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  • Issue #4
  • Metals futures market: a comparative analysis of investment and arbitrage strategies

Metals futures market: a comparative analysis of investment and arbitrage strategies

  • Received November 27, 2019;
    Accepted December 16, 2019;
    Published March 3, 2020
  • Author(s)
    Link to ORCID Index: https://orcid.org/0000-0002-2009-1451
    Lidiya Guryanova
    ORCID ,
    Link to ORCID Index: https://orcid.org/0000-0002-0073-8457
    Natalia Chernova
    ORCID
  • DOI
    http://dx.doi.org/10.21511/dm.17(4).2019.04
  • Article Info
    Volume 17 2019, Issue #4, pp. 42-54
  • TO CITE
  • Cited by
    1 articles
    Journal title: Resources Policy
    Article title: Beyond the glitter: An empirical assessment of the true risk and hedging role of precious metals
    DOI: 10.1016/j.resourpol.2024.105238
    Volume: 96 / Issue: / First page: 105238 / Year: 2024
    Contributors: Chitrakalpa Sen, Gagari Chakrabarti
  • 587 Views
  • 256 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

The article deals with the application of optimal portfolio theory and pair trading theory on the metals futures market. Advantages of the futures market over the spot market include relatively small initial price, low transaction costs, and high volatility. The main aim of the study is to explore the potential of both strategies for effective trading. The following financial instruments were chosen as the inputs of the models: futures on industrial metals (aluminum, copper, nickel, zinc, lead, tin), futures on precious metals (gold and silver). When building the optimal portfolio, it was decided to include Dow Jones Index futures and S&P Index futures among metals. This is because these instruments are extremely volatile and may play the role of a hedge in the portfolio. A drawdown indicator was used to assess the effectiveness of each strategy. The results show that both strategies can be applied on the real-life market. The final choice will depend on the level of risk taking by investors and the desired value of return.

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  • PAPER PROFILE
  • AUTHORS CONTRIBUTIONS
  • FIGURES
  • TABLES
  • REFERENCES
  • Keywords
    correlation, futures, metals market, model, optimal portfolio, pairs trading, ratio, return, risk, stationarity
  • JEL Classification (Paper profile tab)
    C22, C58, C61
  • References
    17
  • Tables
    5
  • Figures
    11
    • Figure 1. Average daily notional value, October 2019 (in blns of dollars)
    • Figure 2. Daily volume for top six metal futures on CME
    • Figure 3. Daily volume for top six metal futures on LME
    • Figure 4. Average risk and return
    • Figure 5. Efficient frontier
    • Figure 6. Total share of gold and INDU Index
    • Figure 7. Efficient frontiers for two portfolio sets
    • Figure 8. Normalized ratios
    • Figure 9. Ratio trading results – lead and copper
    • Figure 10. Ratio trading results – tin and silver
    • Figure 11. Ratio trading results – tin and gold
    • Table 1. Correlation coefficients
    • Table 2. Portfolio members, risk and return
    • Table 3. List of pairs
    • Table 4. Dickey-Fuller test critical values
    • Table 5. Ratios’ statistics
    • Chen, D., Cui, J., Gao, Y., & Leilei, W. (2018). Pairs trading in Chinese commodity futures markets: an adaptive cointegration approach. Accounting & Finance, 57(5), 1237-1264.
    • CME Group (n.d.).
    • Do, B., & Faff, R. (2010). Does simple Pairs trading still work? Financial Analysts Journal, 66(4), 83-95.
    • Elliott, R., van der Hoek, J., & Malcolm, W. (2005). Pairs trading. Quantitative Finance, 5(3), 271-276.
    • Fernholz, R., & Maguire, C. (2007). The statistics of statistical arbitrage. Financial Analysts Journal, 63(5), 46-52.
    • Gatev, E., Goetzmann, W., & Rouwenhorst, K. (2006). Pairs trading: Performance of a relative-value arbitrage rule (NBER Working Paper No. 7032). Review of Financial Studies, 19(3), 797-827.
    • Göncü, A., & Akyildirim, E. (2016). Statistical Arbitrage with Pairs Trading. International Review of Finance, 16(2), 307-319.
    • Hull, J. (2012). Options, Futures, and Other Derivatives (Global Edition) (896 p.). London: Pearson PLC.
    • Krauss, C. (2017). Statistical Arbitrage Pairs Trading Strategies: Review and Outlook. Journal of Economic Surveys, 31(2), 513-545.
    • Mangram, M. (2013). A Simplified Perspective of the Markowitz Portfolio Theory. Global Journal of Business Research, 7(1), 59-70.
    • Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91.
    • Nijman, T., de Roon, F., & Veld, C. (1996). Pricing Term Structure Risk in Futures Markets (CentER Discussion Paper. Vol. 1996-78) (25 p.). Tilburg: Finance.
    • Sharpe, W. (1964). Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. The Journal of Finance, 19(3), 425-442.
    • Stübinger, J., & Bredthauer, J. (2017). Statistical Arbitrage Pairs Trading with High-frequency Data. International Journal of Economics and Financial Issues, 7(4), 650-662.
    • Tobin, J. (1969). A General Equilibrium Approach to Monetary Theory. Journal of Money, Credit and Banking, 1(1), 15-29.
    • Vidyamurthy, G. (2004). Pairs Trading: Quantitative Methods and Analysis (224 p.). USA: John Wiley & Sons, Inc.
    • Vollmer, M. (2015). A Beta-return Efficient Portfolio Optimisation Following the CAPM. Wiesbaden: Springer Gabler.
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