A Vector Heterogeneous Autoregressive Index Model for Realized Volatility Measures

Abstract

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Is implied volatility more informative for forecasting realized volatility: An international perspective

“…We use the state‐of‐the‐art heterogeneous autoregressive (HAR) framework proposed by Corsi (2009), which includes information on the volatility of the previous day, week, and month, and can thus accommodate the heterogeneous beliefs of traders in the stock market. Numerous studies use the HAR model and its extensions to predict the RV due to its advantages and success in volatility forecasting (see, e.g., Corsi, 2009; Buncic & Gisler, 2016; Cubadda, Guardabascio, & Hecq, 2017; Zhang, Ma, et al, 2019). We use various forecasting models to compare the effectiveness of two types of information flow, constructed by RV and IV, respectively, in predicting international stock market volatility.…”

Which popular predictor is more useful to forecast international stock markets during the coronavirus pandemic: VIX vs EPU?

“…In our study, we employ the superior volatility measures to forecasts realized volatility (RV), which has been recorded in the numerous literature ( Fang, Wang, Liu, & Song, 2018 ; Liu, Ma, & Zhang, 2019 ; Liu & Zhang, 2015 ; Ma, Wei, Liu, & Huang, 2018 ; Peng, Chen, Mei, & Diao, 2018 ; Pu, Chen, & Ma, 2016 ; Wang, Wei, Wu, & Yin, 2018 ; Wei, Wang, & Huang, 2010 ; Wen, Gong, & Cai, 2016 ; Wen, Zhao, Zhang, & Hu, 2019 ). Subsequently, we utilize the heterogeneous autoregressive (HAR) model of Corsi (2009) , which has become the workhorse of forecasting models for stock volatility due to its simple linear regression techniques and consistently superior forecasting performance ( Buncic & Gisler, 2016 ; Cubadda, Guardabascio, & Hecq, 2017 ; Liang et al, 2020 ; Liu et al, 2019 ; Ma et al, 2018 ; Pu et al, 2016 ; Qiu, Zhang, Xie, & Zhao, 2019 ; Wang, Ma, Wei, & Wu, 2016 ; Wen et al, 2016 ; Wen et al, 2019 ). More specifically, Aganin (2017) compare the GARCH, ARFIMA and HAR-RV models and the results show that HAR-RV model has superior performance than GARCH and ARFIMA models; moreover, Vortelinos (2017) compares the forecasting performance of nonlinear models (Principal Components Combining, neural networks and GARCH) and HAR-RV model, the result indicate the simple HAR model is the most accurate for seven US financial markets (spot equity, spot foreign exchange rates, exchange traded funds, equity index futures, US Treasury bonds futures, energy futures, and commodities options).…”

Latent state recognition by an enhanced hidden Markov model

“…Brown and Warner (1985) also recognize the auto-correlation in daily returns and further indicate the change of the covariance due to the market event. Consequently, modelling the stock price return by an autoregressive process has been confirmed by theoretical and empirical studies such as the work of Engle (1982); Nelson (1991) and the vector autoregressive model for the multi-variable financial return in recent studies as Cubadda et al (2017); Kalli and Griffin (2018); Billio et al (2019).…”