev

引用次数: 10

Approximate option pricing under a two-factor Heston–Kou stochastic volatility model 双因素Heston-Kou随机波动率模型下的近似期权定价

Computational Management Science Pub Date : 2023-11-03 DOI: 10.1007/s10287-023-00486-8

Youssef El-Khatib, Zororo S. Makumbe, Josep Vives

{"title":"Approximate option pricing under a two-factor Heston–Kou stochastic volatility model","authors":"Youssef El-Khatib, Zororo S. Makumbe, Josep Vives","doi":"10.1007/s10287-023-00486-8","DOIUrl":"https://doi.org/10.1007/s10287-023-00486-8","url":null,"abstract":"Abstract Under a two-factor stochastic volatility jump (2FSVJ) model we obtain an exact decomposition formula for a plain vanilla option price and a second-order approximation of this formula, using Itô calculus techniques. The 2FSVJ model is a generalization of several models described in the literature such as Heston (Rev Financ Stud 6(2):327–343, 1993); Bates (Rev Financ Stud 9(1):69–107, 1996); Kou (Manag Sci 48(8):1086–1101, 2002); Christoffersen et al. (Manag Sci 55(12):1914–1932, 2009) models. Thus, the aim of this study is to extend some approximate pricing formulas described in the literature, like formulas in Alòs (Finance Stoch 16(3):403–422, 2012); Merino et al. (Int J Theor Appl Finance 21(08):1850052, 2018); Gulisashvili et al. (J Comput Finance 24(1), 2020), to pricing under the more general 2FSVJ model. Moreover, we provide numerical illustrations of our pricing method and its accuracy and computational advantage under double exponential and log-normal jumps. Numerically, our pricing method performs very well compared to the Fourier integral method. The performance is ideal for out-of-the-money options as well as for short maturities.","PeriodicalId":46743,"journal":{"name":"Computational Management Science","volume":"44 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135819590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Value of Shared Information for allocation of drivers in ride-hailing: a proof-of-concept study 共享信息对网约车司机分配的价值:一项概念验证研究

Computational Management Science Pub Date : 2023-11-03 DOI: 10.1007/s10287-023-00487-7

Gianfranco Liberona, David Salas, Léonard von Niederhäusern

引用次数: 0