scispace - formally typeset
Journal ArticleDOI

Copper price determination: fundamentals versus non-fundamentals

Juan Ignacio Guzmán, +1 more
- 01 Oct 2018 - 
- Vol. 31, Iss: 3, pp 283-300
Reads0
Chats0
TLDR
In this paper, the authors developed an empirical model for the copper market, consisting in a vector autoregression (VAR) with 16 variables, including both fundamentals and non-fundamentals.
Abstract
In mineral economics, there is quite broad consensus that market fundamentals (physical supply and demand) explain commodity price fluctuations, particularly in the medium and long term. However, following the recent price boom, some dissent has arisen about the role played by non-fundamentals such as liquidity or money supply in key countries and regions, the financialization of commodities and, particularly, financial speculation. This paper develops an empirical model for the copper market, consisting in a vector autoregression (VAR) with 16 variables, including both fundamentals and non-fundamentals. Since the variables’ impact probably changes over time, the 20 years studied are divided into three periods (1995–2003, 2003–2008, and 2008–2015) separated by two structural breaks related to the Chinese boom and the financial crisis/Great Moderation. The results show that, although the fundamentals are relevant in all the periods analyzed, liquidity and other macroeconomic variables are also necessary in order to understand the level of copper prices and their fluctuations. In the case of financial speculation, the results indicate that its impact was significant only in 2003–2008 and, even then, was smaller than that of the fundamentals and macroeconomic variables, explaining around 9% of the price increase in this period. The results support the conclusion that, for the purposes of modeling and forecasting, current models based only on the fundamentals cannot fully explain price dynamics which are shown to be, in general, more complex than has been assumed by mainstream mineral economics.

read more

Citations
More filters
Journal ArticleDOI

Expected prices, futures prices and time-varying risk premiums: The case of copper

TL;DR: In this paper, a three-factor no-arbitrage stochastic commodity pricing model is calibrated to copper using analysts' predictions provided by Bloomberg's Commodity Price Forecast and futures prices from the COMEX and LME metals exchanges.
Journal ArticleDOI

Cointegration between the structure of copper futures prices and Brexit

TL;DR: In this article, the impact of macroeconomic factors on the structure of copper futures prices was investigated in the context of a market shortage, Brexit-related macroeconomics and their effect on local companies.
Journal ArticleDOI

The Role of Financial Speculation in Copper Prices

TL;DR: In this article, the role played by financial speculation in copper price boom during the last decade was examined using least squares with breakpoints, and the results indicated that from January 1993 to December 2016 real copper spot prices have been characterized by structural changes and its determinants significantly varies in distinct periods.
Journal ArticleDOI

The asymmetric relationship between Baltic Dry Index and commodity spot prices: evidence from nonparametric causality-in-quantiles test

TL;DR: In this paper, a causality-in-quantiles (CiQ) model is used to model the causal relationship between BDI spot values and spot price of major dry bulk commodities like iron ore, aluminum, copper, agricultural products by considering 12-year of daily data.
References
More filters
Journal ArticleDOI

Time Series Analysis.

Journal ArticleDOI

Time series analysis

James D. Hamilton
- 01 Feb 1997 - 
TL;DR: A ordered sequence of events or observations having a time component is called as a time series, and some good examples are daily opening and closing stock prices, daily humidity, temperature, pressure, annual gross domestic product of a country and so on.
BookDOI

New Introduction to Multiple Time Series Analysis

TL;DR: This reference work and graduate level textbook considers a wide range of models and methods for analyzing and forecasting multiple time series, which include vector autoregressive, cointegrated, vector Autoregressive moving average, multivariate ARCH and periodic processes as well as dynamic simultaneous equations and state space models.
Journal ArticleDOI

Estimating and testing linear models with multiple structural changes

Jushan Bai, +1 more
- 01 Jan 1998 - 
TL;DR: In this article, the authors developed the statistical theory for testing and estimating multiple change points in regression models, and several test statistics were proposed to determine the existence as well as the number of change points.
Posted Content

Not All Oil Price Shocks are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market

TL;DR: In this paper, a structural decomposition of the real price of crude oil in four components is proposed: oil supply shocks driven by political events in OPEC countries; other oil supply shock; aggregate shocks to the demand for industrial commodities; and demand shocks that are specific to the crude oil market.
Related Papers (5)
Trending Questions (1)
Why copper prices are increasing 2021?

The results show that, although the fundamentals are relevant in all the periods analyzed, liquidity and other macroeconomic variables are also necessary in order to understand the level of copper prices and their fluctuations.