Gold Investment Incentives: An Empirical Identification of the Main Gold Price Determinants and Prognostication of Gold Price Future Trends

Gasparėnienė, Ligita; Remeikienė, Rita; Sadeckas, Alius; Ginevičius, Romualdas

doi:10.14254/2071-789x.2018/11-3/15

Cited by 6 publications

(2 citation statements)

References 48 publications

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“…Gold and Bitcoin can be considered as safe havens during the global pandemic when investing in the NYSE energy stocks. This result is consistent with that of Gaspareniene et al ( 2018 ) and Robiyanto et al ( 2019 ). However, it is inconsistent with that of Conlon and McGee ( 2020 ) and Corbet et al ( 2020 ).…”

Section: Data and Resultssupporting

confidence: 94%

Volatility spillovers and contagion between energy sector and financial assets during COVID-19 crisis period

Ghorbel

Jeribi

2021

Eurasian Econ Rev

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In this paper, we examine the relationship between the volatilities of the energy index, crude oil, gas prices, and financial assets (Gold, Bitcoin, and G7 stock indexes), especially during the coronavirus crisis. The study tests the presence of regime changes in the GARCH volatility dynamics of the G7 stock indexes, Bitcoin, Gold, and energy assets (energy index, oil, and gas) by using the Markov-Switching GARCH model. It estimates the dynamic correlation and volatility spillover between energy and financial assets, by using the multivariate MSGARCH models. The estimation results of the Markov-Switching-BEKK-GARCH prove the volatility spillover from energy assets to financial assets. For the high regime, the results indicate a high level of dynamic correlation between energy assets and stock indexes which proves the contagion effect of the COVID-19. On the contrary, the dynamic conditional correlation between energy assets and Gold prices decreased during the COVID-19 crisis. This paper makes an original contribution in identifying the contagion between energy and financial assets and indicates that Gold is a safe haven for all energy and financial assets during the COVID-19 crisis. However, Bitcoin cannot be considered as a safe haven during the COVID-19 pandemic when investing in energy assets (crude oil and gas).

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Section: Data and Resultssupporting

confidence: 94%

Volatility spillovers and contagion between energy sector and financial assets during COVID-19 crisis period

Ghorbel

Jeribi

2021

Eurasian Econ Rev

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“…It is an assumption of the method that the data set applied in this method, which provides successful results in short and medium term modelling and prediction studies, is a discrete and stationary data set consisting of observation values with equal time intervals. The basic principle of the Box-Jenkins methodology is based on its value in any period of time series, a combination of past observations and error terms (Gaspareniene, 2018). Since Box-Jenkins models can only be used in stationary series, stationary condition is important in determining the appropriate model group (Anvari et al, 2016).…”

Section: Box-jenkins Methodsmentioning

confidence: 99%

Modelling and Forecasting Inbound Tourism Demand to Croatia using Artificial Neural Networks: A Comparative Study

Çuhadar

2020

JoTS

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Tourism demand is the basis on which all commercial decisions concerning tourism ultimately depend. Accurate estimation of tourism demand is essential for the tourism industry because it can help reduce risk and uncertainty as well as effectively provide basic information for better tourism planning. The purpose of this study is to develop the optimal forecasting model that yields the highest accuracy when compared to the forecast performances of three different methods, namely Artificial Neural Network (ANN), Exponential Smoothing, and Box-Jenkins methods for forecasting monthly inbound tourist flows to Croatia. Prior studies have been applied to forecast tourism demand to Croatia based on time series models and casual methods. However, the monthly and comparative tourism demand forecasting studies using ANNs are still limited, and this paper aims to fill this gap. The number of monthly foreign tourist arrivals to Croatia covers the period between January 2005-December 2019 data were used to build optimal forecasting models. Forecasting performances of the models were measured by Mean Absolute Percentage Error (MAPE) statistics. As a result of the experiments carried out, when compared to the forecasting performances of various models, 12 lagged ANN models, which have [4-3-1] architecture, were seen to perform best among all models applied in this study. Considering both the empirical findings obtained from this study and previous studies on tourism forecasting, it can be seen that ANN models that do not have any negativities (such as over-training, faulty architecture, etc.) produce successful forecasting results when compared with results generated by conventional statistical methods.

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How do black swan events go global? -Evidence from US reserves effects on TOCOM gold futures prices

Wang

Cao

Sui

et al. 2019

Finance Research Letters

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Gold Investment Incentives: An Empirical Identification of the Main Gold Price Determinants and Prognostication of Gold Price Future Trends

Cited by 6 publications

References 48 publications

Volatility spillovers and contagion between energy sector and financial assets during COVID-19 crisis period

Volatility spillovers and contagion between energy sector and financial assets during COVID-19 crisis period

Modelling and Forecasting Inbound Tourism Demand to Croatia using Artificial Neural Networks: A Comparative Study

How do black swan events go global? -Evidence from US reserves effects on TOCOM gold futures prices

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