Gold Price Volatility Research Articles

Macroeconomic Determinants of Volatility for the Gold Price in Ethiopia: The Application of GARCH and EWMA Volatility Models

Modelling and forecasting of commodity price volatility has important applications for asset management, portfolio analysis and risk assessment due to the simple fact that volatility has informational content and contains signals of the market information flow. This article models and forecasts the gold price volatility using the exponentially weighted moving average (EWMA) and the generalized autoregressive conditional heteroscedasticity (GARCH) models for the period from 1998 to 2014. The gold series shows the classical characteristics of financial time series, such as leptokurtic distributions, data dependence and strong serial correlation in squared returns. Hence, the series can be modelled using both EWMA and GARCH-type models. Among the GARCH-type models, GARCH-M(2,2) with Student’s t distribution for the residuals was found to be the best-fit model. Moreover, the manuscript finds that interest rates, exchange rates and crude oil prices have a significant impact on gold volatility. The risk premium effect is found to be positive and statistically significant, suggesting increased volatility is followed by a higher mean. Finally, a comparison is made between the GARCH and the EWMA models. Using the relative mean squared error and mean absolute error measures, the empirical result suggests that GARCH models with explanatory variables are superior for volatility forecasting.

Quantitative Easing Program and Financial Market Volatility in Indonesia

This research aims to examine the impact of the USD money supply during and before quantitative easing program towards financial market volatility in Indonesia which is proxied by variance of financial market index such as IHSG, Gold Price in IDR, and Exchange Rate IDR/USD to find out the effect of the excess USD money supply on Indonesias financial market volatility. This reseacrh has used monthly time series data of M1 of USD, IHSG, IDR/USD Exchange Rate, and Gold Price from December 2008 to December 2013. TGACRH in this research is used to find out wheter the volatility or variance at previous time affects volatility of these financial market index at present time and assymetric information is exist in the financial market index. The result showed that theres a difference between the effect of USD money supply to financial market index volatility in Indonesia during QE program and before QE program. Before and during QE program, USD money supply positively affects IDR/USD exchange rate volatiliy and IHSG volatility and negatively affects Gold Price volatility. During QE program, USD money supply negatively affects volatility of IDR/USD exchange rate and IHSG, and positively affects Gold Price volatility.

Multi-Scale Volatility Feature Analysis and Prediction of Gold Price

Volatility of gold price is of great significance for avoiding the risk of gold investment. It is necessary to understand the effect of external events and intrinsic regularities to make accurate price predictions. This paper first compared EMD with CEEMD algorithm, and the results find that CEEMD algorithm performance is better than that of EMD in analysis gold price volatility. Then this paper uses the complementary ensemble empirical mode decomposition (CEEMD) to decompose the historical price of international gold into price components at different frequencies, and extracts a short-term fluctuation, a shock from significant events and a long-term price. In addition, this paper combines the Iterative cumulative sum of squares (ICSS) with Chow test to test the three event prices for structural breaks, and analyzes the effect of external events on volatility of gold price by comparing the external events with the test results for structural breaks. Finally, this paper constructs support vector machine (SVM) models and artificial neural network (ANN) on three series for prediction, and finds that the SVM performed better in gold price prediction in one-step-ahead and five-step-ahead, and when we combine the SVMs and ANNs with price components to make predictions, the error of the combined prediction is smaller than SVMs and ANNs with separate terms of series extracted.

The asymmetric volatility in the gold market revisited

Based on 13.5 years of intraday data, this paper sheds light on the inverse asymmetric volatility effect inherent in the gold market. After decomposing realized volatility into positive and negative semivariance, rolling estimations of the HAR model uncover the relative importance of the long-term positive semivariance and reveal the dynamics of the individual volatility components over time. Two effects are identified: The relevance of the short-term negative semivariance is rather pervasive while the impact of the positive semivariance is strongly correlated with the overall development of the gold market. The asymmetric nature of gold price volatility is multi-faceted and hence more complex than previously documented.

Googling gold and mining bad news

This paper studies investor's attention to gold price movements by analyzing the relationship between gold price changes and internet search queries for gold. We find a positive relationship of gold price volatility and search queries and a strong asymmetric effect of negative gold price changes on search queries indicating a preference to mine (google) bad news rather than good news. The analysis of silver, palladium and platinum demonstrates that the findings for gold are unique.

Long memory volatility of gold price returns: How strong is the evidence from distinct economic cycles?

This paper examines the long memory behavior in the volatility of gold returns using daily data for the period 1985–2009. We divided the whole sample into eight sub-samples in order to analyze the robustness and consistency of our results during different crisis periods. This constitutes our main contribution. We cover four major world crises, namely, (i) the US stock market crash of 1987; (ii) the Asian financial crisis of 1997; (iii) the World Trade Center terrorist attack of 2001 and finally, (iv) the sub-prime crisis of 2007, in order to investigate how the fractional integrated parameter of the FIGARCH(1,d,1) model evolves over time. Our findings are twofold: (i) there is evidence of long memory in the conditional variance over the whole sample period; (ii) when we consider the sub-sample analysis, the results show mixed evidence. Thus, for the 1985–2003 period the long memory parameter is positive and statistically significant in the pre-crisis sub-samples, and there is no evidence of long memory in the crisis sub-sample periods; however the reverse pattern occurs for the 2005–2009 period. This highlights the unique characteristics of the 2007 sub-prime crisis.

Gold price volatility: A forecasting approach using the Artificial Neural Network–GARCH model

One of the most used methods to forecast price volatility is the generalized autoregressive conditional heteroskedasticity (GARCH) model. Nonetheless, the errors in prediction using this approach are often quite high. Hence, continued research is conducted to improve forecasting models employing a variety of techniques. In this paper, we extend the field of expert systems, forecasting, and model by applying an Artificial Neural Network (ANN) to the GARCH method generating an ANN–GARCH. The hybrid ANN–GARCH model is applied to forecast the gold price volatility (spot and future). The results show an overall improvement in forecasting using the ANN–GARCH as compared to a GARCH method alone. An overall reduction of 25% in the mean average percent error was realized using the ANN–GARCH. The results are realized using the Euro/Dollar and Yen/Dollar exchange rates, the DJI and FTSE stock market indexes, and the oil price return as inputs. We discuss the implications of the study within the context of the discipline as well as practical applications.

The Interactions among Gold, Oil, and Stock Market: Evidence from S&P500

Abstract The inter-relationship between financial and commodity markets is one of the most challenging issues for investors. The volatility in one market might affect the price index of the other market. The aim of this paper is to test whether gold price, oil price, gold price volatility (GVZ) and oil price volatility (OVX) have significant effect on stock market price index (GSPC) or not. In order to carry out the task, due to the properties of the data, the ARDL co-integration approach has been used to check the long–run relationship among OVX and GVZ; as proxies of oil and gold market volatility indexes; and S&P500 market price index. Obtained results indicate the presence of long-run equilibrium among the variables under investigation and reveal that S&P500 stock market price index converges to its long-run equilibrium level by 1.2% speed of daily adjustment by contribution of oil and gold market prices and their volatilities.

Commodity prices and exchange rate volatility: Lessons from South Africa's capital account liberalization

We examine the relationship between the South African Rand and the gold price volatility using monthly data for the period 1979–2010. Our main finding is that prior to capital account liberalization the causality runs from the South African Rand to the gold price volatility but the causality runs the other way around for the post-liberalization period. This finding suggests that gold price volatility plays a key role in explaining both the excessive exchange rate volatility and current disproportionate share of speculative (short-run) inflows that South Africa has been coping with since the opening up of its capital account.

Macro-factors on gold pricing during the financial crisis

Purpose – This study aims to propose the idea of which macro-factors and how the macro-factors impact on the gold price. Design/methodology/approach – An EGARCH model is applied to test the volatility of gold price. A VAR method is applied to validate the idea by decomposing gold's value into three parts according to its features. Findings – Three macro-factors have significant impact on the gold's price. The USDX index is negatively correlated with the gold price, while the CRB index and the US Treasury CDS spreads are positively correlated with the gold price. In particular, it is found that the one-lagged CRB index, one-lagged USDX index, and two-lagged US Treasury CDS spreads have significant impact on the gold price. Research limitations/implications – The findings in this study suggest a normal case of the gold price. However, in particular cases, new models or new parameters may need to be introduced. Practical implications – This paper bridges the gap between theory and practice on the gold pricing model. The three-factor model can be used for trading in the field of gold investment. Originality/value – This paper provides a composite idea for investors and researchers to study the gold price.

Gold Price and Exchange Rate Volatility: Effects of Economic Sanctions

The gold market has recently attracted much attention and gold prices have fluctuated over the past few years. Therefore, the aim of this study is investigate and analyze the trend of gold prices over the past five years in Iran. The importance of this topic is coming from uncertainty and volatility of the gold market, gold excitement and lack of a correct analysis of the market trends with reference to the probable factors which affect the gold price. The studys period refers to the ended five-year in December 2012. This research from methodological point of view is a descriptive-perspective analysis that examines the influence of variables such as the gold price on the market, the official exchange rate and non-official rate on the Irans market, the price of Brent crude oil, the global rate per ounce gold and foreign economic sanction. Vector auto regression model is used to analyze the results of the study. The results indicate that gold price is affected from exchange rates fluctuations and gold global rate, it is also noteworthy to mention that the most important factors affecting the recent volatility in the Irans gold market and the exchange rate is the economic sanctions which have been imposed against Iran.

How does oil market uncertainty interact with other markets? An empirical analysis of implied volatility index

OVX (Crude oil volatility index), as a measure of oil market uncertainty and new volatility derivatives published by CBOE (Chicago Board Options Exchange) during the 2008 global financial crisis, provides a direct prediction of the market's expectation for future 30-day crude oil price volatility. This paper investigates the short- and long-term cross-market uncertainty transmission implied by OVX and other important volatility indices which are VIX (stock market volatility index), EVZ (euro/dollar exchange rate volatility index) and GVZ (gold price volatility index). The results indicate that there are no strong long-run equilibrium relationships among these volatility indices, which indirectly verify the effectiveness of cross-market volatility portfolio strategy for risk hedge. Furthermore, OVX is significantly influenced by other ones, which indicates that investors' volatility expectation in the oil market become more sensitive to uncertainty shocks from other markets when the global economic situation is extremely unstable. Finally, impacts of interior and exterior uncertainty shocks on OVX are found to be positive and transient. And the significant short-term uncertainty transmission between oil and other major markets has been confirmed.

Effects on Economic Growth of Prices of Gold and Oil: An Application by MS-VAR

Oil and gold are the two strategic commodities which have received much attention. The prices of oil and gold, which are the world’s most traded commodities, are very volatile. This paper aims to analyze the impact of oil shock and gold price on the business cycles of some developed countries: the United States, the United Kingdom, Germany, Canada, Australia and Italy. MS-VAR models have been extended to verify if the inclusion of oil prices, Gold Price as exogenous variables improves the ability of each specification and to identify the different phases of the business cycle for each country. MS-ARX and MS-VAR models were selected for capturing the 2 or 3 regimes for Austria, Canada, Germany, Italy, United Kingdom, France and USA. OP and AP variables were taken as exogenous for univariate MS(.)-ARX(.) models. When the transition probabilities are taken into account, important asymmetries in oil prices were recognized. There are two econometric results; the results for volatility of oil and gold price and the result for countries.The macro-economic policy studies had been made with MSIA(3)-VARX(4) models that AP and OP were taken as exogenous. The results highlighted the importance of energy policy on economic growth, economic development and welfare.

The Islamic Gold Dinar: An Alternative Hedging Tool?

This study examines the viability of the Islamic Gold Dinar as a hedging tool against currency exchange rate risk. Using the Auto Covariance Ratio the paper attempts to identify the percentage of time gold value is stable in the currency exchange market on a daily basis for a period of 12 years against the US Dollar and Malaysian Ringgit. This study also examines the volatility of gold prices relative to a host of other currencies using conventional standard deviation. Our findings show that the value of gold is more stable than the US dollar and Malaysian Ringgit over the sample period. However our findings also reveal that the volatility of gold prices itself has been progressively rising since 2003. Further tests reveal that the volatility of gold prices are highly correlated with the volatility of other national currencies. We conclude that the Islamic Gold Dinar cannot as yet be used as a hedging tool for exchange rate risks.

Commodity Prices and Exchange Rate Volatility: Lessons from South Africa's Capital Account Liberalization

We examine the relationship between South African Rand and gold price volatility using monthly data for the period 1980-2010. Our main findings is that prior to capital account liberalization the causality runs from South African Rand to gold price volatility but the causality runs the other way around for the post-liberalization period. These findings suggest that gold price volatility plays a key role in explaining both the excessive exchange rate volatility and current disproportionate share of speculative (short-run) inflows that South Africa has been coping with since the opening up of its capital account.

Is Gold a ‘Safe-Haven’? - An Econometric Analysis

Gold has been considered a resplendent and highly coveted precious metal since the genesis of humankind. Gold standards have been the most common basis for monetary policies throughout history. Recently, gold price volatility has garnered the attention of many researchers, academicians and analysts. This paper is hence an attempt to analyze the correlation and causality relation that may run between gold prices and stock market returns across six countries. The study based on data related to six renowned stock exchanges, investigates the Granger causality in the Vector Error Correction Model for the period January 2002 to December 2011 and aims to analyze the reasons behind contrasting results observed across countries. The analysis provides the evidence of feedback causality between the variables. Thus, it concludes whether one variable can be used to predict the other, or not.

Forecasting Volatility of Gold Price Using Markov Regime Switching and Trading Strategy

In this paper, we forecast the volatility of gold prices using Markov Regime Switching GARCH (MRS-GARCH) models. These models allow volatility to have different dynamics according to unobserved regime variables. The main purpose of this paper is to find out whether MRS-GARCH models are an improvement on the GARCH type models in terms of modeling and forecasting gold price volatility. The MRS-GARCH is best performance model for gold price volatility in some loss function. Moreover, we forecast closing prices of gold price to trade future contract. MRS-GARCH got the most cumulative return same GJR model.

Oil and Gold Price Volatility

Volatility is a measure of the average yearly changes in the market prices. The high volatility of the oil price has been pivotal in the development of international petroleum exchanges. The low volatility of the gold price is the reason why market participants usually view gold as a wealth preserver especially during inflationary time periods. In this paper, we present the stylized facts of the oil and gold price volatility – for usage of optimizing the returns on an oil and gold portfolio.

The crude oil market and the gold market: Evidence for cointegration, causality and price discovery

Given that the gold market and the crude oil market are the main representatives of the large commodity markets, it is of crucial practical significance to analyze their cointegration relationship and causality, and investigate their respective contribution, from the perspective of price discovery, to the common price trend so as to interpret the dynamics of the whole large commodity market and forecast the fluctuation of crude oil and gold prices.Empirical analysis indicates that, first, there are consistent trends between the crude oil price and the gold price with significant positive correlation coefficient 0.9295 during the sampling period, from January of 2000 to March of 2008. Second, there can be seen a long-term equilibrium between the two markets, and the crude oil price change linearly Granger causes the volatility of gold price, but not vice versa; moreover, the two market prices do not face a significant nonlinear Granger causality, which overall suggests their fairly direct interactive mechanism. Finally, with regard to the common effective price between the two markets, the contribution of the crude oil price seems larger than that of the gold price, whether with the permanent transitory (PT) model (86.50% versus 13.50%) or the information share (IS) model (50.28% versus 49.72%), which implies that the influence of crude oil on global economic development proves more far-reaching and extensive, and its role in the large commodity markets has attracted more attention in recent years.

An examination of Australian gold mining firms’ exposure over the collapse of gold price in the late 1990s

PurposeThe purpose of this study is to examine the exposures of Australian gold mining firms in the highly volatile period from 1995 to 2000. This period has been characterized by significant changes in gold price due to bulk sale of gold by collective central banks. Specifically, the paper aims to investigate several firm‐specific factors that are hypothesized to carry substantial influence on gold beta.Design/methodology/approachTo estimate gold beta, we use the following multifactor model: Rg,t = α + βg GPRt + βx FXRt + βm Rm,t + εt, where Rg,t is the return on the gold stock Index at time t, GPRt is the gold price return denominated in US dollar at time t, FXRt is the foreign exchange return of Australian dollar in terms of US dollar at time t, Rm,t is the market return at time t, and εt is the random error term at time t.FindingsThe paper finds that the values of gold beta are consistently greater than one, implying the sensitive nature of firms’ stock returns to gold price changes. This also suggests that investors holding gold mining stock would receive higher percentage increases in stock returns from a percentage increase in gold price returns, as opposed to investors holding gold bullion. Furthermore, these values have changed substantially over time with significant changes in gold price volatility. The most important and consistent relationship that we find is the impact of firms’ hedging behavior on their respective gold betas. This is consistent with Tufano's study. It implies that firms, which hedge a greater proportion of their gold reserves, are less sensitive to movements in gold prices. The finding therefore supports the risk management theory that hedging increases shareholder's wealth. However, cash operating costs, cash reserves and the level of gold production seem to influence very little on the firms’ exposure to gold price changes.Originality/valueThis study is of interest and important to the stock mining companies and investors because the extent of the effect of gold price movements on the stock returns of gold mining companies has significant impacts on returns for both firms and investors especially in their risk management and investment decisions, respectively.