Gold Volatility Research Articles

Time-varying risk aversion and realized gold volatility

We study the in- and out-of-sample predictive value of time-varying risk aversion for realized volatility of gold returns via extended heterogeneous autoregressive realized volatility (HAR-RV) models. Our findings suggest that time-varying risk aversion possesses predictive value for gold volatility both in- and out-of-sample. Time-varying risk aversion is found to absorb the in-sample predictive power of n index of economic policy uncertainty at a short forecasting horizon. We also study the out-of-sample predictive power of time-varying risk aversion in the presence of realized higher-moments, jumps, gold returns, a leverage effect as well as an index of economic policy uncertainty in the forecasting model. In addition, we study the role of the shape of the loss function used to evaluate losses from forecast errors for the role of time-varying risk aversion as a predictor of realized volatility. Overall, our findings show that time-varying risk aversion often captures information useful for out-of-sample prediction of realized volatility not already contained in the other predictors.

Modelling the Time-Varying Gold Price Volatility in Malaysia (2005 – 2018)

This study provides new insights into the recent time-varying pattern of gold volatility surrounding the Subprime Global Financial Crisis (GFC) 2008 with special emphasis given to small open economy of Malaysia. Using daily data of gold bullion Kijang Emas issued by the Bank Negara Malaysia from August 2005 till July 2018, the study modelled the gold volatility using different models of ARCH-GARCH family. Under the context of emerging economy, the study found significant enhanced gold volatility during the middle of GFC, signifying that gold price is highly sensitive to specific economic disturbance. Next, our result displayed inverted asymmetry effect where investors interpret gold as a safe-haven investment during good gold returns, hence contributed to enhanced gold volatility. Finally, bilateral exchange rate (MYR/USD) affected volatility with positive sign, indicating that gold investment could be adversely affected during the depreciation of the Malaysian Ringgit.

A quantile regression analysis of flights-to-safety with implied volatilities

In this paper, we perform a quantile regression analysis of flights-to-safety with the implied market volatilities of stock, gold, gold-mining, and silver. We verify whether flights-to-safety from US equities to gold are significant under different volatility conditions. We test for linear and nonlinear Granger-causality in quantiles. We find unidirectional causality running from the volatility of stock market to the market volatilities of gold, gold-mining, and silver. Besides, there is no causality between gold and silver market volatilities. We also find evidence of unidirectional causality from the market volatilities of stock, gold, and silver to the gold-mining volatility in lower- and upper-tail quantiles. Therefore, gold-mining stocks act as a good substitute for gold, coupled with negative return correlations between these two assets. Overall, our results have important implications for adopting optimal hedging and investing strategies.

Conditional quantiles and tail dependence in the volatilities of gold and silver

We study the dependency structure between option-implied volatilities of gold and silver markets via the application of a copula-based quantile regression. First, we conduct a static analysis and show that the asymptotic lower tail dependence is only pronounced in the low volatility regime of both gold and silver markets. Second, given the existence of a bi-directional causality between the two option-implied volatilities, we consider the lead-lag relationship via non-parametric tail dependence estimators. Results indicate an extreme tail dependency in lower and upper quantiles, with evidence of an asymmetric behavior between/for low and high volatility regimes. Our findings have implications to investors and risk managers. Practically, findings imply evidence of predictability of the probability of gold implied volatility based on the lagged silver implied volatility across different quantiles. Another implication concerns a volatility-based trading strategy, especially during in tandem occurrence of high volatility regimes, which involves the simultaneous selling of an out-of-the money call and put with different strike prices on gold implied volatility.

Forecasting gold futures market volatility using macroeconomic variables in the United States

The U.S. gold futures market has recently attracted significant attention and the gold volatility is closely linked to macroeconomics. As such, the question is how to analyze the impact of various macroeconomic variables on gold. We use the GARCH-MIDAS (mixed data sampling) model to investigate whether macroeconomic variables can improve the predictions on the volatility structure of U.S. gold futures. Our empirical results reveal that macroeconomic variables have a significant influence on gold volatility, especially during and after the global financial crisis, indicating macroeconomic variables are driving factors of the long-term volatility on the U.S. gold futures market. Additionally, we use principal component analysis to obtain key information on different macroeconomic variables and further investigate their joint effects on the volatility of gold futures, finding that the first and second principal components are good proxies of macroeconomic variables. Our results show that principal components improve forecast accuracy, as do macrovariables, which are robust to various forecast rolling window schemes.

Does gold a safe haven during the crisis period Explanation by behavioural finance for the GCC and BRIC markets

This paper investigates the interaction between the volatilities of gold price, stock markets of the emerging countries (GCC and BRIC) and investors' sentiment (VIX). Accordingly, we first examined the effect of the investors' sentiment shocks on the volatility of gold price and stock markets and analysed the dynamic correlation between them. Second, we evaluated the optimal weights, the optimal hedge and the hedging effectiveness ratios for gold-stock portfolio holdings. The empirical results showed that the shocks of investors' sentiment significantly affect the volatilities of gold and emerging stock markets. Moreover, a sharp fall in the dynamic conditional correlation was observed between gold-VIX and gold-emerging stock market pairs during the period of stress. These results provide the orientation of investors to the gold market for hedging when the fear sentiment and the pessimism dominate the stock markets.

Does gold a safe haven during the crisis period Explanation by behavioural finance for the GCC and BRIC markets

This paper investigates the interaction between the volatilities of gold price, stock markets of the emerging countries (GCC and BRIC) and investors' sentiment (VIX). Accordingly, we first examined the effect of the investors' sentiment shocks on the volatility of gold price and stock markets and analysed the dynamic correlation between them. Second, we evaluated the optimal weights, the optimal hedge and the hedging effectiveness ratios for gold-stock portfolio holdings. The empirical results showed that the shocks of investors' sentiment significantly affect the volatilities of gold and emerging stock markets. Moreover, a sharp fall in the dynamic conditional correlation was observed between gold-VIX and gold-emerging stock market pairs during the period of stress. These results provide the orientation of investors to the gold market for hedging when the fear sentiment and the pessimism dominate the stock markets.

Bitcoin(Gold)’s Hedge․Safe-Haven․Equity․Taxation

Btcoin has made a big progress through anonymity, decentralized authority, sharing economy, multi-ledger book-keeping, block-technology and the convenient financial vehicle. Bitcoin has the characteristics of mining and supply by decentralized suppliers, limited supply quantity and the partial money-like function as well as gold. The paper studies the hedge and safe-haven of Bitcoin and gold on daily frequency data over the period of July 20, 2010-Dec. 27, 2017 employing Asymmetric Vector GARCH. It finds that gold has the hedge and safe-haven against inflation and capital markets while Bitcoin has the weak hedge and the weak safe-haven. It shows insignificant effects of inflations of US and Korea on the volatilities of Bitcoin and gold. It also suggests the necessity of clearing of vagueness behind the anonymity for fair and transparent trade through the law application in the absence or fault in law (Lucken im Recht). following the spirit of the living constitution (lebendige gutes Recht oder Vorschrift). The relevant institutions are hoped to be given some of obligations such as registration, minimum required capital. report, disclosure, explanation, compliance and governance with autonomous corresponding rights. The study also suggests the reestablishment of the relevant financial law and taxation law. The hedge would not be successfully accomplished without the vigilant cautions of investors.

Modelling time varying volatility spillovers and conditional correlations across commodity metal futures

This paper examines how the most prevalent stochastic properties of key metal futures returns have been affected by the recent financial crisis using both mapped and unmapped data. Our results suggest that copper and gold futures returns exhibit time-varying persistence in their corresponding conditional volatilities over the crisis period; in particular, such persistence increases during periods of high volatility compared with low volatility. The estimation of a bivariate GARCH model further shows the existence of time-varying volatility spillovers between these returns during the different stages of such a crisis. Our results, which are broadly the same in relation to the use of mapped or unmapped data, suggest that the volatilities of copper and gold are inherently linked, although these metals have very different applications.

Return and Volatility Spillovers: An Evaluation of India's Demonetization Policy

We investigate the evolution of dynamic interactions among five major financial assets in the Indian economy, which its recent demonetization policy tried to influence. Spillovers account for more than 25 percent of the forecast error variance in all the five markets. In terms of total spillovers, the banking and the real estate sectors matter the most for the Indian economy. Gold market is responsible for the highest net volatility spillovers to other markets. Spillovers show major trends and cycles in their time series plots. The US economy transmits shocks directly to the key sectors of the Indian economy and via the gold and the foreign exchange markets. The events such as the election of the National Democratic Alliance government in India and the Indian government’s demonetization exercise were contemporaneous to some of the major episodes of return and volatility spillovers in the analyzed assets. The increased regulatory risk for the Indian IT sector post India’s demonetization policy and President Trump’s election seems to have increased the importance of the IT sector for gold and banking sector volatility shock transmission.

Volatility of main metals forecasted by a hybrid ANN-GARCH model with regressors

A hybrid model is analyzed to predict the price volatility of gold, silver and copperThe hybrid model used is a ANN-GARCH model with regressors.APGARCH with exogenous variables is used as benchmark.The benchmark is better than the classical GARCH used in previous studies.The incorporation of ANN into the best Garch with regressors increases the accuracy. In this article, we analyze volatility forecasts associated with the price of gold, silver, and copper, three of the most important metals in the world market. First, a group of GARCH models are used to forecast volatility, including explanatory variables like the US Dollar-Euro and US Dollar-Yen exchange rates, the oil price, and the Chinese, Indian, British, and American stock market indexes. Subsequently, these model predictions are used as inputs for a neural network in order to analyze the increase in hybrid predictive power. The results obtained show that for these three metals, using the hybrid neural network model increases the forecasting power of out-of-sample volatility. In order to optimize the results, we conducted a series of sensitizations of the artificial neural network architecture and analyses for different cases, finding that the best models to forecast the price return volatility of these main metals are the ANN-GARCH model with regressors. Due to the heteroscedasticity in the financial series, the loss function used is Heteroskedasticity-adjusted Mean Squared Error (HMSE), and to test the superiority of the models, the Model Confidence Set is used.

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.

Cointegration and nonlinear causality amongst gold, oil, and the Indian stock market: Evidence from implied volatility indices

The emerging economy of India counts gold and oil amongst its top imports, suggesting that the prices of these resources affect the domestic inflation and stock market. Expectations on future volatility in these prices might lead to changes in the expected (implied) volatility of the Indian stock market. Unlike prior studies, we use implied volatility indices to examine the cointegration and nonlinear causality amongst international gold, crude oil, and the Indian stock market. Results indicate the presence of cointegration relationships and a nonlinear and positive impact of the implied volatilities of gold and oil on the implied volatility of the Indian stock market. Interestingly, there is evidence of an inverse bi-directional causality between the implied volatilities of gold and oil prices.

A volatility-based approach to gold as a safe haven: can it explain the abnormalities in gold returns during periods of extreme financial adversity?

In this paper, the authors provide an explanation of the abnormal behavior of gold returns between the 1st of January 2008 and the 31st of December 2013. The authors suggest a behavioral finance foundation to the fact that gold returns exceed those of a wide range of other assets over this period. The approach rests on the safe haven (SH) motif for flights to gold during heavy financial stress periods. The prevailing Baur-Lucey-McDermott paradigm on gold as a SH is shown to be insufficient, as it ignores the roles of volatility and risk preferences. The auhors suggest a formal SH definition, recovering those elements from behavioral finance. Contrary to the previous paradigm, the approach is data-consistent, in the sample period. The authors find that gold is a SH for all stock markets considered, some exchange rates, and even Euro Area sovereign bonds (including German bunds). They estimate the SH risk premium in all cases. The authors find that investors perceive the distinction between good and bad volatility, and that they do not ask for excess returns when gold volatility is high for SH reasons. This is consistent with the literature on the low frequency of idiosyncratic shocks in the gold market. Furthermore, the authors find evidence that, in a period of high financial uncertainty, fund managers building portfolios consisting only of gold might be acting rationally, contrary to the finance common sense for normal periods. In fact, in the sample period, gold is even strictly dominant in mean-variance terms, when compared to equity. Keywords: safe haven, gold, euro debt crisis, risk preferences, fund management. JEL Classification: C22, C58, G01, G11, G15

Examining the flight-to-safety with the implied volatilities

This paper investigates the flight-to-safety phenomenon by examining the interactions between the stock market volatility (VIX) and volatilities of the Treasury note, gold, and silver markets. We find that increases in VIX lead to contemporaneous and delayed increases in the volatilities of T-note, gold, and silver prices. The VIX Granger causes the volatilities of T-note, gold, and silver markets, but the latter volatilities do not predict the stock market volatility. Changes in VIX explain more of volatility increases in T-note and gold prices during the financial crisis than in other periods. The leading positive effect of VIX on other expected volatilities, along with the possible negative asset correlations, complements the cross-market hedging and is consistent with the flight-to-safety phenomenon.

On exchange-rate movements and gold-price fluctuations: evidence for gold-producing countries from a nonparametric causality-in-quantiles test

The links between exchange-rate movements and gold-price fluctuations have been extensively studied in earlier research using various econometric techniques. Our contribution to this research is that we apply a novel nonparametric causality-in-quantiles test to study the causal links between exchange-rate movements and gold-price fluctuations. We use daily data for the sample period 1994–2015 for major gold-producing countries to illustrate the novel test. We find that, for the majority of countries, gold-price fluctuations help to predict in sample the returns and the volatility of exchange rates. While exchange-rate movements predict in sample gold volatility, they do not predict gold returns.

True or Spurious Long Memory?: Evidence from Russian Precious Metals Market

We examine the long memory property and structural break in the spot and futures gold volatility in Russia from 2008 through 2013. We find strong evidence of long memory in the volatility of both spot and futures gold series. The break dates are associated with the recent global financial crisis. Moreover, we investigate the volatility spillover effect between the Russian spot and futures gold markets using the corrected Dynamic Conditional Correlation model (cDCC). The findings show relatively high level of conditional correlation between spot and futures gold returns. This outcome decreases the portfolio diversification benefits for gold investors.

The volatility dynamics of spot and futures gold prices: Evidence from Russia

We examine the long memory property and structural break in the spot and futures gold volatility in Russia from 2008 through 2013. We find strong evidence of long memory in the volatility of both spot and futures gold series. The break dates are associated with the recent global financial crisis. Moreover, we investigate the volatility spillover effect between the Russian spot and futures gold markets using the corrected Dynamic Conditional Correlation model (cDCC). The findings show relatively high level of conditional correlation between spot and futures gold returns. This outcome decreases the portfolio diversification benefits for gold investors.

Does uncertainty move the gold price? New evidence from a nonparametric causality-in-quantiles test

Much significant research has been done to study the links between gold returns and the returns of other asset classes in times of economic crisis and high uncertainty. We contribute to this research by using a novel nonparametric causality-in-quantiles test to study how measures of policy and equity-market uncertainty affect gold-price returns and volatility. For daily and monthly data, we find evidence of causality running from various uncertainty measures to both gold returns and volatility. For quarterly data, evidence of causality weakens and is significant only for some uncertainty measures and only for gold volatility.

A Study of Excess Volatility of Gold and Silver

This paper discusses the case of strong path dependency in asset prices from the theoretical and empirical standpoints. Specifically, it demonstrates persistence of excess volatility in the gold spot price data that engenders excessive path dependence, whereas it is not the same with silver. For this study, we use the extreme value estimator proposed by Rogers and Satchell (1991) and the VRatio proposed by Maheswaran et al (2011). The data for the study is for the period from January, 2001 to October, 2015. We use multiple-days’ time horizons for examining the excess volatility with a better approximation of Brownian motion in the data. We capture the excess volatility in the gold data using the Binomial Markov Random Walk model. In this paper, we also utilize a new measure of risk called the Expected Lifetime Shortfall (ELS) ratio, to test for the presence of mean reversion in asset prices. Using this ratio, one can observe that the strong mean-reverting characteristic in gold makes it a better investment choice than silver, in general, in the medium term.