Metal prices and the business cycle

Labys, W.C; Achouch, A; Terraza, Michel

doi:10.1016/s0301-4207(99)00030-6

Cited by 75 publications

(47 citation statements)

References 23 publications

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“…Examples of times series econometrics approaches include Cuddington and Urzu´a (1989); Deaton and Miller (1995); Cashin and McDermott (2002) ;Cuddington, Ludema, and Jayasuriya (2007); and Gilbert (2007). A number of authors have analyzed the movement of metal prices over the business cycle as well as co-movements among commodity prices (see Labys, Achouch, and Terraza, 1999;McDermott, Cashin, and Scott, 1999;and Pindyck and Rotemberg, 1990). Lower frequency cycles in metals prices, in contrast, have received scant attention.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

Super Cycles in Real Metals Prices?

Cuddington¹,

Jerrett²

2008

IMF Staff Pap

151

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Section: Motivation and Backgroundmentioning

confidence: 99%

Super Cycles in Real Metals Prices?

Cuddington¹,

Jerrett²

2008

IMF Staff Pap

151

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“…First, there is dearth of research that considers dynamic (time-varying) spillovers among different commodities 3 Noteworthy, the observed negative relation between volatilities of crude oil and precious metals can be exploited by commodity portfolio managers the pricing of options (Hammoudeh and Yuan, 2008). 4 Pindyck and Rotemberg (1990) and Labys et al (1999) propound that co-movement in commodity prices has three main explanations. First, supply-and demand-related shocks in one commodity market may spill over into other markets.…”

Section: Introductionmentioning

confidence: 99%

Dynamic spillovers between commodity and currency markets

Antonakakis

Kizys

2015

International Review of Financial Analysis

208

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In this study, we examine the dynamic link between returns and volatility of commodities and currency markets. Based on weekly data over the period from January 6, 1987 to July 22, 2014, we find the following empirical regularities. First, our results suggest that the information contents of gold, silver, platinum, and the CHF/USD and GBP/USD exchange rates can help improve forecast accuracy of returns and volatilities of palladium, crude oil and the EUR/CHF and GBP/USD exchange rates. Second, gold (CHF/USD) is the dominant commodity (currency) transmitter of return and volatility spillovers to the remaining assets in our model. Third, the analysis of dynamic spillovers shows time-and event-specific patterns. For instance, the dynamic spillover effects originating in gold and silver (platinum) returns and volatility intensified (degraded) in the period marked by the global financial crisis. After the global financial crisis, the net transmitting role of gold and silver (platinum) returns shocks weakened (strengthened), while the net transmitting role of gold, silver and platinum volatility shocks remained relatively high. Overall, our findings reveal that, while the static analysis clearly classifies the aforementioned variables into net transmitters and net receivers, the dynamic analysis denotes episodes wherein the role of transmitters and receivers of return (volatility) spillovers can be interrupted or even reversed. Hence, even if certain commonalities prevail in each identified category of commodities, such commonalities are time-and event-dependent.

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“…These empirical properties of metal series should be incorporated into a formal model framework. In the current literature (Labys et al 1999, Lombardi et al 2012, Byrne et al 2013) a class of linear factor models with a single common and several idiosyncratic (individual) factors is mainly exploited in order to reflect all these characteristics with the exception of individual peaks. The common factor is responsible for the joint dynamics of the series whereas individual factors are introduced in order to cope with residual autocorrelation (Labys et al 1999).…”

Section: Introductionmentioning

confidence: 99%

“…In the current literature (Labys et al 1999, Lombardi et al 2012, Byrne et al 2013) a class of linear factor models with a single common and several idiosyncratic (individual) factors is mainly exploited in order to reflect all these characteristics with the exception of individual peaks. The common factor is responsible for the joint dynamics of the series whereas individual factors are introduced in order to cope with residual autocorrelation (Labys et al 1999). The obtained empirical results indicate that the extracted common factor is positively correlated to the major macroeconomic fundamentals such as industrial production, consumer or stock prices, and negatively correlated with real interest rates.…”

Section: Introductionmentioning

confidence: 99%

“…The benchmark model as in Labys et al (1999) and Vansteenkiste (2009) with a single common and several individual factors (states) is contrasted to more general approaches presuming two common factors with a number of individual factors. Since some parameters of these general models appear to be insignificant, we introduce zero restriction on factors loadings in order to find a suitable but parsimonious specification.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling dynamics of metal price series via state space approach with two common factors

Golosnoy

Rossen

2017

Empir Econ

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. www.econstor.eu November 2014 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means (electronic, mechanical, photocopying, recording or otherwise) without the prior written permission of the publisher. Terms of use: Documents in Modeling Dynamics of Metal Price Series via State Space Approach with Two Common FactorsVasyl Golosnoy * and Anja Rossen † November 13, 2014 AbstractIn this paper we model the dynamics of 100 years long monthly price series of eight non-ferrous and precious metals. Applying the state space framework we impose and identify two common factors related to non-ferrous and precious metals, respectively, which exhibit quite distinct autoregressive dynamics.The preferred two common factor specifications outperform single common factor approaches which are usually used in the current literature. Furthermore, we provide interpretation for the extracted common factors by investigating their exposure to the major macroeconomic fundamentals.

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Metal prices and the business cycle

Cited by 75 publications

References 23 publications

Super Cycles in Real Metals Prices?

Super Cycles in Real Metals Prices?

Dynamic spillovers between commodity and currency markets

Modeling dynamics of metal price series via state space approach with two common factors

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