A boosting approach to forecasting the volatility of gold-price fluctuations under flexible loss

International Journal of Forecasting

McAleer

2020

105

For purposes of forecasting the covariance matrix for the returns of crude oil and gold futures, this paper examines the effects of leverage, jumps, spillovers, and geopolitical risks, using their respective realized covariance matrices. In order to guarantee the positive definiteness of the forecasts, we consider the full BEKK structure on the conditional Wishart model. By the specification, we can divide flexibly the direct and spillover effects of volatility feedback, negative returns, and jumps. The empirical analysis indicates the benefits in accommodating the spillover effects of negative returns and the geopolitical risks indicator for modelling and forecasting the future covariance matrix.

Section: Introductionmentioning

confidence: 99%

Forecasting volatility and co-volatility of crude oil and gold futures: Effects of leverage, jumps, spillovers, and geopolitical risks

Asai

International Journal of Forecasting

McAleer

2020

105

“…Understandably, an accurate forecast of gold return volatility is of paramount interest to investors and portfolio managers in their asset pricing models (such as, gold derivatives pricing) as well as in hedging strategies to mitigate portfolio risks. Not surprisingly, there exists a large amount of literature on empirical finance that aims to forecast gold volatility based on various metrics that capture the uncertain environment of the financial markets and macroeconomy (see for example, Pierdzioch et al (2016), Fang et al, (2018), Asai et al (2019, forthcoming), Bouri and Jalkh (2019), Demirer et al (2019), , Bonato et al, (forthcoming b), and the references cited therein).…”

Section: Introductionmentioning

confidence: 99%

The role of global economic conditions in forecasting gold market volatility: Evidence from a GARCH-MIDAS approach

Salisu

Research in International Business and Finance

Bouri

et al. 2020

In this study, we examine the role of global economic conditions in forecasting gold market volatility using alternative measures. Based on the available data frequency for the relevant series, we adopt the GARCH-MIDAS approach which allows for mixed data frequencies. We find that global economic conditions contribute significantly to gold market volatility albeit with mixed outcomes. While the results lend support to the safe-haven properties of the gold market, the outcome is influenced by the choice of measure of global economic conditions. For completeness, we extend the analyses to other precious metals such as silver, platinum, palladium, and rhodium and find that global economic conditions forecast the volatility of gold returns better than other precious metals. Our results are robust to multiple forecast horizons and offer useful insights into plausible investment choices in the precious metals market.

“…A third strand of research focuses on the properties of the realized volatility of gold price fluctuations. For example, using a boosting approach, Pierdzioch et al (2016a) examine the timevarying predictive value of several financial and macroeconomic variables for out-of-sample forecasting the monthly realized gold-price volatility over the sample period from 1987 to 2015.…”

Section: Introductionmentioning

confidence: 99%

Time-varying risk aversion and realized gold volatility

Demirer

Γκίλλας

The North American Journal of Economics and Finance

et al. 2019

Self Cite

We study the in-and out-of-sample predictive value of time-varying risk aversion for realized volatility of gold-price 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. Risk aversion is found to absorb in sample the predictive power of stock-market volatility at a short forecasting horizon. We also study the out-of-sample predictive power of risk aversion in the presence of realized higher-moments, jumps, gold returns, a leverage effect as well as the aggregate stock-market volatility in the forecasting model. Results show that risk aversion adds to predictive value, where the shape of the loss function used to evaluate losses from forecast errors plays a prominent role for the beneficial effects using time-varying risk aversion to forecast realized volatility. Specifically, additional tests suggest that the short-run (long-run) out-of-sample predictive value of risk aversion is beneficial for investors who are more concerned about over-predicting (under-predicting) gold market volatility. Overall, our findings show that time-varying risk aversion captures information useful for out-of-sample predicting realized volatility not already contained in the other predictors.