Forecasting stock return volatility using a robust regression model

He, Mengxi; Hao, Xianfeng; Zhang, Yaojie; Meng, Fanyi

doi:10.1002/for.2779

Cited by 17 publications

(6 citation statements)

References 85 publications

(209 reference statements)

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“…Equity funds are most inclined to invest in the financial industry and adjust the investment ratio of different industries according to the stock market fluctuation. He et al [ 29 ] evaluated the intrinsic stock value of existing capital flows of state-owned banks. They use the cash flow method to make estimates of free cash flow equity and stock value by looking at regulatory capital.…”

Section: Results Analysismentioning

confidence: 99%

The Prediction of Enterprise Stock Change Trend by Deep Neural Network Model

Ma¹,

Chen

Liu

et al. 2022

Computational Intelligence and Neuroscience

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This study aims to accurately predict the changing trend of stocks in stock trading so that company investors can obtain higher returns. In building a financial forecasting model, historical data and learned parameters are used to predict future stock prices. Firstly, the relevant theories of stock forecasting are discussed, and problems in stock forecasting are raised. Secondly, the inadequacies of deep neural network (DNN) models are discussed. A prediction trend model of enterprise stock is established based on long short-term memory (LSTM). The uniqueness and innovation lie in using the stock returns of Bank of China securities in 2022 as the training data set. LSTM prediction models are used to perform error analysis on company data training. The 20-day change trend of the company’s stock returns under different models is predicted and analyzed. The results show that as the number of iterations increases, the loss rate of the LSTM training curve keeps decreasing until 0. The average return price of the LSTM prediction model is 14.01. This figure is closest to the average real return price of 13.89. Through the forecast trend analysis under different models, LSTM predicts that the stock change trend of the enterprise model is closest to the changing trend of the actual earnings price. The prediction accuracy is better than other prediction models. In addition, this study explores the characteristics of high noise and complexity of corporate stock time series, designs a DNN prediction model, and verifies the feasibility of the LSTM model to predict corporate stock changes with high accuracy.

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Section: Results Analysismentioning

confidence: 99%

The Prediction of Enterprise Stock Change Trend by Deep Neural Network Model

Ma¹,

Chen

Liu

et al. 2022

Computational Intelligence and Neuroscience

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“…Recent literature tries to improve out-of-sample performance by adjusting measurement errors with various methods, such as inserting an interaction term between the lagged realized variance and the realized quantity (RQ), which is termed the HARQ model (Bollerslev et al, 2016) and adding a generalized autoregressive conditional heteroskedasticity (GARCH) correction within an asymmetric extension of the HAR class (Cipollini et al, 2021). In addition, He et al (2021) accurately forecast stock return volatility based on a robust regression model by considering the impact of outliers. D. Wen et al (2022) propose a new family of the HAR-RV models by considering truncated methods for predicting the RV in China's stock market.…”

Section: Improvement Of Prediction Models Considering Outliersmentioning

confidence: 99%

“…In addition, He et al. (2021) accurately forecast stock return volatility based on a robust regression model by considering the impact of outliers. D. Wen et al.…”

Section: Relevant Literature and Our Contributionmentioning

confidence: 99%

Predicting stock realized variance based on an asymmetric robust regression approach

Zhang

Zhao

et al. 2023

Bulletin of Econ Res

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This paper introduces an asymmetric robust weighted least squares (ARLS) approach to improve the forecasting performance of the heterogeneous autoregressive model for realized volatility. The ARLS approach down‐weights extreme observations to limit the bad influence of outliers on the estimated parameters. Compared with existing robust regression methods, our model further takes into account the asymmetry of outliers using a class of kernel functions. Out‐of‐sample results show the ARLS approach can generate more accurate forecasts of the S&P 500 index realized volatility in the statistical and economic senses. The model that considers the asymmetry of outliers gains superior performance among various robust regression competitors. The forecasting improvements also hold in other international stock markets. More importantly, the source of the predictive ability of the ARLS model comes from the less biased and more efficient parameter estimation.

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“…As each has advantages of its own, different papers have selected either of the two windows. For instance, the finance literature tends to construct forecasts using a rolling window (see, e.g., Bollerslev et al, 2016;Degiannakis & Filis, 2017;Ma et al, 2019;Zhang et al, 2020;He et al, 2021). Whereas the macroeconomics literature generally uses an expanding window for estimating parameters (see, e.g., Stock & Watson, 2003;Stock & Watson, 2007;Schrimpf & Qingwei, 2010;Gillitzer & McCarthy, 2019).…”

Section: Introductionmentioning

confidence: 99%

Out‐of‐sample volatility prediction: Rolling window, expanding window, or both?

Feng,

Zhang,

Wang

2023

Journal of Forecasting

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Estimation windows, either rolling or expanding, are used for volatility forecasting. In this study, we propose a new approach relying on both estimation windows. Our method is based on how well these two windows performed in terms of prediction during a recent period of past time. We will continue to use whichever one has performed better in the past. Results show that our strategy significantly outperforms the individual and mean combination models. Whether the window is rolling or expanding, the relatively better performance is persistent. In other words, we document the existence of the momentum of predictability (MoP). A mean–variance investor can achieve the highest utility gains using our strategy for volatility forecasting. Moreover, the results pass a series of robustness tests.

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Forecasting stock return volatility using a robust regression model

Cited by 17 publications

References 85 publications

The Prediction of Enterprise Stock Change Trend by Deep Neural Network Model

The Prediction of Enterprise Stock Change Trend by Deep Neural Network Model

Predicting stock realized variance based on an asymmetric robust regression approach

Out‐of‐sample volatility prediction: Rolling window, expanding window, or both?

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