Can news-based economic sentiment predict bubbles in precious metal markets?

Maghyereh, Aktham; Abdoh, Hussein

doi:10.1186/s40854-022-00341-w

Cited by 20 publications

(7 citation statements)

References 125 publications

(141 reference statements)

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“…constructed using machine-learning techniques. 7 The daily continuously compounded returns for each asset, r t , were computed as Li & Lucey, 2017;Maghyereh & Abdoh, 2022a, 2022bReboredo, 2013). Given the traditional motives of "flight-to-safety" from stocks to bonds, Baele et al (2020) showed that these motives correspond to heightened levels of volatility and a reduction in sentiment levels.…”

Section: Discussionmentioning

confidence: 99%

“…99Many studies have indicated that gold generally performs a “safe haven” role (Baur & Lucey, 2010; Ciner et al, 2013; Li & Lucey, 2017; Maghyereh & Abdoh, 2022a, 2022b; Reboredo, 2013). Given the traditional motives of “flight‐to‐safety” from stocks to bonds, Baele et al (2020) showed that these motives correspond to heightened levels of volatility and a reduction in sentiment levels.…”

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confidence: 99%

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Global financial crisis versus COVID‐19: Evidence from sentiment analysis

Maghyereh

2022

International Finance

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This study examines the relationship between sentiment and the realized volatility of returns for different asset classes (stocks, bonds, foreign currency, and commodities). Specifically, we aim to answer two key questions: first, how does sentiment relate to volatility during crises (mainly during the global financial crisis [GFC] and the COVID‐19 pandemic)? Second, can sentiment be used to forecast volatility during crises? Using two nonparametric methods, mutual information and transfer entropy, we find that information sharing and transfer increased during the pandemic. We also find that sentiment information transfer to the volatility of assets differed between the GFC and the COVID‐19 crisis. Since sentiment can reduce uncertainty around the realized variance of assets, we investigate the forecasting ability of sentiment during crises. We find that sentiment has a greater predictive power on realized volatility during crises, with a differential impact on volatility depending on the asset class. Our findings carry important implications for hedging, risk management and building models to predict variance during crises.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Global financial crisis versus COVID‐19: Evidence from sentiment analysis

Maghyereh

2022

International Finance

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“…Due to the high prices of Pt and Ir, many other potential catalysts that are more favorable, comparably active, and stable for both the ORR and the OER are still being searched. Ag is postulated as a possible alternative, since it is less expensive and shows a comparable ORR electrocatalytic activity to Pt in alkaline conditions. , Ag has been used in Siemens fuel cells in combination with nickel and titanium, and voltages around 800 and 900 mV at current densities of 400 and 200 mA cm –2 have been achieved, respectively . With gas diffusion electrodes (GDEs) made of Raney-silver and PTFE (polytetrafluoroethylene), voltages above 1 V and current densities around 30 mA cm –2 have been reached …”

Section: Introductionmentioning

confidence: 99%

Stability of Carbon Supported Silver Electrocatalysts for Alkaline Oxygen Reduction and Evolution Reactions

Linge,

Briega-Martos,

Hutzler

et al. 2023

ACS Appl. Energy Mater.

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Ag-based electrocatalysts are promising candidates to catalyze the sluggish oxygen reduction reaction (ORR) in anion exchange membrane fuel cells (AEMFC) and oxygen evolution reaction (OER) in unitized regenerative fuel cells. However, to be competitive with existing technologies, the AEMFC with Ag electrocatalyst must demonstrate superior performance and long-term durability. The latter implies that the catalyst must be stable, withstanding harsh oxidizing conditions. Moreover, since Ag is typically supported by carbon, the strict stability requirements extend to the whole Ag/C catalyst. In this work, Ag supported on Vulcan carbon (Ag/VC) and mesoporous carbon (Ag/MC) materials is synthesized, and their electrochemical stability is studied using a family of complementary techniques. We first employ an online scanning flow cell combined with inductively coupled plasma mass spectrometry (SFC-ICP-MS) to estimate the kinetic dissolution stability window of Ag. Strong correlations between voltammetric features and the dissolution processes are discovered. Very high silver dissolution during the OER renders this material impractical for regenerative fuel cell applications. To address Ag stability during AEMFC load cycles, accelerated stress tests (ASTs) in O2-saturated solutions are carried out in rotating disk electrode (RDE) and rotating ring-disk electrode (RRDE) setups. Besides tracking the ORR performance evolution, an ex situ long-term Ag dissolution study is performed. Moreover, morphological changes in the catalyst/support are tracked by identical-location transmission electron microscopy (RDE-IL-TEM). Voltammetry analysis before and after AST reveals a smaller change in ORR activity for Ag/MC, confirming its higher stability. RRDE results reveal a higher increase in the H2O2 yield for Ag/VC after the ASTs. The RDE-IL-TEM measurements demonstrate different degradation processes that can explain the changes in the long term performance. The results in this work point out that the stability of carbon-supported Ag catalysts depends strongly on the morphology of the Ag nanoparticles, which, in turn, can be tuned depending on the chosen carbon support and synthesis method.

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“…Corbet and Connor (2021) found that pure silver must be mixed with other metal types because of its soft nature when forming another product. Another type of precious metal with a reasonably expensive price is Palladium (Maghyereh and Abdoh, 2022). It is a shiny white metal with the lowest melting strength This Journal is licensed under a Creative Commons Attribution 4.0 International License compared to others and is very dense, not easily damaged by chemical compounds and physical impact.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Precious Metal Price Movements Using Long Memory Model and Fuzzy Time Series Markov Chain

Arif

Devianto

Yollanda

et al. 2022

IJEEP

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Precious metals occur naturally and have a high resistance to corrosion or oxidation. These natural resources are used as investment instruments to protect wealth values, such as gold, silver, and palladium. Price movements need to be understood when investing, and it is achieved through a time series model that predicts future prices. Also, autoregressive fractional integrated moving average (ARFIMA) is used to model price movements with long memory effects, while fuzzy time series Markov chain (FTSMC) is employed for performing numerical approach. It was observed that gold price movement has a long memory effect; therefore, it is eligible to be formed into the ARFIMA model. However, the silver and palladium prices do not contain a long memory effect, which means their movements are only formed through the FTSMC numerical model. The ARFIMA modeling results show that the gold price long memory model has the best accuracy with the smallest error value and also demonstrates excellent goodness of fit. Furthermore, the gold price long memory model movement has long-term stability compared to other precious metals. This provides an investment advantage because it is a stable asset, easy to liquidate in cash, free of interest, has an emergency fund role, and protects wealth’s value.

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Can news-based economic sentiment predict bubbles in precious metal markets?

Cited by 20 publications

References 125 publications

Global financial crisis versus COVID‐19: Evidence from sentiment analysis

Global financial crisis versus COVID‐19: Evidence from sentiment analysis

Stability of Carbon Supported Silver Electrocatalysts for Alkaline Oxygen Reduction and Evolution Reactions

Analysis of Precious Metal Price Movements Using Long Memory Model and Fuzzy Time Series Markov Chain

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