Can gold prices forecast the Australian dollar movements?

Apergis, Nicholas

doi:10.1016/j.iref.2013.04.004

Cited by 55 publications

(29 citation statements)

References 37 publications

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“…Reboredo (2013) and Reboredo and Rivera-Castro (2014a,b) confirm this finding. Wang and Lee (2011) use a threshold vector autoregressive model and find that this function of gold is also valid for the Japanese yen, while Jain and Ghosh (2013) and Apergis (2014) extend this result for the Indian rupee and Australian dollar, respectively. Studying the relationship between commodities and currencies, Antonakakis and Kizys (in press) find that gold is the dominant commodity transmitter of return and volatility spillover to other commodities and currencies.…”

Section: Gold In the Diversification Of Portfolios: A Literature Reviewmentioning

confidence: 91%

Is gold different for risk-averse and risk-seeking investors? An empirical analysis of the Shanghai Gold Exchange

Hoang

Wong

Zhu³

2015

Economic Modelling

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Section: Gold In the Diversification Of Portfolios: A Literature Reviewmentioning

confidence: 91%

Is gold different for risk-averse and risk-seeking investors? An empirical analysis of the Shanghai Gold Exchange

Hoang

Wong

Zhu³

2015

Economic Modelling

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“…10 On the benefits of forecasting the gold price, Apergis (2014) investigates whether the gold price has a reliable out-of-sample relationship with the Australian dollar/U.S. dollar nominal and real exchange rates using daily and quarterly data, respectively.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Forecasting the price of gold using dynamic model averaging

Aye

Gupta

Hammoudeh

et al. 2015

International Review of Financial Analysis

113

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We develop several models to examine possible predictors of the return of gold, which embrace six global factors (business cycle, nominal, interest rate, commodity, exchange rate and stock price) extracted from a recursive principal component analysis (PCA) and two uncertainty and stress indices (the Kansas City Fed's financial stress index and the U.S. Economic policy uncertainty index). Specifically, by comparing alternative predictive models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform linear models (such as the random walk) as well as the Bayesian model averaging (BMA) model. The DMS is the best predictive model overall across all forecast horizons. Generally, all the predictors show strong predictive power at one time or another though at varying magnitudes, while the exchange rate factor and the Kansas City Fed's financial stress index appear to be strong at almost all horizons and sub-periods. However, the forecasting prowess of the exchange rate is supreme. JEL Classification codes: C11, C53, F37, F47, Q02 Abstract We develop several models to examine possible predictors of the return of gold, which embrace six global factors (business cycle, nominal, interest rate, commodity, exchange rate and stock price) extracted from a recursive principal component analysis (PCA) and two uncertainty and stress indices (the Kansas CityFed's financial stress index and the U.S. Economic policy uncertainty index). Specifically, by comparing alternative predictive models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform linear models (such as the random walk) as well as the Bayesian model averaging (BMA) model. The DMS is the best predictive model overall across all forecast horizons.Generally, all the predictors show strong predictive power at one time or another though at varying magnitudes, while the exchange rate factor and the Kansas City Fed's financial stress index appear to be strong at almost all horizons and sub-periods. However, the forecasting prowess of the exchange rate is supreme.JEL Classification codes: C11, C53, F37, F47, Q02

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“…One strand of the literature has examined the determinants of gold prices with specific focus on the interplay of gold demand and supply forces (Apergis, 2014;Feldstein, 1980;Govett & Govett, 1982;Kaufmann & Winters, 1989;Rockerbie, 1999;Selvanathan & Selvanathan, 1999), whilst another strand has investigated the capacity of gold to act as a 'safe haven', 'store of value', hedging and derivative instrument, and risk and portfolio diversification security (Blose, 1996;Davidson et al, 2003;Capie et al, 2005;Conover, Jensen, Johnson, & Mercer, 2009;Baur & McDermott, 2010;Baur & Lucey, 2010;Wang, Wei, et al, 2011;Bialkowski et al, in press). A third strand of the literature has assessed the interdependencies, linkages, spillovers, information flow and efficiency amongst gold markets (e.g., Japan, UK, and US), and also between gold and other markets (e.g., stock, bond, and other precious metal markets) (Caminschi & Heaney, 2014;Chang et al, 2013;Ewing & Malik, 2013;Laulajainen, 1990;Lucey et al, 2013Lucey et al, , 2014Xu & Fung, 2005), whilst a fourth strand of the literature has sought to ascertain whether there exists a causality and/or co-integration relationship between gold prices/ markets and macroeconomic variables often by employing different versions of autoregressive conditional heteroscedasticity (ARCH) models (Blose, 2010a,b;Kutan & Aksoy, 2004;Mahdavi & Zhou, 1997;Pukthuanthong & Roll, 2011;Sjaastad, 2008;Sjaastad & Scacciavillani, 1996;Tully & Lucey, 2007;Zhang & Wei, 2010).…”

Section: Introductionmentioning

confidence: 99%

On the efficiency of the global gold markets

Ntim

English

Nwachukwu

et al. 2015

International Review of Financial Analysis

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a b s t r a c t a r t i c l e i n f o Available online xxxx JEL classification: G1 Financial markets G14 Information and market efficiency G15 International financial markets G17 Financial forecasting Keywords: Global gold markets Macroeconomic variables Random walks and martingales Weak-form efficiency Variance-ratios Ranks and signsThis paper examines the weak-form efficiency of the global gold markets with specific focus on the random walks (RWS) and martingale difference sequence (MDS) hypotheses, and consequently, investigates the extent to which predictability or non-predictability of global daily spot gold price return series behaviour can be explained by volatilities in macroeconomic fundamentals. We apply traditional parametric variance-ratio tests and their recent non-parametric modifications based on ranks and signs to one of the largest datasets on world gold markets to-date, consisting of daily spot price series of 28 emerging and developed gold markets from January 1968 to August 2014. First, our results show that gold markets in Egypt, Indonesia, Mexico, Nepal, Pakistan, Russia, Saudi Arabia, UAE and Vietnam are not weak-form efficient neither from the perspective of the strict RWS nor in the relaxed MDS sense. By contrast, RWS and MDS hypotheses cannot be rejected for gold markets in Hong Kong, Japan, Switzerland, UK and US at the conventional rejection levels. Results for gold markets in Australia, Thailand and Turkey are, however, mixed. Second, our findings show that greater changes in economic fundamentals are associated with lower levels of rejecting the RWS and MDS hypotheses. Third, our evidence shows that the probability of rejecting the weak-form efficiency is higher in emerging gold markets than developed ones. Fourth, our results show that the RWS hypothesis is rejected more frequently than its MDS alternative, and thereby justifying our decision to conduct an explicit test of the RWS and MDS hypotheses. Our results are robust to estimating subsamples, overlapping rolling windows and endogeneity corrected models, as well as controlling for a number of country-specific institutional and trading factors. Our findings have crucial implications for global portfolio managers, investors, poly-makers and regulatory authorities.

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Can gold prices forecast the Australian dollar movements?

Cited by 55 publications

References 37 publications

Is gold different for risk-averse and risk-seeking investors? An empirical analysis of the Shanghai Gold Exchange

Is gold different for risk-averse and risk-seeking investors? An empirical analysis of the Shanghai Gold Exchange

Forecasting the price of gold using dynamic model averaging

On the efficiency of the global gold markets

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