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http://dx.doi.org/10.5351/KJAS.2022.35.1.093

Deep learning forecasting for financial realized volatilities with aid of implied volatilities and internet search volumes  

Shin, Jiwon (Institute of Mathematical Sciences, Ewha Womans University)
Shin, Dong Wan (Department of Statistics, Ewha Womans University)
Publication Information
The Korean Journal of Applied Statistics / v.35, no.1, 2022 , pp. 93-104 More about this Journal
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.
Keywords
deep learning; LSTM; volatility forecasting; search volume index; vector error correction model;
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Times Cited By KSCI : 3  (Citation Analysis)
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