The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Deep learning forecasting for financial realized volatilities with aid of implied volatilities and internet search volumes

금융 실현변동성을 위한 내재변동성과 인터넷 검색량을 활용한 딥러닝

  • Shin, Jiwon (Institute of Mathematical Sciences, Ewha Womans University) ;
  • Shin, Dong Wan (Department of Statistics, Ewha Womans University)
  • 신지원 (이화여자대학교 수리과학연구소) ;
  • 신동완 (이화여자대학교 통계학과)
  • Received : 2021.09.07
  • Accepted : 2021.10.05
  • Published : 2022.02.28
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Abstract

In forecasting realized volatility of the major US stock price indexes (S&P 500, Russell 2000, DJIA, Nasdaq 100), internet search volume reflecting investor's interests and implied volatility are used to improve forecast via a deep learning method of the LSTM. The LSTM method combined with search volume index produces better forecasts than existing standard methods of the vector autoregressive (VAR) and the vector error correction (VEC) models. It also beats the recently proposed vector error correction heterogeneous autoregressive (VECHAR) model which takes advantage of the cointegration relation between realized volatility and implied volatility.

S&P 500과 RUSSELL 2000, DJIA, Nasdaq 100 4가지 미국 주가지수의 실현변동성(realized volatility, RV)을 예측하는데 있어서 사람들의 관심 지표로 삼을 수 있는 인터넷 검색량(search volume, SV) 지수와 내재변동성(implied volatility, IV)를 이용하여 LSTM 딥러닝(deep learning) 방법으로 RV의 예측력을 높이고자하였다. SV을 이용한 LSTM 방법의 실현변동성 예측력이 기존의 기본적인 vector autoregressive (VAR) 모형, vector error correction (VEC)보다 우수하였다. 또한, 최근 제안된 RV와 IV의 공적분 관계를 이용한 vector error correction heterogeneous autoregressive (VECHAR) 모형보다도 전반적으로 예측력이 더 높음을 확인하였다.

Keywords

Acknowledgement

이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임 (No. 2020R1F1A1A01051039).

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