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Do LBMA gold price follow random-walk?

Abstract

The present study attempted to analyse the random-walk characteristics of the gold spot price of the London Bullion Market Association (LBMA) by using several linear and nonlinear models. The research collects two decades of daily data from 3rd February 2000 to 2nd October 2020. Econometric tests such as serial correlation test, unit-root tests, multiple variance ratio (MVR), and the BDS test were applied to examine the linear and nonlinear dependence of return series. Further, we employed all the tests from ARCH family to examine the volatility clustering of the gold return series. The results of serial correlation and the unit-root test suggest that the gold return is stationary, therefore does not follow the random-walk benchmark, and hence the gold market is inefficient. EGARCH results indicate that the positive news has a more significant impact on the gold return than the negative news. The findings have important implications for the efficient portfolio investments, and better hedging opportunities for the investors.

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Notes

  1. The study finds the increase in gold prices of around 42 percentages while there exists a major trench in other financial markets.

  2. [26] documents three versions of informational efficiency such as weak, semi-strong, and strong based on the different types of available information in the market. The financial market is weakly efficient if its current prices quickly reflects all the preceding information that contained in the market trading activity. Under semi-strong category, the asset prices should instantly absorb all the publicly available information set. Strong- form of efficiency enables the prices to absorb both the public and private information set during the trading period.

  3. However, there is a similar study which explored conditional volatility to examine bitcoin market efficiency [27].

  4. The details of the auction of LBMA and the mechanism of the LBMA spot gold price can be obtained from the World Gold Council. The data is also publicly available in the World Gold Council web site. For sake of brevity, we left it on the interest of the readers.

  5. SETS™ is an electronic order book, which trades the UK’s most liquid securities, and therefore raises 86 percentages of transactions in each year and a daily average of 69,000 electronic bargains.

  6. Landmark™ is a market for regional companies which is dedicated to increase the liquidity of the shares traded, and therefore acts as a key source of information about local companies.

  7. For details, refer the cite, https://www.cnbc.com/2016/07/01/brexit-helps-gold-gain-over-25-in-first-half-of-2016.html accessed on 17/12/2020 at 6.50 p.m.

  8. Breusch-Godfrey test examines the auto-correlation of the errors in the regression model. The test is based on the idea of Lagrange Multiplier testing, hence, called as Breusch-Godfrey LM test. It test the null hypothesis of no serial correlation. We have not presented the results of LM statistics here because of the conciseness of space; however, can be obtained with request.

  9. The Augmented Dickey and Fuller (ADF) test examines the null hypothesis that a time series has unit root. The hypothesis will increase the chance of rejection if the magnitude of t-statistics obtained from ADF test will increase with negative sign. The acceptance of null hypothesis indicates the time series has unit root and hence follows random walk, vice-versa. If the null is rejected, then, the series do not follow random walk; therefore, the series is stationary.

  10. The interpretation of Phillips–Perron (PP) test is similar to the Augmented Dickey and Fuller test.

  11. The Kwaiatkowski–Phillips–Schmidt–Shin (KPSS) test examines the null hypothesis that the time series is stationary. The acceptance of the hypothesis will show the time series is stationary and does not follow random walk.

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Acknowledgements

We thank the Indian Institute of Technology Kharagpur, India for providing infrastructure support during the initial stage of this research.

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Correspondence to Biswabhusan Bhuyan.

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Appendix

Appendix

  AC PAC Q-Stat Prob
1  − 0.000  − 0.000 0.0002 0.989
2 0.004 0.004 0.0666 0.967
3 0.008 0.008 0.3826 0.944
4 0.000 0.000 0.3826 0.984
5  − 0.002  − 0.002 0.4135 0.995
6  − 0.032  − 0.033 6.1272 0.409
7  − 0.012  − 0.012 6.9474 0.434
8  − 0.004  − 0.004 7.0516 0.531
9 0.021 0.021 9.3497 0.406
10  − 0.015  − 0.015 10.629 0.387
11  − 0.042  − 0.042 20.173 0.043
12  − 0.009  − 0.011 20.626 0.056
13 0.031 0.031 25.796 0.018
14  − 0.009  − 0.008 26.233 0.024
15  − 0.018  − 0.018 28.083 0.021
16 0.014 0.013 29.214 0.023
17 0.014 0.012 30.351 0.024
18  − 0.004  − 0.006 30.431 0.033
19  − 0.005  − 0.004 30.590 0.045
20 0.021 0.023 33.026 0.034
21  − 0.044  − 0.046 43.358 0.003
22 0.008 0.005 43.733 0.004
23  − 0.030  − 0.029 48.636 0.001
24  − 0.032  − 0.028 54.122 0.000
25 0.006 0.005 54.317 0.001
26  − 0.012  − 0.013 55.107 0.001
27  − 0.024  − 0.024 58.129 0.000
28 0.005 0.007 58.286 0.001
29 0.034 0.030 64.408 0.000
30  − 0.027  − 0.029 68.409 0.000
31  − 0.013  − 0.013 69.328 0.000
32 0.002  − 0.000 69.350 0.000
33 0.016 0.013 70.773 0.000
34  − 0.014  − 0.015 71.884 0.000
35 0.003 0.002 71.933 0.000
36  − 0.027  − 0.028 75.777 0.000

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Bhuyan, B., Patra, S. & Bhuian, R.K. Do LBMA gold price follow random-walk?. Gold Bull 54, 151–159 (2021). https://doi.org/10.1007/s13404-021-00300-w

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Keywords

  • Random-walk
  • Gold price
  • Unit-root
  • ARCH

JEL classifications

  • G1
  • G14
  • G12
  • C12