Gold Prices Movements and Predictions with Reference to Indian Context

Authors

  • Prof. Naveen Pol Assistant Professor, Acharya Bangalore Business School Author
  • Arindam Poddar MBA Student, Acharya Bangalore Business School Author
  • Dr. Feba Thomas Research Mentor, Accendere Knowledge Management Services Author

DOI:

https://doi.org/10.61841/evfarv90

Keywords:

Forecasting, Gold Price, Trade Deficit, ARIMA Model

Abstract

The purpose of the research is to forecast the gold price (IND), with the assistance of traditional method of Time Series, for example Box –ARIMA model, which gives us better outcomes for decision making, and furthermore helps the investors in forecasting the effectiveness of savings in the Gold Market. During this investigation the data of gold price from 2014 to 2019 have taken for examining the influence for predicting of gold price based on Time Series ARIMA (p,d,q) model. Forecasting is a capacity in the executives to help decision making. It is additionally described as the procedure of estimate in unknown future situations. In an increasingly broad term it is normally known as prediction which refers to estimation of time series or longitudinal type data. Gold is a valuable yellow product which can be convert into cash at any time. Time series ARIMA is the best technique for forecasting, modeling and characterization, all of which have been utilized in this investigation. For optimum accuracy Auto Correlation and Partial Correlation analysis have been utilized as a statistical support, and simultaneously. The rate of gold is keep on rising every day. So, by predicting the price of gold an individual can buy and invest his money on gold according to his suitability. The ARIMA model helps in finding the cost of gold in coming year. Research implication: people who are trading in different market share and people who are in the field of buying and selling of gold are also going to be benefited by this research. The findings of through ANIMA model the movement of gold price for coming next 5 years is predicted.

 

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References

1. Aye, G., R. Gupta, S. Hammoudeh, and W.J. Kim, Forecasting the Price of Gold Using Dynamic Model Averaging. International Review of Financial Analysis, 2015.

https://doi.org/10.1109/ICSECS.2015.7333120

2. Christie–David, R., Chaudhry, M., & Koch, T. W. (2000). Do macroeconomics news releases affect gold and silver prices?. Journal of Economics and Business, 52(5), 405-421.

3. Dooley, M. P., Isard, P., & Taylor, M. P. (1995). Exchange rates, country-specific shocks, and gold. Applied financial economics, 5(3), 121-129.

4. Harmston, S. (1998). Gold as a Store of Value. Centre for Public Policy Studies, The World Gold Council.

5. Hassani, H., E.S. Silva, R. Gupta, and M.K. Segnon, Forecasting the price of gold. Applied Economics, 2015. https://doi.org/10.1109/ICSECS.2015.7333120

6. Ismail, Z., Yahya, A., &Shabri, A. (2009). Forecasting gold prices using multiple linear regression method. American Journal of Applied Sciences, 6(8), 1509.

7. Karunagaran, A. (2011). Recent Global Crisis and the Demand for Gold by Central Banks: An Analytical Perspective. 2011l07l09]. https://www. rbi. org. in/Scripts/PublicationsView. aspx.

8. Khashei, M., Hejazi, S. R., Bijari, M. (2008). A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. Fuzzy Sets and Systems, 159:769-786.

9. Koutsoyiannis, A. (1983). A short-run pricing model for a speculative asset, tested with data from the gold bullion market. Applied Economics, 15(5), 563-581.

10. Kristjanpoller, Fadic and Minutolo (2014) Gold price volatility: A forecasting approach. https://doi.org/10.1016/j.eswa.2015.04.058

11. Mahato, P.K. and D.V. Attar. Prediction of gold and silver stock price using ensemble models. in International Conference on Advances in Engineering and Technology Research (ICAETR). 2014. Unnao. https://doi.org/10.1109/ICSECS.2015.7333120.

12. Mandelbrot, B. B. (1967): How long is the coast of Britain? Statistical self-similarity and fractional dimension. Science, 156, 636–638. http://dx.doi.org/10.1126/science.156.3775.636

13. Mui, H. W., & Chu, C. W. (1993). Forecasting the spot price of gold: combined forecast approaches versus a composite forecast approach. Journal of Applied Statistics, 20(1), 13-23.

14. Sjaastad, L. A., &Scacciavillani, F. (1996). The price of gold and the exchange rate. Journal of international money and finance, 15(6), 879-897.

15. Smith, G. (2001). The price of gold and stock price indices for the United States. The World Gold Council, 8(1), 1-16.

16. Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of econometrics, 66(1-2), 225-250.

17. Tully, E., & Lucey, B. M. (2007). A power GARCH examination of the gold market. Research in International Business and Finance, 21(2), 316-325.

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Published

31.10.2020

How to Cite

Pol, N., Poddar, A., & Thomas, F. (2020). Gold Prices Movements and Predictions with Reference to Indian Context. International Journal of Psychosocial Rehabilitation, 24(8), 10050-10067. https://doi.org/10.61841/evfarv90