Top

Journal of Economic Interaction and Coordination

Published in:

21-11-2019 | Regular Article

Quantifying the risk of price fluctuations based on weighted Granger causality networks of consumer price indices: evidence from G7 countries

Authors: Qingru Sun, Xiangyun Gao, Ze Wang, Siyao Liu, Sui Guo, Yang Li

Published in: Journal of Economic Interaction and Coordination | Issue 4/2020

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The consumer price index (CPI) is the weighted average of a basket of subcategories (CPI classes) and is the most widely adopted indicator in analyzing the risk of inflation or deflation. However, CPI classes contain more risk information. The CPI classes and the transmission of price fluctuations among them form a price index system. By using the CPI classes of the G7 countries, we explored the evolution of the fluctuation–transmission relationships among CPI classes and constructed weighted Granger causality networks (WGCNs) for each country. We measured the price fluctuation risk of the price index system in the G7 countries by using system risk entropy and revealed the structure of the systems from four perspectives: out-degree, clustering coefficient, correlation degree between CPI classes and the survival ratio of the Granger causality. We found the following trends. (1) The system risk entropy changed over time. After the 2008 financial crisis, the price fluctuation risk in the price index system increased. (2) The identified CPI classes with large out-degrees are vital in monitoring the fluctuations of commodities prices. (3) The stability of the Granger causality among CPI classes decreased as the time span increased, and the structure of most WGCNs was completely different after 2 years.

previous article Does a ‘financial transaction tax’ drive out information mirages? An experimental analysis

next article Financial contagion in inter-bank networks with overlapping portfolios

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Available only for authorised users

Please see Education indicators in Canada: An international perspective, 2017.

Please see Education indicators in Canada: An international perspective, 2018.

Acemoglu D, Carvalho VM, Ozdaglar A, Tahbaz-Salehi A (2012) The network origins of aggregate fluctuations. Econometrica 80:1977–2016. https://doi.org/10.3982/ecta9623CrossRef

Acemoglu D, Akcigit U, Kerr W (2016a) Networks and the macroeconomy: an empirical exploration NBER. Macroecon Ann 30:52

Acemoglu D, Malekian A, Ozdaglar A (2016b) Network security and contagion. J Econ Theory 166:536–585. https://doi.org/10.1016/j.jet.2016.09.009CrossRef

Acemoglu D, Ozdaglar A, Tahbaz-Salehi A (2017) Microeconomic origins of macroeconomic tail risks. Am Econ Rev 107:54–108. https://doi.org/10.1257/aer.20151086CrossRef

Alvarez-Diaz M, Gupta R (2016) Forecasting US consumer price index: does nonlinearity matter? Appl Econ 48:4462–4475. https://doi.org/10.1080/00036846.2016.1158922CrossRef

An H, Gao X, Fang W, Ding Y, Zhong W (2014) Research on patterns in the fluctuation of the co-movement between crude oil futures and spot prices: a complex network approach. Appl Energy 136:1067–1075CrossRef

Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B-Methodol 57:289–300

Dinh TTH, Vu VT, Bui TL, IEEE (2015) Forecasting of Consumer Price Index using the ensemble learning model with multi-objective evolutionary algorithms: Preliminary results. In: 2015 international conference on advanced technologies for communications. IEEE, New York

Carvalho VM (2014) From micro to macro via production networks. J Econ Perspect 28:23–48. https://doi.org/10.1257/jep.28.4.23CrossRef

Coondoo D, Majumder A, Chattopadhyay S (2011) Estimating spatial consumer price indices through engel curve analysis. Rev Income Wealth 57:138–155. https://doi.org/10.1111/j.1475-4991.2010.00382.xCrossRef

Ebel H, Mielsch LI, Bornholdt S (2002) Scale-free topology of e-mail networks. Phys Rev E 66:035103CrossRef

Gaffeo E, Molinari M (2018) A functional perspective on financial networks. J Econ Interact Coord 13:51–79. https://doi.org/10.1007/s11403-017-0210-7CrossRef

Gao XY, An HZ, Zhong WQ (2013) Features of the correlation structure of price indices. PLoS One. https://doi.org/10.1371/journal.pone.0061091CrossRef

Gao XY, Fang W, An F, Wang Y (2017) Detecting method for crude oil price fluctuation mechanism under different periodic time series. Appl Energy 192:201–212. https://doi.org/10.1016/j.apenergy.2017.02.014CrossRef

García-Germán S, Bardají I, Garrido A (2015) Evaluating price transmission between global agricultural markets and consumer food price indices in the European Union. Agric Econ 47:59–70CrossRef

Gilles RP, Lazarova EA, Ruys PHM (2015) Stability in a network economy: the role of institutions. J Econ Behav Organ 119:375–399. https://doi.org/10.1016/j.jebo.2015.09.011CrossRef

Guo S, Li HJ, An HZ, Sun QR, Hao XQ, Liu YX (2019) Steel product prices transmission activities in the midstream industrial chain and global markets. Resources Policy 60:56–71CrossRef

Ji Q, Bouri E, Roubaud D (2018) Dynamic network of implied volatility transmission among US equities, strategic commodities, and BRICS equities. Int Rev Financ Anal. https://doi.org/10.1016/j.irfa.2018.1002.1001CrossRef

Jones DD (1975) Amino acid properties and side-chain orientation in proteins: a cross correlation approach. J Theor Biol 50:167–183. https://doi.org/10.1016/0022-5193(75)90031-4CrossRef

Klick J (2018) Improving initial estimates of the chained consumer price index. Mon Labor Rev. https://doi.org/10.21916/mlr.22018.21916CrossRef

Laloux L, Cizeau P, Bouchaud J-P, Potters M (1999) Noise dressing of financial correlation matrices. Phys Rev Lett 83:1467–1470. https://doi.org/10.1103/PhysRevLett.83.1467CrossRef

Larraz-Iribas B, Pavia-Miralles JM, Ferrari G (2013) Weighting elementary prices in consumer price index construction using spatial autocorrelation commun stat-theory. Methods 42:4460–4475. https://doi.org/10.1080/03610926.2011.648793CrossRef

Lent J (2011) Estimating an energy consumer price index from establishment survey data. Mon Labor Rev 134:13–28

Li H, An H, Liu X, Gao X, Fang W, An F (2016) Price fluctuation in the energy stock market based on fluctuation and co-fluctuation matrix transmission networks. Energy 117:73–83CrossRef

Mantegna RN (1999) Hierarchical structure in financial markets. Eur Phys J B 11:193–197. https://doi.org/10.1007/s100510050929CrossRef

Neveu AR (2018) A survey of network-based analysis and systemic risk measurement. J Econ Interac Coord 13:241–281. https://doi.org/10.1007/s11403-016-0182-zCrossRef

Onnela JP, Chakraborti A, Kaski K, Kertész J, Kanto A (2003) Dynamics of market correlations: taxonomy and portfolio analysis. Phys Rev E 68:056110CrossRef

Sarantitis GA, Papadimitriou T, Gogas P (2018) A network analysis of the United Kingdom’s consumer price index. Comput Econ 51:173–193. https://doi.org/10.1007/s10614-016-9625-9CrossRef

Seth A (2007) Granger causality. Scholarpedia 2(7):1667. https://doi.org/10.4249/scholarpedia.1667CrossRef

Shahbaz M, Wahid ANM, Haider A (2010) Empirical psychology between wholesale price and consumer price indices: the case of Pakistan Singap. Econ Rev 55:537–551. https://doi.org/10.1142/s0217590810003882CrossRef

Sharma SS (2016) Can consumer price index predict gold price returns? Econ Model 55:269–278. https://doi.org/10.1016/j.econmod.2016.02.014CrossRef

Sun Q, Gao X, Wen S, Chen Z, Hao X (2018a) The transmission of fluctuation among price indices based on Granger causality network. Phys A 506:36–49. https://doi.org/10.1016/j.physa.2018.04.055CrossRef

Sun Q, Gao X, Wen S, Feng S, Wang Z (2018b) Modeling the impulse response complex network for studying the fluctuation transmission of price indices J Econ Interact Coord 15:17. https://doi.org/10.1007/s11403-018-0231-xCrossRef

Tiwari AK (2012) An empirical investigation of causality between producers’ price and consumers’ price indices in Australia in frequency domain. Econ Model 29:1571–1578CrossRef

Tiwari AK, Suresh KG, Arouri M, Teulon F (2014) Causality between consumer price and producer price: evidence from Mexico. Econ Model 36:432–440. https://doi.org/10.1016/j.econmod.2013.09.050CrossRef

Tsekeris T (2017) Network analysis of inter-sectoral relationships and key sectors in the Greek economy. J Econ Interact Coord 12:413–435. https://doi.org/10.1007/s11403-015-0171-7CrossRef

Wang JX, Sun L, Zhao HS, Wang YD (2016) ARIMA-BP integrated intelligent algorithm for China’s consumer price index forecasting and its applications. J Intell Fuzzy Syst 31:2187–2193. https://doi.org/10.3233/jifs-169058CrossRef

Wang GJ, Xie C, Chen S (2017a) Multiscale correlation networks analysis of the US stock market: a wavelet analysis. J Econ Interact Coord 12:561–594. https://doi.org/10.1007/s11403-016-0176-xCrossRef

Wang MG et al (2017b) Systemic risk and spatiotemporal dynamics of the consumer market of China. Phys A 473:188–204. https://doi.org/10.1016/j.physa.2017.01.021CrossRef

Wang GJ, Jiang ZQ, Lin M, Xie C, Stanley HE (2018) Interconnectedness and systemic risk of China’s financial institutions. Emerg Markets Rev 35:1–8CrossRef

Wang Z, Gao X, An H, Tang R, Sun Q (2019) Identifying influential energy stocks based on spillover network. Int Rev Financ Anal. https://doi.org/10.1016/j.irfa.2018.11.004CrossRef

Xiao J, Wang MG, Lixin TX, Zhen ZL (2018) The measurement of China’s consumer market development based on CPI data. Phys A 490:664–680. https://doi.org/10.1016/j.physa.2017.08.135CrossRef

Title: Quantifying the risk of price fluctuations based on weighted Granger causality networks of consumer price indices: evidence from G7 countries
Authors: Qingru Sun
Xiangyun Gao
Ze Wang
Siyao Liu
Sui Guo
Yang Li
Publication date: 21-11-2019
Publisher: Springer Berlin Heidelberg
Published in: Journal of Economic Interaction and Coordination / Issue 4/2020
Print ISSN: 1860-711X
Electronic ISSN: 1860-7128
DOI: https://doi.org/10.1007/s11403-019-00273-2

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 4/2020

Does a ‘financial transaction tax’ drive out information mirages? An experimental analysis

An artificial Wicksell–Keynes economy integrating short-run business cycle and long-term cumulative trend

Financial accumulation implies ever-increasing wealth inequality

Financial contagion in inter-bank networks with overlapping portfolios

A path integral approach to business cycle models with large number of agents

Investment behaviour and “bull & bear” dynamics: modelling real and stock market interactions