The law of one price in quadratic hedging and mean-variance portfolio selection
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作者:
Cerny, Ales
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机构:
City St Georges Univ London, Bayes Business Sch, 106 Bunhill Row, London EC1Y 8TZ, EnglandCity St Georges Univ London, Bayes Business Sch, 106 Bunhill Row, London EC1Y 8TZ, England
Cerny, Ales
[1
]
Czichowsky, Christoph
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机构:
London Sch Econ & Polit Sci, Dept Math, Columbia House,Houghton St, London WC2A 2AE, EnglandCity St Georges Univ London, Bayes Business Sch, 106 Bunhill Row, London EC1Y 8TZ, England
Czichowsky, Christoph
[2
]
机构:
[1] City St Georges Univ London, Bayes Business Sch, 106 Bunhill Row, London EC1Y 8TZ, England
[2] London Sch Econ & Polit Sci, Dept Math, Columbia House,Houghton St, London WC2A 2AE, England
Law of one price;
E-density;
Efficient frontier;
Mean-variance portfolio selection;
Quadratic hedging;
FUNDAMENTAL THEOREM;
ARBITRAGE;
D O I:
10.1007/s00780-025-00563-7
中图分类号:
F8 [财政、金融];
学科分类号:
0202 ;
摘要:
The law of one price (LOP) broadly asserts that identical financial flows should command the same price. We show that when properly formulated, the LOP is the minimal condition for a well-defined mean-variance portfolio allocation framework without degeneracy. Crucially, the paper identifies a new mechanism through which the LOP can fail in a continuous-time L-2-setting without frictions, namely "trading from just before a predictable stopping time", which surprisingly identifies LOP violations even for continuous price processes. Closing this loophole allows us to give a version of the "fundamental theorem of asset pricing" appropriate in the quadratic context, establishing the equivalence of the economic concept of the LOP with the probabilistic property of the existence of a local E-martingale state price density. The latter provides unique prices for all square-integrable contingent claims in an extended market and subsequently plays an important role in mean-variance portfolio selection and quadratic hedging. Mathematically, we formulate a novel variant of the uniform boundedness principle for conditionally linear functionals on the L-0-module of conditionally square-integrable random variables. We then study the representation of time-consistent families of such functionals in terms of stochastic exponentials of a fixed local martingale.
机构:
Univ New S Wales, Sch Risk & Actuarial Studies, Sydney, NSW 2052, Australia
Univ New S Wales, UNSW Business Sch, CEPAR, Sydney, NSW 2052, AustraliaUniv New S Wales, Sch Risk & Actuarial Studies, Sydney, NSW 2052, Australia
机构:
East China Normal Univ, Sch Stat, Key Lab Adv Theory & Applicat Stat & Data Sci MOE, Shanghai 200062, Peoples R ChinaEast China Normal Univ, Sch Stat, Key Lab Adv Theory & Applicat Stat & Data Sci MOE, Shanghai 200062, Peoples R China
Zhang, Yu
Jin, Zhuo
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机构:
Macquarie Univ, Dept Actuarial Studies & Business Analyt, Sydney, NSW 2109, AustraliaEast China Normal Univ, Sch Stat, Key Lab Adv Theory & Applicat Stat & Data Sci MOE, Shanghai 200062, Peoples R China
Jin, Zhuo
Wei, Jiaqin
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机构:
East China Normal Univ, Sch Stat, Key Lab Adv Theory & Applicat Stat & Data Sci MOE, Shanghai 200062, Peoples R ChinaEast China Normal Univ, Sch Stat, Key Lab Adv Theory & Applicat Stat & Data Sci MOE, Shanghai 200062, Peoples R China
Wei, Jiaqin
Yin, George
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机构:
Univ Connecticut, Dept Math, Storrs, CT 06269 USAEast China Normal Univ, Sch Stat, Key Lab Adv Theory & Applicat Stat & Data Sci MOE, Shanghai 200062, Peoples R China
机构:
Cent Univ Finance & Econ, China Inst Actuarial Sci, Beijing 100081, Peoples R ChinaSun Yat Sen Univ, Sun Yat Sen Business Sch, Guangzhou 510275, Guangdong, Peoples R China
Wu, Huiling
Li, Zhongfei
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机构:
Sun Yat Sen Univ, Sun Yat Sen Business Sch, Guangzhou 510275, Guangdong, Peoples R China
Sun Yat Sen Univ, Lingnan Univ Coll, Guangzhou 510275, Guangdong, Peoples R ChinaSun Yat Sen Univ, Sun Yat Sen Business Sch, Guangzhou 510275, Guangdong, Peoples R China
机构:
Vanguard Grp Inc, CAI Data Sci & Machine Learning, Malvern, PA USAVanguard Grp Inc, CAI Data Sci & Machine Learning, Malvern, PA USA
Wang, Haoran
Zhou, Xun Yu
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机构:
Columbia Univ, Dept Ind Engn & Operat Res, New York, NY 10027 USA
Columbia Univ, Data Sci Inst, New York, NY 10027 USAVanguard Grp Inc, CAI Data Sci & Machine Learning, Malvern, PA USA