Density estimation through quasi-analytic Monte-Carlo simulation: Options arbitrage with transactions costs

Discretely rebalanced options arbitrage strategies in the presence of transaction costs have path dependent returns that are difficult to model analytically. I instead use a quasi-analytic procedure that combines the computational efficiency of analytical solutions with the flexibility of simulations. The central feature is the estimation of the distribution of returns of the arbitrage strategy by mapping simulated returns percentiles and the input parameter set. Using the estimated density, I evaluate the tradeoff between transaction costs and risk exposure under generalized transaction costs structures that includes bid-ask spread and brokerage commission. I show that the optimal strategy depends on transaction costs, volatility, and option moneyness. Strategies such as rebalancing when the hedge ratio changes by 0.25, balances transaction costs and risk exposure, and can be optimal. Copyright Springer Science+Business Media, LLC 2007