一个评估商品收益峰值的随机模型

IF 1.3 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Applied Stochastic Models in Business and Industry Pub Date : 2023-06-26 DOI:10.1002/asmb.2790
Roy Cerqueti, Raffaele Mattera, Alessandro Ramponi
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引用次数: 0

摘要

本文提出了一个用于评估商品收益峰值成分的概率模型。研究的基础在于有证据表明,回报的峰值是由外部环境的冲击造成的。我们采用的方法基于一类特殊的点过程--空间混合泊松过程--利用这类过程的不变量特性。该理论框架用于介绍基于可用信息的收益估算程序。为了激发我们的理论方法,我们介绍了一个基于不同商品回报率的经验实例,以及作为外部冲击的波动指数异常回报率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A stochastic model for evaluating the peaks of commodities' returns

This paper proposes a probabilistic model for the evaluation of the peak components of the return of a commodity. The ground of the study lies in the evidence that the spikes in the returns are due to the shocks occurring in the external environment. We follow an approach based on a particular class of point processes—the Spatial Mixed Poisson Processes—by exploiting an invariance property for such a class. The theoretical framework is used for presenting an estimation the procedure of the returns based on the available information. An empirical instance based on different commodities' returns and the abnormal returns of the volatility index as external shocks are presented to motivate our theoretical approach.

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来源期刊
CiteScore
2.70
自引率
0.00%
发文量
67
审稿时长
>12 weeks
期刊介绍: ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process. The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.
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