Gold Volatility Research Articles

Gold as a Hedge and a Diversifier: The Perspective from Lebanon

The purpose of this paper is to find out whether gold, valued in Lebanese terms, is a hedge against losses in purchasing power, and whether it is a portfolio diversifier. Gold is found to be a partial hedge to consumer price inflation, a weak hedge to the depreciation of the domestic currency, and a modest hedge to commodity price changes, like oil prices. Although in a multiple regression context gold seems to be unrelated to US stock prices, converted to Lebanese pounds, the bivariate correlation is positive and high. The diversification properties of gold, when it is added to (1) oil, to (2) the DJIA, to (3) the Euro, to (4) a deposit in Lebanese pounds, or to (5) oil, the DJIA, the Euro and a deposit in Lebanese pounds, all together, are studied. It is found that the diversification benefits are rather substantial. The implied price of risk is commensurate with the one in the US financial market. Finally, gold volatility is asymmetrically affected by positive and negative shocks in gold prices, similar to the evidence elsewhere. But unlike the evidence elsewhere the volatility of gold responds strictly to positive shocks but not to negative shocks.

Forecasting volatility in gold returns under the GARCH, IGARCH and FIGARCH frameworks: New evidence

This study employs three volatility models of the GARCH family to examine the volatility behavior of gold returns. Much of the literature on this topic suggests that gold plays a fundamental role as a hedge and safe haven against adverse market conditions, which is particularly relevant in periods of high volatility. This makes understanding gold volatility important for a number of theoretical and empirical applications, namely investment valuation, portfolio selection, risk management, monetary policy-making, futures and option pricing, hedging strategies and value-at-risk (VaR) policies (e.g. Baur and Lucey (2010)).We use daily data from August 2, 1976 to February 6, 2015 and divide the full sample into two periods: the in-sample period (August 2, 1976–October 24, 2008) is used to estimate model coefficients, while the out-of-sample period (October 27, 2008–February 6, 2015) is for forecasting purposes. Specifically, we employ the GARCH(1,1), IGARCH(1,1) and FIGARCH(1,d,1) specifications.The results show that the FIGARCH(1,d,1) is the best model to capture linear dependence in the conditional variance of the gold returns as given by the information criteria. It is also found to be the best model to forecast the volatility of gold returns.

Low-Frequency Volatility in China’s Gold Futures Market and Its Macroeconomic Determinants

We extract low- and high-frequency volatility from China’s Shanghai gold futures market using an asymmetric Spline-GARCH (ASP-GARCH) model. We then regress monthly low-frequency volatility on selected monthly macroeconomic indicators to study the impact of macroeconomy on gold futures market and to test for excess volatility. Our main result is volatility in China’s Shanghai gold futures market resulting from both macroeconomic fluctuations and investor behaviour. Chinese Consumer Price Index Volatility and US dollar volatility are the two main determinants of low-frequency gold volatility. We also find significant evidence of excess volatility, which can in part be explained in terms of loss-aversive investor behaviour.

Do Shining Precious Metals Overshadow Other Assets? Dynamic Spillovers Between Commodity and Currency Markets

In this study, we examine the dynamic link between returns and volatility of commodities and currency markets. In particular, based on weekly data on gold, silver, platinum, palladum, oil and the USD/EUR exchange rate over the period January 6, 1987 to July 22, 2014 we find the following empirical regularities. First, the static spillover analysis reveals that gold, silver and platinum are net transmitters of returns and volatility spillovers, while palladium, crude oil and USD/EUR exchange are net receivers of returns (volatility) spillovers. These results suggest that, the information contents of gold, silver and platinum can help improve forecast accuracy of returns (volatilities) on palladium, crude oil and currency returns (volatilities). Second, returns (volatilities) on gold are interestingly the largest gross transmitters of spillovers to the remaining assets in our model. Third, pairwise return (volatility) spillovers reveal relatively stronger bidirectional interdependences between gold and silver, and platinum and palladium. Fourth, the analysis of dynamic spillovers suggests that gold, silver and, to a lesser extent, platinum act as net transmitters of return (volatility) spillovers to palladium, crude oil and the USD/EUR exchange rate. Interestingly, the observed net transmission shows time- and event-specific patterns. For instance, for gold and silver (platinum) returns and volatility, it intensified (degraded) in the period marked by the global financial crisis. After the global financial crisis, the role of gold and silver (platinum) returns as net transmitters of shocks weakened (strengthened), while the role of gold, silver and platinum volatility as net transmitters of 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.

Investigating Intraday Interdependence Between Gold, Silver and Three Major Currencies: the Euro, British Pound and Japanese Yen

In this study, the interrelationship between major exchange rate returns (namely EUR/USD, GBP/USD, JPY/USD) and precious metal returns (gold and silver) is examined using a vector autoregressive model in a multivariate asymmetric GARCH framework on the intraday frequency. Our findings indicate a unidirectional volatility transmission from the majority of our currencies (EUR/USD, GBP/USD) to precious metals. The sluggish response of silver volatility to currency volatility shocks permits implementation of intraday profitable strategies, providing implications against market efficiency when analyzing intraday data. In the case of the British pound and Japanese yen, a volatility shock affects silver volatility more than gold volatility. Crisis events such as the Greek default and US credit rating downgrade reduce significantly the correlation of EUR/USD and gold/silver. The covariance between EUR/USD and silver increases after a volatility shock in EUR/USD. The same happens with JPY/USD and silver. These findings are important for portfolio managers and monetary authorities.

Long Memory and Structural Breaks in the Returns and Volatility of Gold: Evidence from Turkey

This paper examines the long memory properties and structural breaks in spot and futures gold returns and volatility in Turkey. The data covers the period from 2008 through 2013 in which gold prices hit an all-time high. ARFIMA-FIGARCH model provides evidence of anti-persistence in spot returns and a lack of long memory property in futures returns. Anti-persistence is indicative of an overreaction of gold prices to new information, thus, disconfirming the weak form of market efficiency. The findings further provide evidence of one structural break, which is associated with correction in the gold prices during the post-global financial crisis. The analysis suggests that the long memory is true, not spurious. This implies that long memory is a feature of the data instead of an outcome of structural changes.

Long memory and structural breaks in the returns and volatility of gold: evidence from Turkey

This article examines the long-memory properties and structural breaks in spot and futures gold returns and volatility in Turkey. The data cover the period from 2008 through 2013 in which gold prices hit an all-time high. ARFIMA–FIGARCH model provides evidence of dual long memory in spot series and a lack of long-memory property in futures returns. Anti-persistence in spot returns is indicative of an overreaction of gold prices to new information, thus disconfirming the weak form of market efficiency. The findings further provide evidence of one structural break, which is associated with correction in the gold prices during the post-global financial crisis. The analyses suggest that the long memory is true, not spurious. This implies that long memory is a feature of the data instead of an outcome of structural changes.

Exchange-Traded Funds on Gold - A Free Lunch?

Exchange-traded funds (ETFs) on indices provide access to a diversified portfolio of assets at a low cost. Whilst ETFs on single commodities have less obvious advantages its number has grown substantially in recent years. This paper analyzes the benefits and risks of single-asset ETFs theoretically and empirically using more than 80 physical and synthetic gold ETFs. We show that ETFs on gold provide a low cost and more liquid alternative to physical investments in gold. As a consequence, the introduction of ETFs on gold leads to a structural demand shift with implications for the price of gold, the volume traded and the volatility of gold. Our analysis provides new evidence on the financialization of commodities and can help to explain the growth of gold ETFs and changes in the price of gold.

Risk Premia in Gold Leasing Markets

Gold is an important global reserve asset, widely held by the ocial sector and private investors. In this paper, we study a measure of the opportunity costs of holding gold, the gold lease rates { interests paid in gold for borrowing gold. Gold lease rates are economically signicant in magnitude and display substantial variations over time. Using a term structure model with \unspanned risk factors, we nd that risk premia in gold lease rates are highly time-varying and strongly increasing in the level and slope of gold lease rates, as well as in gold volatility. Expected excess returns of \gold bonds are mostly positive, suggesting that they are perceived as risky investments.

Contemporaneous Spill-Over Among Equity, Gold, and Exchange Rate Implied Volatility Indices

This paper examines the contemporaneous spill-over effects among the CBOE implied volatility indices for stocks (VIX), gold (GVZ) and the exchange rate (EVZ). We use the 'identification through heteroskedasticity' approach of Rigobon (2003) to decompose the contemporaneous relationship between these implied volatility indices into causal relationships. Our findings suggest that there is strong unidirectional, spill-over from VIX to GVZ and EVZ, where increases in stock market volatility lead to increases in gold and exchange rate volatility; and bi-directional spill-over between GVZ and EVZ. We emphasize the implications of our model by comparing the impulse-responses generated by our structural VAR with the impulse-responses of a traditional VAR. Our results show that the responses to shocks originating in GVZ and EVZ are seriously overestimated in the traditional VAR. These findings on the direction and magnitude of spill-over and the long-run impact on volatility have important implications for portfolio and risk management.

Volatility transmission between gold and oil futures under structural breaks

This paper employs univariate and bivariate GARCH models to examine the volatility of gold and oil futures incorporating structural breaks using daily returns from July 1, 1993 to June 30, 2010. We find strong evidence of significant transmission of volatility between gold and oil returns when structural breaks in variance are accounted for in the model. We compute optimal portfolio weights and dynamic risk minimizing hedge ratios to highlight the significance of our empirical results. Our findings support the idea of cross-market hedging and sharing of common information by financial market participants.

Asymmetric Volatility in the Gold Market

The volatility of equity returns generally exhibits an asymmetric reaction to positive and negative shocks. Economic explanations for this phenomenon are leverage and a volatility feedback effect. This article studies the volatility of gold and demonstrates that there is an inverted asymmetric reaction to positive and negative shocks—positive shocks increase volatility more than negative shocks. The author argues that this effect is related to the safe-haven property of gold. Investors interpret positive gold price changes as a signal for future adverse conditions and uncertainty in other asset markets. This introduces uncertainty in the gold market and thus higher volatility. The empirical results hold for gold bullion and gold coins denominated in different currencies and for different return frequencies, sample periods, and distributional assumptions. Finally, the author shows that the inverted volatility effect of gold can lower the aggregate risk of a portfolio for specific correlation levels.

Asymmetric Volatility in the Gold Market

The volatility of equity returns generally exhibits an asymmetric reaction to positive and negative shocks. Economic explanations for this phenomenon are leverage and a volatility feedback effect. This paper studies the volatility of gold and demonstrates that there is an inverted asymmetric reaction to positive and negative shocks, i.e. positive shocks increase the volatility by more than negative shocks. The paper argues that this effect is related to the safe haven property of gold. Investors interpret positive gold price changes as a signal for future adverse conditions and uncertainty in other asset markets. This introduces uncertainty in the gold market and thus higher volatility. The empirical results hold for gold bullion and gold coins denominated in different currencies, for different return frequencies, sample periods and distributional assumptions. Finally, we show that the inverted volatility effect of gold can lower the aggregate risk of a portfolio for specific correlation levels.

The macroeconomic determinants of volatility in precious metals markets

This paper models the monthly price volatilities of four precious metals (gold, silver, platinum and palladium prices) and investigates the macroeconomic determinants (business cycle, monetary environment and financial market sentiment) of these volatilities. Gold volatility is shown to be explained by monetary variables, but this is not true for silver. Overall, there is limited evidence that the same macroeconomic factors jointly influence the volatility processes of the four precious metal price series, although there is evidence of volatility feedback between the precious metals. These results are consistent with the view that precious metals are too distinct to be considered a single asset class, or represented by a single index. This finding is of importance for portfolio managers and investors.

Volatility and Volatility Spillovers in Crude Oil and Precious Metals Markets

Crude oil and precious metals, silver and especially gold, prices have been more volatile in the recent years. Investors and speculators such as investment banks, hedge funds, mutual funds, etc. pay close attention to the price and volatility of these commodities. The daily returns of Brent, gold and silver from 8 April 1999 to 7 April 2009 are employed to model the volatility and volatility spillovers across markets. The univariate conditional volatility models suggest that there is time-varying volatility in all assets. Moreover, asymmetry is observed in the Brent and gold markets. For multivariate conditional volatility models, the previous gold and silver volatility have no spillovers effects to the Brent market. The previous volatility in Brent and silver are spillovers to the gold volatility, but only previous gold volatility spillovers to silver volatility. Therefore, silver and gold precious metals investors have to be aware of other precious metal market volatility. Particularly, gold investors should consider the previous Brent market volatility. Asymmetric effects exist in Brent and gold markets. Therefore, we can state that asymmetric models are clearly superior to symmetric models for the Brent and gold markets. However, rolling windows suggest that the assumption of constant conditional correlations is too restrictive.

Estimativa da volatilidade de projetos de bens minerais

A volatilidade dos projetos de bens minerais depende do seu fluxo de caixa futuro e, por isso, deve ser estimada usando-se as suas características econômico-financeiras e operacionais intrínsecas, ao invés de séries históricas. Em muitos casos, por inexistência de informações sobre o futuro do projeto, assume-se que a sua volatilidade seja igual à dos preços dos bens minerais (preço do ouro, petróleo, etc). Mas essa abordagem pode fornecer resultados incorretos. Para contornar essas limitações, esse trabalho estima a volatilidade de projetos de mineração e petróleo com base nas projeções de seu fluxo de caixa, considerando-se que o preço do bem mineral seja uma variável incerta e se comporte, ao longo do tempo segundo dois processos estocásticos: 1) movimento geométrico Browniano e 2) movimento de reversão à média. Um dos principais resultados desse modelo é que a volatilidade do projeto é igual à do preço, somente no caso de projetos com baixos custos operacionais e/ou preços elevados.