A real-time quantile-regression approach to forecasting gold returns under asymmetric loss

Pierdzioch, Christian; Risse, Marian; Rohloff, Sebastian

doi:10.1016/j.resourpol.2015.07.002

Cited by 23 publications

(2 citation statements)

References 52 publications

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“…Using data for major gold-producing countries, we have shown that a novel nonparametric causality-in-quantiles test provides new insights into the in-sample causal links between gold-price fluctuations and exchange-rate movements in both their first and second moments. In future research, it is interesting to extend our analysis to a out-of-sample forecasting context, since in-sample predictability does not guarantee the same over the out-ofsample (Bonaccolto et al, 2015; on out-of-sample forecasting of gold-price fluctuations using variants of quantile-regression techniques, see also Pierdzioch et al 2015Pierdzioch et al , 2016.…”

Section: Discussionmentioning

confidence: 99%

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

Balcılar

Gupta

Pierdzioch

2016

Int Econ Econ Policy

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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.

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Section: Discussionmentioning

confidence: 99%

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

Balcılar

Gupta

Pierdzioch

2016

Int Econ Econ Policy

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“…Such a sample period allows us to cover the longest possible high-frequency (monthly) data available for gold, and the associated predictive impact of various historical global and country-specific geopolitical risk, and in the process makes our analysis immune to any sample-selection bias (Hollstein et al, 2021). Our paper can be considered to add to the relatively large literature associated with the forecasting gold-market developments based on a wide array of macroeconomic, financial, and behavioral predictors that rely on a large spectrum of linear and nonlinear univariate or multivariate models (see, for example, Pierdzioch et al, 2014aPierdzioch et al, , 2014bPierdzioch et al, , 2015aPierdzioch et al, , 2015bPierdzioch et al, , 2016aPierdzioch et al, , 2020a2020b;Aye et al, 2015;Hassani et al, 2015;Sharma, 2016;Bonato et al, 2018;Nguyen et al, 2019;Dichtl, 2020, with the last paper in particular providing a detailed review). Our paper goes beyond this earlier research in that we use the information content of country-level disaggregate geopolitical risk.…”

Section: Introductionmentioning

confidence: 96%

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

Gupta,

Karmakar,

Pierdzioch

2023

Comput Econ

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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.

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Stock market bubbles and the forecastability of gold returns and volatility

Gabauer,

Gupta,

Karmakar

et al. 2024

Appl Stoch Models Bus & Ind

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In this article, multi‐scale LPPLS confidence indicator approach is used to detect both positive and negative bubbles at short‐, medium‐, and long‐term horizons for the stock markets of the G7 and the BRICS countries. This enables detecting major crashes and rallies in the 12 stock markets over the period of the 1st week of January, 1973 to the 2nd week of September, 2020. Similar timing of strong (positive and negative) LPPLS indicator values across both G7 and BRICS countries was also observed, suggesting interconnectedness of the extreme movements in these stock markets. Next, these indicators were utilized to forecast gold returns and its volatility, using a method involving block means of residuals obtained from the popular LASSO routine, given that the number of covariates ranged between 42 and 72, and gold returns demonstrated a heavy upper tail. The finding was, these bubbles indicators, particularly when both positive and negative bubbles are considered simultaneously, can accurately forecast gold returns at short‐ to medium‐term, and also time‐varying estimates of gold returns volatility to a lesser extent. The results of this paper have important implications for the portfolio decisions of investors who seek a safe haven during boom‐bust cycles of major global stock markets.

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A real-time quantile-regression approach to forecasting gold returns under asymmetric loss

Cited by 23 publications

References 52 publications

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

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

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

Stock market bubbles and the forecastability of gold returns and volatility

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