特征滞后计算的股市波动预测  

Stock market volatility forecast based on calculation of characteristic hysteresis

作　　者：姚宏亮[1] 李大光[1] 李俊照[1] 

机构地区：[1]合肥工业大学计算机与信息学院,合肥230009

出　　处：《计算机应用》2015年第7期2077-2082,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61175051;61070131;61175033)

摘　　要：针对股票价格波动拐点难以有效预测导致预测精度不高的问题,提出一种特征滞后程度计算的均值门限广义自回归条件异方差(LRD-TGARCH-M)模型。首先,基于股价波动与指标变化出现的不一致性,给出了滞后性的定义,并引入能量波动概念,从能量角度提出特征滞后程度(LD)计算模型;然后,用LD度量拐点出现之前的风险大小,将其加入到股价均值方程中,克服均值门限广义自回归条件异方差(TGARCH-M)模型对拐点预测的不足;其次,根据拐点附近波动相对剧烈,将LD加入到误差项的方差方程中,优化方差的变化,提高模型的预测精度;最后,给出了LRD-TGARCH-M模型的波动预测公式和精度分析,并在股票数据上进行实验,结果表明,与TGARCH-M模型相比,精确度提高了3.76%;与均值指数GARCH(EGARCH-M)模型相比,精确度提高了3.44%,证明了LRD-TGARCHM模型可以提高股价走势预测精度,减小误差。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.

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 

分 类 号：TP399[自动化与计算机技术—计算机应用技术] TP182[自动化与计算机技术—计算机科学与技术]