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.
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References
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.
Pellizzari P, Static hedging of multivariate derivatives by simulation, European Journal of Operational Research, 2005, 166(2): 507–4.
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.
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.
Merton R C, Optimum consumption and portfolio rules in a continuous-time model, Journal of Economic Theory, 1971, 3: 373–3.
Samuelson P A, Lifetime portfolio selection by dynamic stochastic programming, Review of Economics and Statistics, 1969, 51: 239–3.
Fama E F, Multiperiod consumption-investment decisions, American Economic Review, 1970, 60: 163–3.
Kim T S and Omberg E, Dynamic nonmyopic portfolio behavior, Review of Financial Studies, 1996, 9: 141–3.
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.
Brennan M J, Schwartz E S, and Lagnado R, Strategic asset allocation, Journal of Economic Dynamics and Control, 1997, 21: 1377–3.
Balduzzi P and Lynch A W, Transaction costs and predictability: Some utility cost calculations, Journal of Financial Economics, 1999, 52: 47–3.
Brandt M W, Estimating portfolio and consumption choice: A conditional Euler equations approach, Journal of Finance, 1999, 54: 1609–3.
Barberis N, Investing for the long run when returns are predictable, Journal of Finance, 2000, 55: 225–3.
Campbell J Y and Viceira L M, Who should buy long-term bonds? American Economic Review, 1999, 91: 99–3.
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.
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.
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.
Klaassen P, Financial asset-pricing theory and stochastic programming models for asset/liability management: A synthesis, Management Science, 1998, 44(1): 31–4.
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.
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.
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.
Topaloglou N, Vladimirou H, and Zenios S A, Optimizing international portfolios with options and forwards, Journal of Banking & Finance, 2011, 35(12): 3188–4.
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.
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.
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.
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.
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.
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.
Flamouris D and Giamouridis D, Approximate basket option valuation for a simple jump process, Journal of Futures Markets, 2007, 27(9): 819–4.
Giannopoulos K, Nonparametric, conditional pricing of higher order multivariate contingent claims, Journal of Banking & Finance, 2008, 32(9): 1907–4.
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.
Natarajan K, Pachamanova D, and Sim M, Constructing risk measures from uncertainty sets, 2009, Operations Research, 57(5): 1129–4.
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.
Rockafellar R and Uryasev S, Conditional value-at-risk for general distributions, Journal of Banking & Finance, 2002, 26(7): 1443–4.
Heitsch H and Römisch W, Scenario reduction algorithms in stochastic programming, Computational Optimization and Applications, 2003, 24: 187–3.
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.
Høyland K and Wallance S W, Generating scenario trees for multistage decision problems, Management Science, 2001, 47(2): 295–4.
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.
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.
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.
Klaassen P, Comment on “generating scenario trees for multistage decision problems”, Management Science, 2002, 48(11): 1512–4.
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.
Grinblatt M and Titman S, Performance measurement without benchmarks: An examination of mutual fund returns, Journal of Business, 1993, 66(1): 47–4.
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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.
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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
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DOI: https://doi.org/10.1007/s11424-015-3001-z