A Fractional Integration Analysis on Daily FX Implied Volatility: Long Memory Feature and Structural Changes

• 발행기관: 강원대학교 경영경제연구소
• 저자명: Young-Wook Han
• 간행물 정보: 『아태비즈니스연구』제13권 제2호, 23~37쪽, 전체 15쪽
• 주제분류: 인문학 > 문학
• 파일형태: PDF
• 발행일자: 2022.06.30

영문 초록

Purpose - The purpose of this paper is to analyze the dynamic factors of the daily FX implied volatility based on the fractional integration methods focusing on long memory feature and structural changes. Design/methodology/approach - This paper uses the daily FX implied volatility data of the EUR-USD and the JPY-USD exchange rates. For the fractional integration analysis, this paper first applies the basic ARFIMA-FIGARCH model and the Local Whittle method to explore the long memory feature in the implied volatility series. Then, this paper employs the Adaptive-ARFIMA-Adaptive-FIGARCH model with a flexible Fourier form to allow for the structural changes with the long memory feature in the implied volatility series. Findings - This paper finds statistical evidence of the long memory feature in the first two moments of the implied volatility series. And, this paper shows that the structural changes appear to be an important factor and that neglecting the structural changes may lead to an upward bias in the long memory feature of the implied volatility series. Research implications or Originality - The implied volatility has widely been believed to be the market’s best forecast regarding the future volatility in FX markets, and modeling the evolution of the implied volatility is quite important as it has clear implications for the behavior of the exchange rates in FX markets. The Adaptive-ARFIMA-Adaptive-FIGARCH model could be an excellent description for the FX implied volatility series

목차

Ⅰ. Introduction

Ⅱ. Long Memory Feature in FX Implied Volatility

Ⅲ. Structural Changes in FX Implied Volatility

Ⅳ. Conclusion

References

키워드

Adaptive-ARFIMA-Adaptive-FIGARCH Model ARFIMA-FIGARCH Model FX Implied Volatility Local Whittle Method Long Memory Feature Structural Changes.