The existence of metal price cycles

Labys, W.C; Lesourd, JB; Badillo, D.

doi:10.1016/s0301-4207(98)00023-3

Cited by 56 publications

(25 citation statements)

References 24 publications

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“…Panas (2001) tests for evidence of long memory and chaos in six metal futures contracts that trade on the London Metal Exchange and finds evidence of long memory in two of the six contracts. Other related papers include Labys, Lesourd, and Badillo (1998), who test for evidence of cyclicality in the prices of nine metals, and Tully and Lucey (2007), who relate gold price volatility to macroeconomic factors in the form of a power GARCH model. Finally, Souza, Tabak, and Cajueiro (2008) examine fed funds futures for evidence of long memory.…”

Section: Fractional Integration In Futuresmentioning

confidence: 99%

Fractional Integration in Commodity Futures Returns

Elder

Jin

2009

The Financial Review

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We reexamine commodity futures returns for evidence of fractional integration utilizing two estimators based on wavelets. We summarize basic wavelet methods for signal processing and decompose commodity futures returns by wavelet scale. We find the evidence for long memory is not conclusive based on visual inspection of the wavelet decomposition, but formal statistical tests suggest evidence of long memory, in the form of antipersistence, in about half of agricultural commodity futures. We find little evidence of long memory in metal futures. Our results are useful in interpreting previous disparate findings based on frequency domain estimators. Copyright (c) 2009, The Eastern Finance Association.

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Section: Fractional Integration In Futuresmentioning

confidence: 99%

Fractional Integration in Commodity Futures Returns

Elder

Jin

2009

The Financial Review

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“…Precious metals are only included to the extent that they appear in articles that are otherwise concerned with industrial metals. A detailed classification and description of each of the 45 published papers reviewed below, namely Agbeyegbe (1992), Ben Nowman and Wang (2001), Bracker and Smith (1999), Bresnahan and Suslow (1985), Brunetti and Gilbert (1995), Canarella and Pollard (1986), Chang et al (1990), Chowdhury (1991), Fama and French (1988), Franses and Kofman (1991), Fraser and MacDonald, 1992), French (1983), Gilbert (1986Gilbert ( , 1995, Goss (1981Goss ( , 1983Goss ( , 1986, Gross (1988), Hall (1991), Hall and Taylor (1989), Hardouvelis and Kim (1995), Heaney (1998Heaney ( , 2002aHeaney ( , 2002b, Hill et al (1991), Hsieh and Kulatilaka (1982), Hussey and Quiroz (1997), Kocagil (1997), Krehbiel and Adkins (1993), Labys et al (1998), MacDonald and Taylor (1988a, 1988b, 1989, MacKinnon and Olewiler (1980). McKenzie et al (2001), McMillan and Speight (2001); Moore and Cullen (1995), Ng and Pirrong, (1994), Sephton and Cochrane (1990a, 1990b…”

Section: Published Research On Non-ferrous Metal Marketsmentioning

confidence: 99%

Econometric modelling of non‐ferrous metal prices

Watkins

McAleer

2004

Journal of Economic Surveys

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This article evaluates the significance of the empirical models and the distributional properties of prices in non-ferrous metal spots and futures markets published in leading refereed economics and finance journals between 1980 and 2002. The survey focuses on econometric analyses of pricing and return models applied to exchange-based spot and futures markets for the main industrially used non-ferrous metals, namely aluminium, copper, lead, nickel, tin and zinc. Published empirical research is evaluated in the light of the type of contract examined, frequency of data used, choice of both dependent and explanatory variables, use of proxy variables, type of model chosen, economic hypotheses tested, methods of estimation and calculation of SEs for inference, reported descriptive statistics, use of diagnostic tests of auxiliary assumptions, use of nested and non-nested tests, use of information criteria and empirical implications for non-ferrous metals.

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“…Evidence of this cyclicity is provided by Labys et al (1998), who show links between economic conditions and the behaviour of precious metal prices. Their tests reveal the presence of two cycles for metal prices: one short, which lasts less than a year, and one longer.…”

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confidence: 99%

Forecasting commodity prices: GARCH, jumps, and mean reversion

Bernard

Khalaf

Kichian

et al. 2008

Journal of Forecasting

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In examining stochastic models for commodity prices, central questions often revolve around time-varying trend, stochastic convenience yield and volatility, and mean reversion. This paper seeks to assess and compare alternative approaches to modelling these effects, with focus on forecast performance. Three specifications are considered: (i) random-walk models with GARCH and normal or Student- t innovations; (ii) Poisson-based jump-diffusion models with GARCH and normal or Student- t innovations; and (iii) mean-reverting models that allow for uncertainty in equilibrium price. Our empirical application makes use of aluminium spot and futures price series at daily and weekly frequencies. Results show: (i) models with stochastic convenience yield outperform all other competing models, and for all forecast horizons; (ii) the use of futures prices does not always yield lower forecast error values compared to the use of spot prices; and (iii) within the class of (G)ARCH random-walk models, no model uniformly dominates the other. Copyright © 2008 John Wiley & Sons, Ltd.

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The existence of metal price cycles

Cited by 56 publications

References 24 publications

Fractional Integration in Commodity Futures Returns

Fractional Integration in Commodity Futures Returns

Econometric modelling of non‐ferrous metal prices

Forecasting commodity prices: GARCH, jumps, and mean reversion

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