Lower Precision calculation for option pricing

The problem of option pricing is one of the most critical issues and fundamental building blocks in mathematical finance. The research includes the deployment of a lower-precision type in two option-pricing algorithms: the Black-Scholes and Monte Carlo simulations. We make an assumption that the shorter the number used for calculations is (in bits), the more operations we are able to perform at the same time. The results are examined by a comparison to the outputs of singleand double-precision types. The major goal of the study is to indicate whether the lower-precision types can be used in financial mathematics. The findings indicate that Black-Scholes provided more precise outputs than the basic implementation of the Monte Carlo simulation. Modification of the Monte Carlo algorithm is also proposed. The research shows the limitations and opportunities of the lower-precision type usage. In order to benefit from the application in terms of the time of calculation, the improved algorithms can be implemented on a GPU or FPGA. We conclude that, under particular restrictions, the lower-precision calculation can be used in mathematical finance.