Skip to main content
SpringerLink
Log in

Optimizing a Portfolio of Liquid and Illiquid Assets

  • Chapter
  • First Online:
Optimal Financial Decision Making under Uncertainty

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 245))

  • 1717 Accesses

Abstract

Current market conditions pose new challenges for institutional investors. Traditional asset and liability models are struggling to meet investors’ needs due to poor performance of equity and bond markets. The move of portfolio allocation to alternative assets is evident. As a result, illiquidity issues and rebalancing difficulty arise. We propose some new tactics of commodity futures to enhance the performance of portfolio return as well as solving illiquidity issues. Hidden Markov Model and multistage stochastic optimization are used to systematically optimize portfolio over a set of assets.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The “golden rule” of 60/40 stock bond mix is explained in [1] by Malkiel.

  2. 2.

    Please refer to [2].

  3. 3.

    Please refer to [3].

  4. 4.

    Please refer to [4].

  5. 5.

    The figure is from course lecture notes of Golden [5].

  6. 6.

    Please refer to [6] by Buffet for detailed discussion of the legendary investor’s investment philosophy.

  7. 7.

    DPT Management constructed this index for FTSE, called the FTSE Target Commodity Family. All rights reserved. See Mulvey [7] for details.

References

  1. B.G. Malkiel, A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing, 10th edn. (W. W. Norton, 2012)

    Google Scholar 

  2. Deutsche Insurance Asset Management, TrendLines semi-annual report on development in the insurance investment market

    Google Scholar 

  3. T. Watson, Global Pension Assets Study 2013, 2012, 2011

    Google Scholar 

  4. Vanguard Group, Inc. Survey of Defined Benefit Plan Sponsors, 2012 and 2010

    Google Scholar 

  5. A. Golden, Endowment Management at Princeton University, Highlighting Some Quantitative Methods, Lecture notes from course ORF 311

    Google Scholar 

  6. W. Buffet, The Essays of Warren Buffet: Lessons for Investors and Managers (Wiley, Singapore, 2013)

    Google Scholar 

  7. J.M. Mulvey, A rule based commodity index. J. Investment Manage. (2013)

    Google Scholar 

  8. J.M. Mulvey, K. Simsek, Z. Zhang, Improving performance for pension plans. J. Asset Manage. 7, 93–108 (2006)

    Article  Google Scholar 

  9. J.M. Mulvey, C. Ural, Z. Zhang, Improving performance for long-term investors: wide diversification. Leverage, and overlay strategies. Quant. Financ. 7(2), 1–13 (2007)

    Article  Google Scholar 

  10. J.M. Mulvey, W. Kim, Constantly rebalanced portfolio—is mean reversion necessary? in Encyclopedia of Quantitative Finance (Wiley, UK, 2009)

    Google Scholar 

  11. J.M. Mulvey, G. Gould, C. Morgan, An asset and liability management system for Towers Perrin-Tillinghast. Interfaces 30(1), 96–114 (2000)

    Article  Google Scholar 

  12. J.M. Mulvey, W. Kim, M. Bilgili, Dynamic investment strategies and rebalancing gains, in The Euromoney Algorithmic Trading Handbook 2007/2008 (2008)

    Google Scholar 

  13. J.M. Mulvey, W. Kim, Y. Ma, Duration-enhancing overlay strategies for defined benefit pension plans. J. Asset Manage. 11, 136–162 (2010)

    Article  Google Scholar 

  14. J. Frankel, R. Andrew, Determinants of Agricultural and Mineral Commodity Prices. HKS Faculty Research Working Paper Series RWP10-038 (John F. Kennedy School of Government, Harvard University, 2010)

    Google Scholar 

  15. J. Gruber, R. Vigfusson, Interest rates and the volatility and correlation of commodity prices. International Finance Discussion Papers 1065 (Board of Governors of the Federal Reserve System, Washington, DC, 2012)

    Google Scholar 

  16. K. Tang, W. Xiong, Index investment and financialization of commodities. Financ. Anal. J. 68(6), 54–74 (2012)

    Article  Google Scholar 

  17. J.D. Hamilton, A new approach to the economic analysis of non-stationary time series and the business cycle. Econometrica 57, 357–384 (1989)

    Article  Google Scholar 

  18. C. Turner, R. Startz, C.R. Nelson, A Markov model of heteroskedasticity, risk, and learning in the stock market. J. Financ. Econ. 25, 3–22 (1989)

    Article  Google Scholar 

  19. B. Hansen, The likelihood ratio test under non-standard conditions: testing the Markov switching model of GNP. J. Appl. Econ. 7, 61–82 (1992)

    Article  Google Scholar 

  20. J.D. Hamilton, R. Susmel, Autoregressive conditional heteroskedasticity and changes in regime. J. Econ. 64, 307–333 (1994)

    Article  Google Scholar 

  21. R. Garcia, Asymptotic null distribution of the likelihood ratio test in Markov switching models. Int. Econ. Rev. 39, 763–788 (1998)

    Article  Google Scholar 

  22. G. Bae, W. Kim, J.M. Mulvey, Dynamic asset allocation for varied financial markets under regime switching framework. Eur. J. Oper. Res. (2013)

    Google Scholar 

  23. A.M. Fraser, Hidden Markov Models and Dynamical Systems. Society for Industrial and Applied Mathematics (2008)

    Google Scholar 

  24. M.C. Bishop, Pattern Recognition and Machine Learning. Information Science and Statistics (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Operations Research and Financial Engineering, Bendheim Center for Finance, Princeton University, Princeton, NJ, USA

    John M. Mulvey

  2. Department of Industrial and System Engineering, KAIST, Daejeon, South Korea

    Woo Chang Kim

  3. Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ, USA

    Changle Lin

  4. Merrill Lynch Wealth Management, Chief Investment Office, New York, NY, USA

    Changle Lin

Authors
  1. John M. Mulvey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Woo Chang Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Changle Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Changle Lin .

Editor information

Editors and Affiliations

  1. University of Bergamo, Bergamo, Italy

    Giorgio Consigli

  2. EPFL, Lausanne, Cham, United Kingdom

    Daniel Kuhn

  3. Politecnico di Torino, Torino, Italy

    Paolo Brandimarte

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Mulvey, J.M., Kim, W.C., Lin, C. (2017). Optimizing a Portfolio of Liquid and Illiquid Assets. In: Consigli, G., Kuhn, D., Brandimarte, P. (eds) Optimal Financial Decision Making under Uncertainty. International Series in Operations Research & Management Science, vol 245. Springer, Cham. https://doi.org/10.1007/978-3-319-41613-7_7

Download citation

Publish with us

Policies and ethics

Search

Navigation