Gold Volatility Research Articles

Natural resources and sustainable development: Evidence from the dynamic correlation between crude oil and gold market

Amidst the uncertainty in markets spurred by globalization and technological advancements, the volatility of the crude oil and gold markets, which are emblematic of natural resource markets (NRMs), profoundly impacts global economic sustainability. This study innovatively quantifies and contrasts the impact of macroeconomic uncertainty factors on the volatility and dynamic correlation of these pivotal NRMs by extending the GJR-GARCH-MIDAS-X and DCC-MIDAS-RC-X models grounded in global economic policy uncertainty (GEPU), trade policy uncertainty (TPU), monetary policy uncertainty (MPU), and geopolitical risk (GPR). Concurrently, employing principal component analysis (PCA) blends numerous uncertainty factors, heightening the models’ explanatory capacity and predictive precision. Empirical findings suggest that all uncertainty factors exert significant positive effects on both crude oil and gold volatility, albeit with heterogeneity. Furthermore, the correlation between crude oil and gold fluctuates over time under the influence of uncertainty factors. Additionally, this study revealed asymmetric effects on realized volatility between the two NRMs. This research not only provides novel theoretical insights and empirical evidence concerning the intricate dynamic interplay between the two NRMs, but also has significant ramifications for policymakers, market regulators, and investors in crafting effective strategies and decisions amidst market uncertainty.

Mineral policy and sustainable development goals: Volatility forecasting in the Global South's minerals market

This paper aims to evaluate the volatility of precious metals, specifically Palladium, Gold, and Platinum, within the context of the global minerals market. The research focuses on understanding the price dynamics of these metals and their implications for sustainable development, particularly in the Global South. The study employs a comprehensive approach, utilizing advanced machine learning and deep learning models such as GRU, Huber, Lasso, LSTM, Random Forest, Ridge Regression, SVM, ANN, and XGBoost. These models are assessed based on their forecasting accuracy for different time horizons, using metrics such as RMSE and MAPE. The findings reveal that the ANN, XGBoost, and LSTM models exhibit robust performance in forecasting the volatility of precious metals across various time horizons. The research highlights the unique volatility patterns of each metal and underscores the effectiveness of machine learning techniques in capturing these dynamics. The study acknowledges limitations such as the exclusion of macroeconomic and geopolitical factors in the forecasting models. Future research is suggested to integrate these factors to enhance forecasting accuracy. The study's findings are pivotal for investors, policymakers, and market regulators, especially in the context of the Global South and sustainable development. The research offers valuable insights for risk management strategies, investment planning, and policy formulation aimed at promoting market stability and sustainable economic growth. The study emphasizes the importance of selecting appropriate forecasting models based on specific time horizons and market requirements.

Exploring the interconnection between international crude oil and silver rates volatilities: Novel evidence from the COVID-19 pandemic

Several research studies globally focus on the volatility of gold and oil prices, neglecting an examination of silver price volatility in relation to other market commodities. The current Covid-19 pandemic has led to various uncertainties and fluctuations in financial and stock exchange markets, yet existing literature primarily concentrates on individual product rates rather than combined rate changes. Our study aims to bridge this research gap by analyzing the relationship between oil rates, silver rates, oil rate transitions, and silver rate transitions on the security exchange in China from 1990 to 2022, using the ARDL approach and Nonlinear ARDL for a comprehensive assessment. The findings reveal a significant impact of silver and oil rates on China's security exchange in the future, with the negative effects of oil rate changes and a positive effect of silver rate transitions. In the short term, oil and silver rates play a crucial role in influencing China's security exchange, highlighting the importance of monitoring these trends for investors. It is recommended that investors respond prudently to market transitions, particularly by considering silver as a secure option during times of uncertainty, while policymakers should implement appropriate measures to manage the rapid fluctuations from oil to the security market.

Skew–Brownian processes for estimating the volatility of crude oil Brent

To predict the volatility of crude oil Brent price, we propose a novel econometric model 11The numerical results presented in this manuscript were reproduced by the Editor-in-Chief on 30 June 2024. where the explanatory variables are a combination of macroeconomic variables (i.e. price pressure), trade data (freight shipment index), and market sentiment (gold volatility). The model is proposed in two alternative variants: first, we assume Gaussian distributed quantities; alternatively, we consider the potential presence of skewness and adopt a Skew–Brownian process. We show that the suggested approach outperforms the selected baseline model as well as other models proposed in the literature, especially when turbulent periods occur.

Global economic contraction, climate change and the gold market volatility: A GARCH‐MIDAS approach

AbstractOur paper has two main objectives. First, we aim to investigate the relationship between global economic contraction (GECON) and the return volatility of gold. Second, we examine the role of climate change as a mediator in this connection. To achieve this, we use the GARCH‐MIDAS model, which accommodates data in different frequencies in the same model. This prevents the loss of important information when aggregating high‐frequency data to lower‐frequency data. We also use alternative measures of GECON from Kilian and Zhou (Journal of International Money and Finance, 2018; 88, 54–78) and Baumeister et al. (Review of Economics and Statistics, 2020;104(4), 828–844) to ensure the robustness of our findings. Our results show that global economic contraction positively impacts the return volatility of gold. Additionally, our findings confirm that the increased uncertainty caused by climate change makes gold a safe haven for investors. This means that gold's return volatility is not negatively impacted by the rising level of uncertainty caused by climate‐induced contraction. Moreover, we note that the index of GECON that accommodates more dynamics can produce more accurate predictability for gold market volatility. Our analysis of stock market volatility further confirms that gold has a safe haven potential relative to the stock market.

Flight-to-safety across time and market conditions

This paper examines the time-varying directional predictability from the implied volatility of risky assets, such as US stocks, to the implied volatility of less risky or safe haven assets, such as gold, cryptocurrency, foreign exchange rates, and US Treasury notes, which allows for making time and regime dependent inferences on the flight-to-safety phenomenon. The results show a positive directional predictability from the VIX to the implied volatility of gold, cryptocurrency, and foreign exchange rates in periods of high market uncertainty such as the COVID-19 outbreak and the war in Ukraine. The volatility of US Treasury notes has a different response dynamic to the VIX, switching to negative cross-correlations in some cases, given its tight link to inflationary pressure and economic policy uncertainty. The main results remain qualitatively unchanged after incorporating economic policy uncertainty and oil volatility. An additional analysis indicates that flight-to-safety is not exclusively triggered by the high volatility regime in US equities but happens at median levels too. Our findings have important implications for investors, risk managers, and policymakers regarding volatility predictability, hedging, and market stability.

Volatility Transmission and Market Connectivity of Metals and Energy Commodities: Insights from the Spillover Index

This paper explores the interconnections between metal and energy commodities by assessing the transmission of volatility within their futures markets. Achieved through the spillover index, the analysis reveals the impact of shocks on asset pairs—highlighting which assets absorb and transmit volatility, thereby explaining market connectivity. The dataset covers a period of twenty years of daily closing prices from the London Metals Exchange for a range of commodities. The Diebold and Yilmaz (2012) index, enriched by Baruník and Krehlík (2018) was used to reflect the changes in trends. The results obtained provide insights into market connectivity and the propagation of volatility during periods of economic distress. In this context, following the 2008 crisis, precious metals exhibited significant interconnectivity, with the emergence of Silver's vulnerability along with Gold's volatility tied to pre-crisis returns. Furthermore, copper's volatility reflects conditional correlations with market stock prices and other commodities particularly during economic downturns.

On the effect of short-run and long-run US economic expectations on oil and gold volatilities

This paper investigates the effect of US Economic expectations on oil and gold volatilities, examining their formation across different time horizons. To this end, we compute expectations using a MS-VAR model from one (short run) to sixteen quarters ahead (long run). Then, we estimate the impact of an expectation shock on oil and gold volatility measures through an impulse response function estimating a VAR model from 1987Q1 to 2022Q1. Our results show that oil volatility is significantly affected by expectations in the short run, while gold volatility is affected by expectations in the long run. In particular, gold and oil volatilities exhibit a decrease following a positive expectation shock.

Dynamic linkages among bitcoin, equity, gold and oil: An implied volatility perspective

Bitcoin Implied Volatility, derived from the recently launched options on Bitcoin, provides a new forward-looking measure of uncertainty in financial markets. This paper explores its long-run and short-run relationships with the implied volatilities of equity, gold, and oil. The NARDL model reveals a long-run relationship among Bitcoin volatility and others. Kyrtsou-Labys Nonlinear Causality tests established pair-wise causality between the implied volatilities under study. Our findings highlight the integration of Bitcoin markets with global financial markets, indicating the coming of age of the Bitcoin market.

The nexus between the volatility of Bitcoin, gold, and American stock markets during the COVID-19 pandemic: evidence from VAR-DCC-EGARCH and ANN models

The spread of the coronavirus has reduced the value of stock indexes, depressed energy and metals commodities prices including oil, and caused instability in financial markets around the world. Due to this situation, investors should consider investing in more secure assets, such as real estate property, cash, gold, and crypto assets. In recent years, among secure assets, cryptoassets are gaining more attention than traditional investments. This study compares the Bitcoin market, the gold market, and American stock indexes (S&P500, Nasdaq, and Dow Jones) before and during the COVID-19 pandemic. For this purpose, the dynamic conditional correlation exponential generalized autoregressive conditional heteroskedasticity model was used to estimate the DCC coefficient and compare this model with the artificial neural network approach to predict volatility of these markets. Our empirical findings showed a substantial dynamic conditional correlation between Bitcoin, gold, and stock markets. In particular, we observed that Bitcoin offered better diversification opportunities to reduce risks in key stock markets during the COVID-19 period. This paper provides practical impacts on risk management and portfolio diversification.

CONTAGION EFFECT IN THE BIST STOCK MARKET

The contagion effect refers to the transfer of price fluctuations from one market to others. In periods of turmoil, the relationship between various asset markets can result in substantial risks of contagion that go further the typical risks connected with changes in volatility and correlation. This can occur through various channels, such as the spread of shocks caused by transformations in the statistical relationships on asset returns, including changes in the relationship between average yields and volatility across different markets. This paper analyses the contagion of the BIST stock market on the volatility of gold, foreign exchange, and crude oil markets during the Covid-19 period. The correlation, coskewness, cocurtosis, covolatility test applied to measure the volatility contagion. The result shows contagions among the mentioned markets through higher moments. Our results are essential for asset management and developing preventive measures for financial stability.

The response of gold to the COVID-19 pandemic

PurposeThis study aims to investigate how the gold return and its volatility respond to the COVID-19 pandemic.Design/methodology/approachStochastic volatility (SV) models are conducted to examine the response of gold to the number of new confirmed cases and deaths.FindingsThe results indicate that an increase in the change rate of the number of COVID-19 infections or fatalities leads to heightened volatility in gold prices. Moreover, the results suggest that gold volatility is more sensitive to the impacts from high-income countries than by those from middle- and low-income countries. In addition, the asymmetric effect is detected in the gold price volatility, which is contrary to the typical asymmetric effect seen in the stock market. Furthermore, the results remain robust after accounting for the US dollar and the volatility index in relation to gold returns.Practical implicationsThis study presents whether and to what extent gold is incorporated in the information related to the number of COVID-19 cases and deaths.Originality/valueThis study augments the existing literature by exploring how the number of COVID-19 infections and fatalities influences gold prices. In addition, it examines the day-of-the-week and asymmetric effects that may contribute to the volatility of gold prices. To the best of the authors’ knowledge, the evolution of gold has not yet been investigated using SV models.

Gold and crude oil: A time-varying causality across various market conditions

This paper examines the relationship between the returns and volatility of gold and crude oil markets using a novel procedure capturing both the quantile and time-varying Granger causality in mean and in variance, which is necessary to understand the complexity and multi-layered causal flow between these two strategic commodities. Using daily data from March 2nd, 2005, to February 7th, 2022, covering the global financial crisis and COVID-19 pandemic, the results are summarized as follows. Firstly, there is a bidirectional causal relationship in mean between gold and oil returns, regardless of the market conditions. Secondly, causality in variance varies across quantiles and mainly flows from gold to Brent during moderate to high volatility states of the volatility conditional distribution. Thirdly, the rolling-window causality-in-quantiles approach provides solid evidence that the return and volatility predictability between gold and oil markets is both time and quantile dependent, with evidence that the causal flows from Brent to gold are relatively stronger and span longer episodes than those running from gold to crude oil. The overall findings imply the unsuitability of applying a static quantile causality approach between these two strategic commodities, particularly over a long sample period, since the interactions between gold and crude oil markets tend to fracture suddenly under crisis periods. Accordingly, our main findings have important implications for portfolio diversification and hedging decisions.

Predicting gold volatility: Exploring the impact of extreme risk in the international commodity market

This paper mainly examines the performance of the tail risk (TR) constructed by 19 sub-categories of commodity price indices for predicting gold futures market volatility. Under the framework of the AR model, we compared the predictive capabilities of AR-type models incorporating the TR indicator as an additional explanatory variable against the AR benchmark model without the TR indicator. Our findings reveal that the incorporation of the TR indicator significantly improves the predictive accuracy of the gold futures volatility model. Notably, this enhancement is particularly pronounced during periods of heightened volatility.

Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data

We use monthly data covering a century-long sample period (1915–2021) to study whether geopolitical risk helps to forecast subsequent gold volatility. We account not only for geopolitical threats and acts, but also for 39 country-specific sources of geopolitical risk. The response of subsequent volatility is heterogeneous across countries and nonlinear. We find that accounting for geopolitical risk at the country level improves forecast accuracy, especially when we use random forests to estimate our forecasting models. As an extension, we report empirical evidence on the predictive value of the country-level sources of geopolitical risk for two other candidate safe-haven assets, oil and silver, over the sample periods 1900–2021 and 1915–2021, respectively. Our results have important implications for the portfolio and risk-management decisions of investors who seek a safe haven in times of heightened geopolitical tensions.

Determinants of United States - Indonesia Equity Market’s Dynamic Correlation: The Role of Commodities and Exchange Rate’s Volatilities

<p>This study aims to analyze the effect of oil price volatility, gold price volatility and exchange rate volatility on the dynamic relationship between Indonesian and United States capital market. The data used in this study are daily closing prices of oil, gold and exchange rates (USD/IDR) as well as Indonesian capital market (JKSE) and United States capital market (DJIA) composite indices during period of January 2005 to October 2020. This study uses DCC-GARCH method to calculate the dynamic correlation between two capital markets and GARCH with the GED parameter to analyze oil volatility, gold volatility and exchange rate volatility on the integration of Indonesian capital market and United States capital market. The results of this study show positive and strong results on the integration of Indonesian and United States capital markets, thus proving that the movements of Indonesian market and American market tend to be strong and mutually influence the two capital markets. Moreover, the oil, gold and exchange rates volatilities have a negative effect on the integration of the Indonesia capital market and the US capital market. This finding implies investors should take oil, gold and exchange rates volatilities in their investment consideration.</p>

주택은 인플레이션을 헷지 할 수 있는가?

This paper examines the inflation hedging capacity of Korea’s housing return from January 1987 to December 2022. The exponential generalized autoregressive conditiuonal heteroskedasticity(EGARCH) model and a dynamic ordinary least squares (DOLS) regression were employed to study the hedging capacity of Korea’s housing return against expected and unexpected inflation over the short run and long run. The results that could be obtained through this paper is as follows. First, The empirical results show a hedge effect on expected inflation only in housing return excluding gold over the short term, but not in unexpected inflation. Second, Over the long run, housing and gold returns provide a positive inflation hedge against expected inflation only. Third, it is found that the price volatility of housing and gold have both a positive asymmetric effect on news shocks. Over the long run, an increase in wages is found to increase housing prices by promoting housing demand, While no similar evidence is found in money supply.Therefore, there is a need to implement a stable wage policy to stabilize the housing market.

Twitter matters for metaverse stocks amid economic uncertainty

The study examines the relationship between economic uncertainty, as measured by the Twitter-based economic uncertainty index (TEU), and Metaverse stocks across different time-frequency domains. By employing Wavelet Local Multiple Correlation analysis, we identify a substantial and statistically significant correlation between TEU and the Metaverse stock market across all time horizons examined. The positive correlation is confirmed when two alternative measures of economic uncertainty: crude oil and gold volatility, are included. The results highlight the importance of considering real-world economic uncertainty in decision-making processes involving virtual reality environments.

The Impact of Gold, Oil Prices, and their Associated Implied Volatilities on Performance of Pakistan’s Stock Market

This study examined the asymmetrical implications of gold and oil prices, as well as the implied volatility that goes along with them, on the performance of Pakistan's stock market. It was used Chicago Board of Options Exchange’s GVZ and OVX index for measuring volatility in gold market and oil market respectively due to more uncertainty in commodity markets and also took historical price movements, investor’s expectations regarding global prices into account. The monthly data used from October 2009 to January 2023. An ARDL approach was employed for checking short term and long term asymmetrical. The results of ARDL revealed that oil volatility index have significant impact of Pakistan’s stock market in short-run and long-run. The Gold Volatility Index shows significant impact after one month on both stock market capitalization and KSE 100 indexes in the short run. Fluctuations in worldwide commodity prices have a significant influence on underdeveloped economies like Pakistan and have a negative effect on the entire economy. The volatility and fluctuations in oil prices lead to higher production costs, which create uncertainty in companies' cash flows.

Technology shocks - Gold market connection: Is the effect episodic to business cycle behaviour?

We explore the connection between technology shocks (TS) and gold return volatility using the various variants of the newly developed TS data and covering several decades from 1950. The consideration for a long range of data affords us to test whether or not the nexus is sensitive to business cycle dynamics. We employ the GARCH-MIDAS framework to establish the connection owing to the mixed data frequencies of the variables of interest. Overall, we find that technological innovations drive higher volatilities in the gold market from a long term perspective. Examining the role of business cycles in the nexus, we partition the analyses into expansion and recession periods. Consequently, we show that the role of business cycles is indeed crucial as the TS increase the level of gold volatility during the period of expansion relative to the period of recession. This justifies that improvements in the economy may further stimulate technological innovations, and by extension, enhance the trading activity in the gold market. Finally, we also demonstrate the forecast gains of including the TS in the predictive model of gold volatility rather than subsuming it in the (aggregate) market risk. Our results are robust to multiple out-of-sample forecast horizons and alternative measures of gold returns, and technology shocks.