{'fraction_non_alphanumeric': 0.02074688796680498, 'fraction_numerical': 0.0, 'mean_word_length': 5.703703703703703, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Dynamic Hedging Strategy for Option Transaction Using Artificial Neural Networks

In order to avoid the risk caused by continuously changing option value, option issuers generally utilize the traditional Dynamic Delta Hedging (DDH) method. DDH tries to maintain risk-neutral position by adjusting hedge position according to the delta by BlackScholes (BS) model. DDH, however, is not able to guarantee optimal hedging performance due to some impractical assumptions inherent in BS model. Therefore, this study presents a methodology for dynamic option hedging strategy using artificial neural network (ANN) to enhance hedging performance and shows the superiority of the proposed method through computational experiments.