关键词: 股价波动, 特征滞后, 能量性, 波动风险, 门限广义自回归条件异方差模型

Abstract:

Focusing on the issue that the inflection points are hard to forecast in stock price volatility degrades the forecast accuracy, a kind of Lag Risk Degree Threshold Generalized Autoregressive Conditional Heteroscedastic in Mean (LRD-TGARCH-M) model was proposed. Firstly, hysteresis was defined based on the inconsistency phenomenon of stock price volatility and index volatility, and the Lag Degree (LD) calculation model was proposed through the energy volatility of the stock. Then the LD was used to measure the risk, and put into the average share price equation in order to overcome the Threshold Generalized Autoregressive Conditional Heteroscedastic in Mean (TGARCH-M) model's deficiency for predicting inflection points. Then the LD was put into the variance equation according to the drastic volatility near the inflection points, for the purpose of optimizing the change of variance and improving the forecast accuracy. Finally, the volatility forecasting formulas and accuracy analysis of the LRD-TGARCH-M algorithm were given out. The experimental results from Shanghai Stock, show that the forecast accuracy increases by 3.76% compared with the TGARCH-M model and by 3.44% compared with the Exponential Generalized Autoregressive Conditional Heteroscedastic in Mean (EGARCH-M) model, which proves the LRD-TGARCH-M model can degrade the errors in the price volatility forecast.

Key words: price volatility, characteristic hysteresis, energy characteristic, fluctuation risk, Threshold Generalized Autoregressive Conditional Heteroscedastic in Mean (TGARCH-M) model