This paper examines movement in implied volatility with the goal of enhancing the methods of options investment in the derivatives market. Indeed, directional movement of implied volatility is forecasted by being modeled into a function of the option Greeks. The function is structured as a locally stationary model that employs a sliding window, which requires proper selection of window width and sliding width. An artificial neural network is employed for implementing and specifying our methodology. Empirical study in the Korean options market not only illustrates how our directional forecasting methodology is constructed but also shows that the methodology could yield a reasonably strong performance. Several interesting technical notes are discussed for directional forecasting.
Bibliographical noteFunding Information:
T. Y. Kim’s work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (MEST) (2009-0065645).
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence