谷物价格波动:基于分布式滞后模型的网络演化方法

IF 1.9 4区 经济学 Q2 ECONOMICS Computational Economics Pub Date : 2024-07-30 DOI:10.1007/s10614-024-10645-x
Yutian Miao, Siyan Liu, Xiaojuan Dong, Gang Lu
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引用次数: 0

摘要

由于世界范围内冲突不断,近年来玉米、小麦价格波动较大,导致各国更加关注与粮食安全相关的问题。为了研究国际粮食期货市场价格波动的动态特征和演变规律,完善粮食价格联动趋势识别。本研究基于分布式滞后模型,构建了玉米和小麦期货价格的有向加权网络,并研究了玉米和小麦期货价格之间的联动关系。我们发现,2013 年至 2023 年间玉米和小麦期货价格联动关系大多形成了一些显著且相对一致的关系模式。通过对复杂网络的分析,发现玉米和小麦期货价格在一段时间内相对稳定,高中心性节点的频繁出现具有规律性,这与全球市场的基本面状况直接相关。我们的研究结果有助于判断农产品价格联动影响的变化趋势,这对提高粮食期货的安全性至关重要。
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Grain Price Fluctuation: A Network Evolution Approach Based on a Distributed Lag Model

Due to the continuous worldwide conflicts, the prices of corn and wheat have fluctuated greatly in recent years, which has led countries to focus more on concerns related to food security. In order to study the dynamic characteristics and evolution law of price volatility in the international grain futures market and improve the price linkage trend of grain identification. This study builds a directed weighted network of corn and wheat futures prices based on the distributed lag model and examines the linkage relationship between corn and wheat futures prices. We discover that most of the price linkages between corn and wheat futures between 2013 and 2023 form some significant and relatively consistent relationship patterns. Through the analysis of complex network, it has been discovered that the prices of corn and wheat futures are relatively stable over time and that the frequent occurrence of high centrality nodes has a regular pattern that is directly related to the fundamental conditions of the global market. Our results are useful in determining the trend of change in the linkage impact of agricultural product prices, which is crucial for enhancing the safety of grain futures.

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来源期刊
Computational Economics
Computational Economics MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.00
自引率
15.00%
发文量
119
审稿时长
12 months
期刊介绍: Computational Economics, the official journal of the Society for Computational Economics, presents new research in a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems from all branches in economics. The topics of Computational Economics include computational methods in econometrics like filtering, bayesian and non-parametric approaches, markov processes and monte carlo simulation; agent based methods, machine learning, evolutionary algorithms, (neural) network modeling; computational aspects of dynamic systems, optimization, optimal control, games, equilibrium modeling; hardware and software developments, modeling languages, interfaces, symbolic processing, distributed and parallel processing
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