Analysis of the gold fixing price fluctuation in different times based on the directed weighted networks

Abstract According to the complex network theory, this paper constructs the gold fixing price fluctuation directed weighted network (GFPFDWN) at 10:30 a.m. (A.M.) and 15:00p.m. (P.M.) London Greenwich Mean Time, and studies the law of the gold fixing price fluctuation by analyzing the basic statistical data and characteristics of the GFPFDWN. The results show that the abnormal distribution of the gold fixing price (GFP) at A.M. and P.M. is confirmed by the statistics, the core fluctuation state of the GFPFDWN is reflected in the first 1.8% nodes, and most of the nodes have smaller strength, only a few nodes have larger strength, which has the characteristics of a typical scale-free network. Meanwhile, the nodes with a large strength are closely related among them, which must appear earlier, but the nodes appearing early are not necessarily the nodes with a large strength. The nodes of the GFPFDWN present a short-range correlation in different periods, and the cumulative time of the new nodes shows a high linear growth trend. In addition, the nodes of the GFPFDWN show the characteristics with a small betweenness, clustering coefficient and node strength in different periods, which are different from the characteristics of the random network and chaotic network. When these nodes with small strength appear, which means that this period is in a transitional period, then identifying and analyzing these nodes can effectively predict the fluctuation of the gold fixing price in the next period.

[1]  B. Lucey,et al.  The Financial Economics of Gold – A Survey , 2015 .

[2]  Dachuan Wei,et al.  An Optimized Floyd Algorithm for the Shortest Path Problem , 2010, J. Networks.

[3]  Rangan Gupta,et al.  Testing for asymmetric nonlinear short- and long-run relationships between bitcoin, aggregate commodity and gold prices , 2018, Resources Policy.

[4]  M. Bilgin,et al.  Time dynamics of connectedness between commodity convenience yields and zero-coupon inflation swap rates , 2020 .

[5]  R. Reis,et al.  Recent advances using gold nanoparticles as a promising multimodal tool for tissue engineering and regenerative medicine , 2017 .

[6]  Christian Pierdzioch,et al.  The international business cycle and gold-price fluctuations , 2014 .

[7]  Chi Keung Marco Lau,et al.  The effects of uncertainty measures on the price of gold , 2018, International Review of Financial Analysis.

[8]  Kim-Hung Pho,et al.  Is Bitcoin a better portfolio diversifier than gold? A copula and sectoral analysis for China , 2021 .

[9]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[10]  S. Vigne,et al.  Return spillovers between white precious metal ETFs: The role of oil, gold, and global equity , 2017 .

[11]  P. Mali.  FLUCTUATION OF GOLD PRICE IN INDIA VERSUS GLOBAL CONSUMER PRICE INDEX , 2014 .

[12]  D. Baur,et al.  Institute for International Integration Studies Is Gold a Safe Haven? International Evidence Is Gold a Safe Haven? International Evidence Is Gold a Safe Haven? International Evidence , 2022 .

[13]  Janelle Mann,et al.  Gold and crude oil prices after the great moderation , 2018 .

[14]  Hongwei Zhang,et al.  Does Bitcoin or gold react to financial stress alike? Evidence from the U.S. and China , 2021 .

[15]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[16]  Erkan Topal,et al.  An overview of global gold market and gold price forecasting , 2010 .

[17]  Pratap Chandra Biswal,et al.  Return and volatility linkages among International crude oil price, gold price, exchange rate and stock markets: Evidence from Mexico , 2019, Resources Policy.

[18]  Şahin Telli,et al.  Multifractal behavior in return and volatility series of Bitcoin and gold in comparison , 2020 .

[19]  Yu Wei,et al.  Analysis of the efficiency and multifractality of gold markets based on multifractal detrended fluctuation analysis , 2011 .

[20]  B. Lucey,et al.  Institute for International Integration Studies Is Gold a Hedge or a Safe Haven? an Analysis of Stocks, Bonds and Gold Is Gold a Hedge or a Safe Haven? an Analysis of Stocks, Bonds and Gold , 2022 .

[21]  Sharmistha Bagchi-Sen,et al.  Small and flat worlds: A complex network analysis of international trade in crude oil , 2015 .

[22]  Provash Mali,et al.  Multifractal characterization of gold market: A multifractal detrended fluctuation analysis , 2014, 1506.08847.

[23]  Jacinta C. Nwachukwu,et al.  On the Efficiency of Global Gold Markets , 2015 .

[24]  Obryan Poyser.  Exploring the dynamics of Bitcoin’s price: a Bayesian structural time series approach , 2018, Eurasian Economic Review.

[25]  Chia‐Lin Chang,et al.  Dynamic price integration in the global gold market , 2013 .

[26]  O. Rosso,et al.  An information theory perspective on the informational efficiency of gold price , 2019, The North American Journal of Economics and Finance.

[27]  Lixin Tian,et al.  Fluctuation behavior analysis of international crude oil and gasoline price based on complex network perspective , 2016 .

[28]  Tezer Yelkenci,et al.  Shock transmission and volatility spillover in stock and commodity markets: evidence from advanced and emerging markets , 2018 .

[29]  R. K. Jana,et al.  COVID-19 and oil market crash: Revisiting the safe haven property of gold and Bitcoin , 2020, Resources Policy.

[30]  An Econometric Analysis of Gold Prices in Turkey , 2015 .

[31]  Mark E. Wohar,et al.  Measuring the response of gold prices to uncertainty: An analysis beyond the mean , 2018, Economic Modelling.

[32]  Rangan Gupta,et al.  Forecasting China's foreign exchange reserves using dynamic model averaging: The roles of macroeconomic fundamentals, financial stress and economic uncertainty , 2014 .

[33]  U. Brandes.  A faster algorithm for betweenness centrality , 2001 .

[34]  Yun Long.  Visibility graph network analysis of gold price time series , 2013 .

[35]  H. Stanley,et al.  Extreme risk spillover effects in world gold markets and the global financial crisis , 2016 .

[36]  Terence C. Mills,et al.  Statistical analysis of daily gold price data , 2004 .

[37]  Ying Fan,et al.  A Dynamic Analysis on Global Natural Gas Trade Network , 2014 .

[38]  H. J. Chun,et al.  A low-cost optical transducer utilizing common electronics components for the gold nanoparticle-based immunosensing application , 2015 .

[39]  Christian Pierdzioch,et al.  Does Uncertainty Move the Gold Price? New Evidence from a Nonparametric Causality-in-Quantiles Test , 2016 .