کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5103001 | 1480094 | 2017 | 17 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Driving factors of interactions between the exchange rate market and the commodity market: A wavelet-based complex network perspective
ترجمه فارسی عنوان
عوامل موثر در تعاملات بین بازار ارز و بازار کالا: چشم انداز شبکه پیچیده مبتنی بر موجک
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کلمات کلیدی
قیمت ارز، کالا، فیلتر کلمن، تحلیل ویولت، شبکه پیچیده
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
فیزیک ریاضی
چکیده انگلیسی
In traditional econometrics, a time series must be in a stationary sequence. However, it usually shows time-varying fluctuations, and it remains a challenge to execute a multiscale analysis of the data and discover the topological characteristics of conduction in different scales. Wavelet analysis and complex networks in physical statistics have special advantages in solving these problems. We select the exchange rate variable from the Chinese market and the commodity price index variable from the world market as the time series of our study. We explore the driving factors behind the behavior of the two markets and their topological characteristics in three steps. First, we use the Kalman filter to find the optimal estimation of the relationship between the two markets. Second, wavelet analysis is used to extract the scales of the relationship that are driven by different frequency wavelets. Meanwhile, we search for the actual economic variables corresponding to different frequency wavelets. Finally, a complex network is used to search for the transfer characteristics of the combination of states driven by different frequency wavelets. The results show that statistical physics have a unique advantage over traditional econometrics. The Chinese market has time-varying impacts on the world market: it has greater influence when the world economy is stable and less influence in times of turmoil. The process of forming the state combination is random. Transitions between state combinations have a clustering feature. Based on these characteristics, we can effectively reduce the information burden on investors and correctly respond to the government's policy mix.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Physica A: Statistical Mechanics and its Applications - Volume 479, 1 August 2017, Pages 299-308
Journal: Physica A: Statistical Mechanics and its Applications - Volume 479, 1 August 2017, Pages 299-308
نویسندگان
Shaobo Wen, Haizhong An, Zhihua Chen, Xueyong Liu,