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
Universal Portfolios With and Without Transaction Costs
Machine Learning - Special issue: computational learning theory, COLT '97
Efficient algorithms for universal portfolios
The Journal of Machine Learning Research
Online trading algorithms and robust option pricing
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Prediction, Learning, and Games
Prediction, Learning, and Games
Improved second-order bounds for prediction with expert advice
Machine Learning
Can we learn to beat the best stock
Journal of Artificial Intelligence Research
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Universal portfolios with side information
IEEE Transactions on Information 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
| Hi-index | 0.00 |
We price various financial instruments, which are classified as exotic options, using the regret bounds of an online algorithm. In addition, we derive a general result, which upper bounds the price of any derivative whose payoff is a convex function of the final asset price. The market model used is adversarial, making our price bounds robust. Our results extend the work of [9], which used regret minimization to price the standard European call option, and demonstrate the applicability of regret minimization to derivative pricing.