Proceedings of 37th Conference on Foundations of Computer Science (1996)
Oct. 14, 1996 to Oct. 16, 1996
P. Chalasani , Los Alamos Nat. Lab., NM, USA
S. Jha , Los Alamos Nat. Lab., NM, USA
I. Saias , Los Alamos Nat. Lab., NM, USA
As increasingly large volumes of sophisticated options are traded in world financial markets, determining a "fair" price for these options has become an important and difficult computational problem. Many valuation codes use the binomial pricing model, in which the stock price is driven by a random walk. In this model, the value of an n-period option on a stock is the expected time-discounted value of the future cash flow on an n-period stock price path. Path-dependent options are particularly difficult to value since the future cash flow depends on the entire stock price path rather than on just the final stock price. Currently such options are approximately priced by Monte Carlo methods with error bounds that hold only with high probability and which are reduced by increasing the number of simulation runs. In this paper we show that pricing an arbitrary path-dependent option is #-P hard. We show that certain types of path-dependent options can be valued exactly in polynomial time. Asian options are path-dependent options that are particularly hard to price, and for these we design deterministic polynomial-time approximate algorithms. We show that the value of a perpetual American put option (which can be computed in constant time) is in many cases a good approximation to the value of an otherwise identical n-period American put option. In contrast to Monte Carlo methods, our algorithms have guaranteed error bounds that are polynomially small (and in some cases exponentially small) in the maturity n. For the error analysis we derive large-deviation results for random walks that may be of independent interest.
Monte Carlo methods; approximate option pricing; world financial markets; computational problem; binomial pricing model; stock price; random walk; path-dependent options; #-P hard; polynomial time; deterministic polynomial-time approximate algorithms; perpetual American put option; Monte Carlo methods; error bounds; error analysis; random walks
P. Chalasani, I. Saias and S. Jha, "Approximate option pricing," Proceedings of 37th Conference on Foundations of Computer Science(FOCS), Burlington, VT, 1996, pp. 244.