Skip to main content
SpringerLink
Log in

Risk management for international portfolios with basket options: A multi-stage stochastic programming approach

  • Published:
Journal of Systems Science and Complexity Aims and scope Submit manuscript

Abstract

The authors consider the problem of active international portfolio management with basket options to achieve optimal asset allocation and combined market risk and currency risk management via multi-stage stochastic programming (MSSP). The authors note particularly the novel consideration and significant benefit of basket options in the context of portfolio optimization and risk management. Extensive empirical tests strongly demonstrate that basket options consistently have more clearly improvement on portfolio performances than a portfolio of vanilla options written on the same underlying assets. The authors further show that the MSSP model provides as a supportive tool for asset allocation, and a suitable test bed to empirically investigate the performance of alternative strategies.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Hobson D, Laurence P, and Wang T H, Static-arbitrage upper bounds for the prices of basket options, Quantitative Finance, 2005, 5(4): 329–4.

    Article  MATH  MathSciNet  Google Scholar 

  2. Pellizzari P, Static hedging of multivariate derivatives by simulation, European Journal of Operational Research, 2005, 166(2): 507–4.

    Article  MATH  MathSciNet  Google Scholar 

  3. Chen X, Deelstra G, Dhaene J, and Vanmaele M, Static super-replicating strategies for a class of exotic options, Insurance: Mathematics and Economics, 2008, 42: 1067–3.

    MATH  MathSciNet  Google Scholar 

  4. Mi H and Zhang S, Dynamic valuation of options on non-traded assets and trading strategies, Journal of Systems Science and Complexity, 2013, 26(6): 991–4.

    Article  MATH  MathSciNet  Google Scholar 

  5. Merton R C, Optimum consumption and portfolio rules in a continuous-time model, Journal of Economic Theory, 1971, 3: 373–3.

    Article  MATH  MathSciNet  Google Scholar 

  6. Samuelson P A, Lifetime portfolio selection by dynamic stochastic programming, Review of Economics and Statistics, 1969, 51: 239–3.

    Article  Google Scholar 

  7. Fama E F, Multiperiod consumption-investment decisions, American Economic Review, 1970, 60: 163–3.

    Google Scholar 

  8. Kim T S and Omberg E, Dynamic nonmyopic portfolio behavior, Review of Financial Studies, 1996, 9: 141–3.

    Article  Google Scholar 

  9. Wachter J, Portfolio and consumption decisions under mean-reverting returns: An exact solution for complete markets, Journal of Financial and Quantitative Analysis, 2002, 37: 63–3.

    Article  Google Scholar 

  10. Brennan M J, Schwartz E S, and Lagnado R, Strategic asset allocation, Journal of Economic Dynamics and Control, 1997, 21: 1377–3.

    Article  MATH  MathSciNet  Google Scholar 

  11. Balduzzi P and Lynch A W, Transaction costs and predictability: Some utility cost calculations, Journal of Financial Economics, 1999, 52: 47–3.

    Article  Google Scholar 

  12. Brandt M W, Estimating portfolio and consumption choice: A conditional Euler equations approach, Journal of Finance, 1999, 54: 1609–3.

    Article  Google Scholar 

  13. Barberis N, Investing for the long run when returns are predictable, Journal of Finance, 2000, 55: 225–3.

    Article  Google Scholar 

  14. Campbell J Y and Viceira L M, Who should buy long-term bonds? American Economic Review, 1999, 91: 99–3.

    Article  Google Scholar 

  15. Campbell J Y, Chan Y L, and Viceira L M, A multivariate model of strategic asset allocation, Journal of Financial Economics, 2003, 67: 41–3.

    Article  Google Scholar 

  16. Dammon R M, Spatt C S, and Zhang H H, Optimal consumption and investment with capital gains taxes, Review of Financial Studies, 2001, 14: 583–3.

    Article  Google Scholar 

  17. Brandt MW, Goyal A, Santa-Clara P, and Stroud J R, A simulation approach to dynamic portfolio choice with an application to learning about return predictability, Review of Financial Studies, 2005, 18(3): 831–4.

    Article  Google Scholar 

  18. Klaassen P, Financial asset-pricing theory and stochastic programming models for asset/liability management: A synthesis, Management Science, 1998, 44(1): 31–4.

    Article  MATH  Google Scholar 

  19. Gaivoronski A A, Krylov S, and Van der Wijst N, Optimal portfolio selection and dynamic benchmark tracking, European Journal of Operational Research, 2005, 163(1): 115–4.

    Article  MATH  MathSciNet  Google Scholar 

  20. Abdelaziz F B, Aouni B, and Fayedh R E, Multi-objective stochastic programming for portfolio selection, European Journal of Operational Research, 2007, 177(3): 1811–4.

    Article  MATH  Google Scholar 

  21. Topaloglou N, Vladimirou H, and Zenios S A, A dynamic stochastic programming model for international portfolio management, European Journal of Operational Research, 2008, 185: 1501–3.

    Article  MATH  MathSciNet  Google Scholar 

  22. Topaloglou N, Vladimirou H, and Zenios S A, Optimizing international portfolios with options and forwards, Journal of Banking & Finance, 2011, 35(12): 3188–4.

    Article  Google Scholar 

  23. Xu Y H, Li Z F, and Tan K S, Optimal investment with noise trading risk, Journal of Systems Science and Complexity, 2008, 21(4): 519–4.

    Article  MATH  MathSciNet  Google Scholar 

  24. Zeng Y and Li Z, Asset-liability management under benchmark and mean-variance criteria in a jump diffusion market, Journal of Systems Science and Complexity, 2011, 24(2): 317–4.

    Article  MATH  MathSciNet  Google Scholar 

  25. He C and Meng W, Dynamic portfolio choice under the time-varying, jumps, and knight uncertainty of asset return process, Journal of Systems Science and Complexity, 2012, 25(5): 896–4.

    Article  MATH  MathSciNet  Google Scholar 

  26. Zhang J L, Resource planning and allocation problem under uncertain environment, Journal of Systems Science and Complexity, 2015, DOI: 10.1007/s11424-014-2183-0.

    Google Scholar 

  27. Dionne G, Gauthier G, Ouertani N, and Tahani N, Heterogeneous basket options pricing using analytical approximations, Multinational Finance Journal, 2011, 15(1–2): 47–2.

    Google Scholar 

  28. Khaliq A Q M, Voss D A, and Yousuf M, Pricing exotic options with L-stable Pad schemes, Journal of Banking & Finance, 2007, 31(11): 3438–4.

    Article  Google Scholar 

  29. Flamouris D and Giamouridis D, Approximate basket option valuation for a simple jump process, Journal of Futures Markets, 2007, 27(9): 819–4.

    Article  Google Scholar 

  30. Giannopoulos K, Nonparametric, conditional pricing of higher order multivariate contingent claims, Journal of Banking & Finance, 2008, 32(9): 1907–4.

    Article  MathSciNet  Google Scholar 

  31. Bedendo M, Campolongo F, Joossens E, and Saita F, Pricing multiasset equity options: How relevant is the dependence function? Journal of Banking & Finance, 2010, 34(4): 788–4.

    Article  Google Scholar 

  32. Natarajan K, Pachamanova D, and Sim M, Constructing risk measures from uncertainty sets, 2009, Operations Research, 57(5): 1129–4.

  33. Chen W, Sim M, Sun J, and Teo C P, From CVaR to uncertainty set: Implications in joint chance-constrained optimization, Operations Research, 2010, 58(2): 470–4.

    Article  MATH  MathSciNet  Google Scholar 

  34. Rockafellar R and Uryasev S, Conditional value-at-risk for general distributions, Journal of Banking & Finance, 2002, 26(7): 1443–4.

    Article  Google Scholar 

  35. Heitsch H and Römisch W, Scenario reduction algorithms in stochastic programming, Computational Optimization and Applications, 2003, 24: 187–3.

    Article  MATH  MathSciNet  Google Scholar 

  36. Dupâcová J, Gröwe-Kuska N, and Römisch W, Scenario reduction in stochastic programming: An approach using probability metrics, Mathematical Programming, Series A, 2003, 95: 493–3.

    Article  MATH  Google Scholar 

  37. Høyland K and Wallance S W, Generating scenario trees for multistage decision problems, Management Science, 2001, 47(2): 295–4.

    Article  Google Scholar 

  38. Rasmussen K M and Clausen J, Mortgage loan portfolio optimization using multi-stage stochastic programming, Journal of Economic Dynamics and Control, 2007, 31(3): 742–4.

    Article  MATH  MathSciNet  Google Scholar 

  39. Geyer A, Hanke M, and Weissensteiner A, No-arbitrage conditions, scenario trees, and multi-stage financial optimization, European Journal of Operational Research, 2010, 206: 609–3.

    MATH  MathSciNet  Google Scholar 

  40. Hochreiter R and Pflug G Ch, Financial scenario generation for stochastic multi-stage decision processes as facility location problems, Annals of Operations Research, 2007, 152(1): 257–4.

    Article  MATH  MathSciNet  Google Scholar 

  41. Klaassen P, Comment on “generating scenario trees for multistage decision problems”, Management Science, 2002, 48(11): 1512–4.

    Article  MATH  Google Scholar 

  42. Sortino F and Van der Meer R, Downside risk-capturing what’s at stake in investment situations, Journal of Portfolio Management, 1991, 17(4): 27–4.

    Article  Google Scholar 

  43. Grinblatt M and Titman S, Performance measurement without benchmarks: An examination of mutual fund returns, Journal of Business, 1993, 66(1): 47–4.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Finance, Central University of Finance and Economics, Beijing, 100081, China

    Libo Yin

  2. School of Economics and Management, Beihang University, Beijing, 100191, China

    Liyan Han

Authors
  1. Libo Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liyan Han
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Libo Yin.

Additional information

This research is fully supported by the National Natural Science Foundation of the Republic of China with financially funding under Grant Nos. 71401193 and 71371022.

This paper was recommended for publication by Editor WANG Shouyang.

These strategies are composed of spot transactions, forwards, futures and in many cases options on a single currency or asset.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yin, L., Han, L. Risk management for international portfolios with basket options: A multi-stage stochastic programming approach. J Syst Sci Complex 28, 1279–1306 (2015). https://doi.org/10.1007/s11424-015-3001-z

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11424-015-3001-z

Keywords

Search

Navigation