Forecasting volatility with outliers in Realized GARCH models

Abstract

The Realized generalized autoregressive conditional heteroskedasticity (GARCH) model proposed by Hansen is often applied to forecast volatility in high-frequency financial data. It is frequently found, however, that the distribution of the estimated residuals from Realized GARCH models has peak fat-tail characteristics. Considering this feature may be a result of neglected additive outliers (AOs) and innovative outliers (IOs), this paper proposes the Realized GARCH model with additive outlier and innovative outlier (Realized GARCH-AI model) for forecasting volatility. This model can detect and correct abnormal returns and realized volatility by estimating the coefficients of volatility models and calculating the outlier test statistics. In the process of simulation, this paper considers different outlier cases in the GARCH model and the Realized GARCH model, and evaluates the performance of the proposed procedure through the accuracy of parameter estimation under different critical values. We find that the critical values will affect the results of outlier detection and correction. When the value is in a suitable range, the proposed procedure based on high-frequency data can obtain unbiased parameter estimation, and the estimation result is close to those of the intervention model containing outlier information. Finally, we use the MCS test proposed by Hansen et al. (Econometrica, 2011, 79(2), 453-497) to study the volatility prediction accuracy, value at risk, and expected shortfall prediction ability of the new model. The empirical analysis demonstrates that the proposed model produces better prediction effects than the traditional Realized GARCH model.

Risk correlation identification of futures market based on wavelet transform and quantile Granger causality test

“…This method can synthesize the information of multiple models, but it also needs to determine the appropriate model set, model weight, model selection criteria, etc. Cai [23] used dynamic model averaging method to establish a volatility prediction model based on dynamic quantile regression. Xiong Tao and Bao Yukun [24] also used dynamic model averaging method to predict soybean futures price, and found that this method can effectively improve the accuracy and robustness of soybean futures price prediction.…”

Modelling returns volatility: mixed-frequency model based on momentum of predictability

“…Forecasting volatility under the assumption of normal distribution may lead to underestimation or overestimation of actual market volatility. Numerous studies have shown that asymmetric fat-tailed distribution can improve the forecasting effects of volatility (see, e.g., Tian & Hamori, 2015;Wu et al, 2020;Cai et al, 2021). Therefore, we also consider the asymmetric and fat-tailed characteristics of financial returns and introduce skewed-t distribution into the MF-MoP model to better describe the characteristics of volatility.…”

Building a Sustainable GARCH Model to Forecast Rubber Price: Modified Huber Weighting Function Approach

“…Although the GARCH model is very general, there are serious challenges, especially when there are outliers. Previous studies have found that outliers can have detrimental effects on parameter estimate [10][11][12] , identification and estimation 13,14 and forecasting 13,15 . Therefore, robust methods are more preferred by researchers to reduce the influence of outliers.…”