The weighted majority algorithm
Information and Computation
Journal of the ACM (JACM)
Universal portfolios with and without transaction costs
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
The Cost of Achieving the Best Portfolio in Hindsight
Mathematics of Operations Research
The statistical adversary allows optimal money-making trading strategies
Proceedings of the sixth annual ACM-SIAM symposium on Discrete algorithms
The Nonstochastic Multiarmed Bandit Problem
SIAM Journal on Computing
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Efficient algorithms for universal portfolios
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Competitive algorithms for VWAP and limit order trading
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Improved second-order bounds for prediction with expert advice
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Universal portfolios with side information
IEEE Transactions on Information Theory
Learning, regret minimization and option pricing
TARK '07 Proceedings of the 11th conference on Theoretical aspects of rationality and knowledge
Optimal algorithms for k-search with application in option pricing
ESA'07 Proceedings of the 15th annual European conference on Algorithms
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
Regret minimization algorithms for pricing lookback options
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Pricing exotic derivatives using regret minimization
SAGT'11 Proceedings of the 4th international conference on Algorithmic game theory
Minimax option pricing meets black-scholes in the limit
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
Lower bounds on individual sequence regret
ALT'12 Proceedings of the 23rd international conference on Algorithmic Learning Theory
Theoretical Computer Science
Artificial Life
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In this work we show how to use efficient online trading algorithms to price the current value of financial instruments, such as an option. We derive both upper and lower bounds for pricing an option, using online trading algorithms.Our bounds depend on very minimal assumptions and are mainly derived assuming that there are no arbitrage opportunities.