Applying option Greeks to directional forecasting of implied volatility in the options market: An intelligent approach

Authors:
Jae Joon Ahn;Dong Ha Kim;Kyong Joo Oh;Tae Yoon Kim
Affiliations:
Department of Information and Industrial Engineering, Yonsei University, 134 Shinchon-Dong, Seodaemun-Gu, Seoul 120-749, South Korea;Department of Information and Industrial Engineering, Yonsei University, 134 Shinchon-Dong, Seodaemun-Gu, Seoul 120-749, South Korea;Department of Information and Industrial Engineering, Yonsei University, 134 Shinchon-Dong, Seodaemun-Gu, Seoul 120-749, South Korea;Department of Statistics, Keimyung University, Daegu 704-701, South Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 3
Cited 0

An early warning system for global institutional investors at emerging stock markets based on machine learning forecasting

Expert Systems with Applications: An International Journal
Intelligent forecasting for financial time series subject to structural changes

Intelligent Data Analysis
Usefulness of artificial neural networks for early warning system of economic crisis

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

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.