Volatility forecasting: long memory, regime switching and heteroscedasticity

Ma, Feng; Lu, Xinjie; Yang, Ke; Zhang, Yaojie

doi:10.1080/00036846.2019.1589645

Cited by 12 publications

(4 citation statements)

References 69 publications

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“…Therefore, accurately predicting volatility is a challenging task. Whether investors, academic researchers, or policy makers are concerned about how to improve the prediction accuracy of asset volatility (see, e.g., Nonejad, 2017; Gong & Lin, 2018; Ma et al, 2018; Ma, Liao, et al, 2019; Ma, Lu, et al, 2019; Wei et al, 2019; Vo & Tran, 2020; Lei et al, 2021; Wilms et al, 2021; Zhao et al, 2021; Qiu et al, 2022; Wen et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Forecasting China's stock market volatility with shrinkage method: Can Adaptive Lasso select stronger predictors from numerous predictors?

Liang

Chen

et al. 2022

Int. J Fin Econ

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This paper employs a shrinkage method named Adaptive Lasso (ALasso) to predict the realized volatility (RV) of the China's stock market with numerous predictors. We observed from the out‐of‐sample predictions that the ALasso model exhibits better predictive power than its competitors, implying that the ALasso method can select stronger predictors in the forecasting process than competing models. In addition, the predictability of ALasso method is better in low volatility periods than in high volatility periods. Finally, several robustness check methods, including different forecasting windows, different low and high volatility division, and different volatility measures, supported our results.

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Section: Introductionmentioning

confidence: 99%

Forecasting China's stock market volatility with shrinkage method: Can Adaptive Lasso select stronger predictors from numerous predictors?

Liang

Chen

et al. 2022

Int. J Fin Econ

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“…The various existing literatures suggest that predictive model with Markov regime-switching can significantly improve accuracy of volatility forecasting over models without regime switching (Calvet & Fisher, 2004;Klaassen, 2002;Ma, Liu, Liu, & Wei, 2015;Ma, Lu, Yang, & Zhang, 2019;Ma, Wahab, Huang, & Xu, 2017;Pan, Wang, Wu, & Yin, 2017). Klaassen (2002) constructs MS-GARCH model that contain GARCH and regimeswitching technique to assess the forecasting performance.…”

Section: Introductionmentioning

confidence: 99%

“…In our study, we employ the superior volatility measures, realized volatility (RV), which has been recorded in the numerous existing literature (Bailey & Steeley, 2019;Luo & Ji, 2018;Ma et al, 2017;Ma, Lu, et al, 2019;Ma, Zhang, Wahab, & Lai, 2019;Wang et al, 2012;Y. Wang, Ma, Wei, & Wu, 2016;X.…”

Section: Introductionmentioning

confidence: 99%

Volatility forecasting revisited using Markov‐switching with time‐varying probability transition

Wang

Liang

et al. 2020

Int. J Fin Econ

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This study proposes a novel model, Markov-switching Heterogeneous Autoregressive (MS-HAR) model with jump-driven time-varying transition probabilities (TVTP), to forecast the future volatility in Chinese stock market. The in-sample results show that MS-HAR models are more powerful than HAR-RV-type models; furthermore, the high-volatility regime is short-lived. Moreover, the out-of-sample results indicate that the MS-HAR with TVTP model can achieve a superior forecasting performance and increase the economic value than the competing models including the simple HAR model and the MS-HAR with fixed transition probabilities (FTP) model. The results are robust to several robustness checks including alternative forecast window, alternative evaluation method, alternative predictive model, sub-sample analysis and alternative representative index.

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“…Financial regulatory authorities view stock volatility as one of the key observation variables for avoiding the volatility spillover effect of drastic changes in financial markets. Given this, many scholars offer insight into stock market volatility based on both theoretical and empirical analyses (see, e.g., Andersen et al, 2001;Andrei & Hasler, 2015;Bollerslev et al, 2016;Christiansen et al, 2012;Corsi, 2009;Fleming et al, 2001;French et al, 1987;Ghysels et al, 2006;Liang et al, 2020;Ma et al, 2019;McAlinn et al, 2020;Paye, 2012;Sakata & White, 1998;Schwert, 1989;Wang et al, 2016;Wang et al, 2018;Zhang, Ma, Wang, & Liu, 2019;Wang et al, 2020;Zhang et al, 2020).…”

mentioning

confidence: 99%

Forecasting stock return volatility using a robust regression model

Hao

Zhang

et al. 2021

Journal of Forecasting

Self Cite

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This paper aims to accurately forecast stock return volatility based on a robust regression model. The robust regression model is developed by replacing the mean squared error (MSE) in the autoregressive (AR) model with the Huber loss function, and the resulting model is called the ARH model. The empirical results show that the ARH model displays significantly stronger predictive power than the AR benchmark model for different evaluation periods and forecasting horizons. From an asset allocation perspective, a mean–variance investor can obtain sizeable utility gains based on the volatility forecasts produced by the ARH model. Furthermore, we find that the superior performance of the ARH model comes from assigning small weights for the extreme values, which are mainly found during recessions and periods of high volatility. Finally, our results are robust to various settings.

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Volatility forecasting: long memory, regime switching and heteroscedasticity

Cited by 12 publications

References 69 publications

Forecasting China's stock market volatility with shrinkage method: Can Adaptive Lasso select stronger predictors from numerous predictors?

Forecasting China's stock market volatility with shrinkage method: Can Adaptive Lasso select stronger predictors from numerous predictors?

Volatility forecasting revisited using Markov‐switching with time‐varying probability transition

Forecasting stock return volatility using a robust regression model

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