Crude oil, gold, natural gas, exchange rate and indian stock market: Evidence from the asymmetric nonlinear ARDL model

Kumar, Suresh; Choudhary, Sangita; Singh, Gurcharan; Singhal, Shelly

doi:10.1016/j.resourpol.2021.102194

Cited by 38 publications

(26 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, according to Table 7 , what affects the elasticity of the stock market most is the stock market itself. This is consistent with Kumar et al (2021) and Jain and Biswal (2016) .…”

Section: Resultssupporting

confidence: 89%

“…Table 3 indicates that the contribution factor for overall connectedness (TCI) among variables is 1.46%. These results are inconsistent with those of Kumar et al (2021) , who argued that only oil and not gold affects the stock market, and Jain and Biswal (2016) , who concluded that gold and oil returns have a symmetric impact on the stock market. According to our findings, however, the effect of gold and oil on stock market elasticity is asymmetric.…”

Section: Resultscontrasting

confidence: 85%

See 1 more Smart Citation

Oil and gold return spillover and stock market elasticity during COVID-19 pandemic: A comparative study between the stock markets of oil-exporting countries and oil-importing countries in the Middle East

Bani-Khalaf

Taşpınar

2022

Resources Policy

View full text Add to dashboard Cite

“…However, according to Table 7 , what affects the elasticity of the stock market most is the stock market itself. This is consistent with Kumar et al (2021) and Jain and Biswal (2016) .…”

Section: Resultssupporting

confidence: 89%

Section: Resultscontrasting

confidence: 85%

Oil and gold return spillover and stock market elasticity during COVID-19 pandemic: A comparative study between the stock markets of oil-exporting countries and oil-importing countries in the Middle East

Bani-Khalaf

Taşpınar

2022

Resources Policy

View full text Add to dashboard Cite

“…Natural gas prices offered diversification benefits. Scholars also studied the interplay of natural gas prices with other commodity and financial assets (Ameur et al, 2020 ; Kumar et al, 2021 ; Liao et al, 2021 ). On a similar note, the natural gas consumption prediction has garnered attention, as reported in the literature (Liu et al, 2021 ; Lu et al, 2020 ; Qiao et al, 2020 ; Wei et al, 2021 ; Zhou et al, 2020 ).…”

Section: Literature Reviewmentioning

confidence: 99%

A residual driven ensemble machine learning approach for forecasting natural gas prices: analyses for pre-and during-COVID-19 phases

Jana

Ghosh

2022

Ann Oper Res

View full text Add to dashboard Cite

The natural gas price is an essential financial variable that needs periodic modeling and predictive analysis for many practical implications. Macroeconomic euphoria and external uncertainty make its evolutionary patterns highly complex. We propose a two-stage granular framework to perform predictive analysis of the natural gas futures for the USA (NGF-USA) and the UK natural gas futures for the EU (NGF-UK) for pre-and during COVID-19 phases. The residuals of the previous stage are introduced as a new explanatory feature along with standard technical indicators to perform predictive tasks. The importance of the new feature is explained through the Boruta feature evaluation methodology. Maximal Overlap Discrete Wavelet Transformation (MODWT) is applied to decompose the original time-series observations of the natural gas prices to enable granular level forecasting. Random Forest is invoked on each component to fetch the respective predictions. The aggregated component-wise sums lead to final predictions. A rigorous performance assessment signifies the efficacy of the proposed framework. The results show the effectiveness of the residual as a feature in deriving accurate forecasts. The framework is highly efficient in analyzing patterns in the presence of a limited number of data points during the uncertain COVID-19 phase covering the first and second waves of the pandemic. Our findings reveal that the prediction accuracy is the best for the NGF-UK in the pre-COVID-19 period. Also, the prediction accuracy of the NGF-USA is better in the COVID-19 period than the pre-COVID-19 period.

show abstract

“…Also, oil prices impacted all emerging stock markets negatively. Meanwhile, the research by Kumar et al (2021) studied an asymmetric causality among crude oil prices, gold prices, natural gas prices, exchange rate, and the stock market index in India by using the NARDL bound test. In the long run, the findings showed that gold prices positively and asymmetrically impacted crude oil prices.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Relationship between the Return of Gold, Crude Oil, and the Stock Exchange of Thailand Based on a Vector Autoregressive Model

Uthumrat

2022

IJEEP

View full text Add to dashboard Cite

This research aims to investigate the dynamic relationship between the return of gold (RGOLD), crude oil (RCO), and the stock exchange of Thailand (RSET) using the Vector Autoregressive model: VAR(p) to analyze the secondary monthly data from January 2002 to October 2022. The paper reports three key findings. Firstly, the Granger causality test reveals two direct relationships: one between RCO and RSET, and the other between RSET, RCO, and RGOLD. Moreover, the researcher estimated the relationship between these variables by using the VAR(1) model. Secondly, the impulse response function (IRF) is applied by Granger causality results. When a variable is shocked by an impulse in the system, the RCO to RSET response increases by 0.21 percent, the RSET to RGOLD response increases by 0.10 percent, and the RCO to RGOLD response increases by 0.04 percent, before decreasing and approaching equilibrium. Finally, variance decomposition shows that the greatest portion of total variation in RGOLD, RCO, and RSET can be explained by itself in the long run. Moreover, the variation of RSET can be affected by the variation of RCO while the variation of RGOLD can be affected by the variation of RCO and RSET. Thus, investors, securities analysts, or fund managers can use the study’s results for strategic investment planning.

show abstract

Crude oil, gold, natural gas, exchange rate and indian stock market: Evidence from the asymmetric nonlinear ARDL model

Cited by 38 publications

References 60 publications

Oil and gold return spillover and stock market elasticity during COVID-19 pandemic: A comparative study between the stock markets of oil-exporting countries and oil-importing countries in the Middle East

Oil and gold return spillover and stock market elasticity during COVID-19 pandemic: A comparative study between the stock markets of oil-exporting countries and oil-importing countries in the Middle East

A residual driven ensemble machine learning approach for forecasting natural gas prices: analyses for pre-and during-COVID-19 phases

Dynamic Relationship between the Return of Gold, Crude Oil, and the Stock Exchange of Thailand Based on a Vector Autoregressive Model

Contact Info

Product

Resources

About