Forecasting metal prices: Do forecasters herd?

Pierdzioch, Christian; Rülke, Jan-Christoph; Stadtmann, Georg

doi:10.1016/j.jbankfin.2012.08.016

Cited by 50 publications

(20 citation statements)

References 16 publications

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“…Our time varying results are in line with those reported by Pierdzioch et al (2010) and Pierdzioch et al (2013) who detected evidence of anti-herding behavior among oil and metal price forecasters. In particular, according to Pierdzioch et al (2013) the anti-herding behavior was attributed to a tendency among forecasters to spread forecasts around market forecast.…”

Section: Rolling Window Analysis Of Herdingsupporting

confidence: 92%

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Do commodity investors herd? Evidence from a time-varying stochastic volatility model

2015

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Section: Rolling Window Analysis Of Herdingsupporting

confidence: 92%

“…In particular, according to Pierdzioch et al (2013) the anti-herding behavior was attributed to a tendency among forecasters to spread forecasts around market forecast. …”

Section: Rolling Window Analysis Of Herdingmentioning

confidence: 99%

Do commodity investors herd? Evidence from a time-varying stochastic volatility model

2015

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“…In agreement with this finding, Baffes (2007) documents lower pass-through from crude oil to prices of precious metals' prices than to prices of other commodities. Further, large swings in precious metals' prices are a source of changes in terms of trade (Aizenman et al, 2012;Pierdzioch et al, 2013), which by turn can trigger changes in the exchange rate (De Gregorio and Wolf, 1994). Also, commodity-price fluctuations can be an important source of changes in the exchange rate of commodity-dependent countries (Cashin et al, 2004).…”

Section: Return Spilloversmentioning

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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“…Pierdzioch et al [30] studied monthly survey data of price forecasts for nine metals compiled by Consensus Economics Forecast (CEF) for the time period 1995-2011.…”

Section: Review Of the Literaturementioning

confidence: 99%

“…The basic criteria are the statistical errors, RMSE, MAE, MSE, the hit rate, and the ROE. Comparison was undertaken for live cattle, wheat, gold and crude oil because we were able to find results in the literature only for these commodities(Tables 27,28,29,30).…”

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confidence: 99%

Commodities’ price trend forecasting by a neuro-fuzzy controller

2015

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This paper presents a novel technique to forecast the price trend (direction) of 25 different commodities, listed on international markets, using a neuro-fuzzy controller. The forecasting system is based on two independent adaptive neural fuzzy inference systems (ANFISs) that form an inverse controller for each commodity. The ANFIS controller belongs to direct control and is based on inverse learning, also known as general learning. Daily data return sets, for the period 14th October 2009 until 28th September 2012 for 25 different commodities, are used to learn and evaluate the proposed system. The results of the trading simulation and the experimental investigations carried out in the laboratory are provided. The forecast accuracy of the proposed technique is evaluated by out-of-sample tests. The return on equity based on the hit rate and the comparison of equity with the buy and hold strategy are the central evaluation criteria. The results are very encouraging, showing high accuracy of the hit rate reaching 68.33 % and a notable superiority of the return on equity when compared with the buy and hold strategy. Also the performance of the model is compared with that of other approaches.

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Forecasting metal prices: Do forecasters herd?

Cited by 50 publications

References 16 publications

Do commodity investors herd? Evidence from a time-varying stochastic volatility model

Do commodity investors herd? Evidence from a time-varying stochastic volatility model

Dynamic spillovers between commodity and currency markets

Commodities’ price trend forecasting by a neuro-fuzzy controller

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