Genetic AlgorithmBased Bi-directional Generative Adversary Network for LIBOR Prediction

Abstract

LIBOR (London Inter Banking Offered Rate) is one of the most important indicators of global currency liquidity risk. LIBOR market, involving top 18 member banks (including HSBC, Citibank, Bank of Tokyo-Mitsubishi UFJ, Credit Suisse etc.) and thousands non-member banks crossing different continents, is the huge market for banks to keep liquidity and currency flow globally. Because LIBOR is so important, decided by so many huge banks together and impacted by both current demand and supply of monetary currency and the forecast of future market, therefore the prediction is quite challenging. This paper is to introduce genetic algorithm (“GA”) based bi-directional generative adversary network (“BiGAN”) to predict the LIBOR in USD. Both the pro and cons of the algorithm will be discussed, with fitness values and Mean Squared Error (“MSE”). 50 test cases are executed randomly to verify the performance of the predictions. The target variance between predication and actual value is no more than 0.015.