Abstract:Precious metals (gold, silver, and platinum) have become an important part of investment portfolios for individuals as well as for institutions. This paper examines the weak-form efficiency of precious metals markets, using the automatic portmanteau and variance ratio tests. It is found that return predictability of these markets has been changing over time, depending on the prevailing economic and political conditions. The return predictability of gold and silver markets have been showing downward trends, imp… Show more
“…Specifically, we observe that the upward Hurst exponent is higher than the downward Hurst exponent for negative scales, while the inverse case is observed for positive scales. These results are consistent with those of Charles et al, 2015a , Charles et al, 2015b who find dynamic return predictability of precious metals (gold, silver, and platinum). They also show that the return predictability of gold and silver shows a downside trend, indicating improve in efficiency level.…”
Section: Empirical Analysis Resultssupporting
confidence: 91%
“… Tabak and Cajueiro (2007) and Charles and Darne (2009) find evidence of time-varying efficiency. Charles et al (2015a , b) find that the return predictability of precious metals is time varying and that the efficiency degree of gold and silver enhance over time. Baruník et al (2012) shows that technical indicators, diffusion indices, and economically motivated restrictions in predictive regressions do not provide accurate predictability of gold excess returns.…”
Section: Introductionmentioning
confidence: 99%
“…Lucey (2011) , Baur et al (2016) , Peirdzioch et al ( 2014 ) have also explored the predictability of gold prices. Charles et al, 2015a , Charles et al, 2015b examine the efficiency of main precious metals (gold, silver, and platinum) have become an important part of investment portfolios for individuals as well as for institutions. This paper examines the weak-form efficiency of precious metal markets, using the automatic portmanteau and variance ratio tests.…”
This paper examines the impacts of COVID-19 on the multifractality of gold and oil prices based on upward and downward trends. We apply the Asymmetric Multifractal Detrended Fluctuation Analysis (A-MF-DFA) approach to 15-min interval intraday data. The results show strong evidence of asymmetric multifractality that increases as the fractality scale increases. Moreover, multifractality is especially higher in the downside (upside) trend for Brent oil (gold), and this excess asymmetry has been more accentuated during the COVID-19 outbreak.
Before the outbreak, the gold (oil) market was more inefficient during downward (upward) trends. During the COVID-19 outbreak period, we see that the results have changed. More precisely, we find that gold (oil) is more inefficient during upward (downward) trends.
Gold and oil markets have been inefficient, particularly during the outbreak. The efficiency of gold and oil markets is sensitive to scales, market trends, and to the pandemic outbreak, highlighting the investor sentiment effect.
“…Specifically, we observe that the upward Hurst exponent is higher than the downward Hurst exponent for negative scales, while the inverse case is observed for positive scales. These results are consistent with those of Charles et al, 2015a , Charles et al, 2015b who find dynamic return predictability of precious metals (gold, silver, and platinum). They also show that the return predictability of gold and silver shows a downside trend, indicating improve in efficiency level.…”
Section: Empirical Analysis Resultssupporting
confidence: 91%
“… Tabak and Cajueiro (2007) and Charles and Darne (2009) find evidence of time-varying efficiency. Charles et al (2015a , b) find that the return predictability of precious metals is time varying and that the efficiency degree of gold and silver enhance over time. Baruník et al (2012) shows that technical indicators, diffusion indices, and economically motivated restrictions in predictive regressions do not provide accurate predictability of gold excess returns.…”
Section: Introductionmentioning
confidence: 99%
“…Lucey (2011) , Baur et al (2016) , Peirdzioch et al ( 2014 ) have also explored the predictability of gold prices. Charles et al, 2015a , Charles et al, 2015b examine the efficiency of main precious metals (gold, silver, and platinum) have become an important part of investment portfolios for individuals as well as for institutions. This paper examines the weak-form efficiency of precious metal markets, using the automatic portmanteau and variance ratio tests.…”
This paper examines the impacts of COVID-19 on the multifractality of gold and oil prices based on upward and downward trends. We apply the Asymmetric Multifractal Detrended Fluctuation Analysis (A-MF-DFA) approach to 15-min interval intraday data. The results show strong evidence of asymmetric multifractality that increases as the fractality scale increases. Moreover, multifractality is especially higher in the downside (upside) trend for Brent oil (gold), and this excess asymmetry has been more accentuated during the COVID-19 outbreak.
Before the outbreak, the gold (oil) market was more inefficient during downward (upward) trends. During the COVID-19 outbreak period, we see that the results have changed. More precisely, we find that gold (oil) is more inefficient during upward (downward) trends.
Gold and oil markets have been inefficient, particularly during the outbreak. The efficiency of gold and oil markets is sensitive to scales, market trends, and to the pandemic outbreak, highlighting the investor sentiment effect.
“…Results from daily, weekly and biweekly data between the mid 1970's and the mid 1980's for silver point to the possibility of an underlying martingale process, indicating that a nonlinear process generates observed silver returns and price of silver is an unpredictable stochastic variable. Charles et al (2015) brings this research up to date looking at daily spot prices for silver and platinum between 1977 and 2013. They test for weak-form efficiency using the automatic Portmanteau test Lobato et al (2001) for the presence of conditional heteroscedasticity.…”
This article provides a review of the academic literature on the financial economics of silver, platinum and palladium. The survey covers the findings on a wide variety of topics relation to the White Precious Metals including Market Efficiency, Forecastability, Behavioral Findings, Diversification Benefits, Volatility Drivers, Macroeconomic Determinants, and their relationships with other assets.
“…The AMH has substantially gained attention in the recent literature, with a number of papers supporting the hypothesis in developed stock markets (e.g., Ito & Sugiyama, ; Kim, Shamsuddin, & Lim, ; Urquhart, ; Urquhart & Hudson, & Urquhart & McGroarty, ), developing stock markets (Dyakova & Smith & ; Hull & McGroarty, ; Smith, , ), foreign exchange markets (Charles, Darné, & Kim, ; Levich & Poti, ), and even precious metal markets (Charles, Darné, & Kim, ; Urquhart, ).…”
The recent rapid growth of algorithmic high‐frequency trading strategies makes it a very interesting time to revisit the long‐standing debates about the efficiency of stock prices and the best way to model the actions of market participants. To evaluate the evolution of stock price predictability at the millisecond timeframe and to examine whether it is consistent with the newly formed adaptive market hypothesis, we develop three artificial stock markets using a strongly typed genetic programming (STGP) trading algorithm. We simulate real‐life trading by applying STGP to millisecond data of the three highest capitalized stocks: Apple, Exxon Mobil, and Google and observe that profit opportunities at the millisecond time frame are better modelled through an evolutionary process involving natural selection, adaptation, learning, and dynamic evolution than by using conventional analytical techniques. We use combinations of forecasting techniques as benchmarks to demonstrate that different heuristics enable artificial traders to be ecologically rational, making adaptive decisions that combine forecasting accuracy with speed.
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