CAJ | 학술논문

通过用久期来调整收益率，把非等距数据等距化，构建ACD-GARCH模型来反映高频波动特征．并添加微观结构变量，构建了ACD-GARCH-M模型，用以分析久期、交易量与收益率和波动率的关系．结果表明：较长的久期是由于信息缺乏所致，久期对收益率的影响不显著，但久期和价格的波动性负相关．交易量和价格的波动性正相关．在加入了微观解释变量的ACD-GARCH-M模型中，GARCH效应大大减弱了，说明ACD-GARCH-M模型能较好地反映高频波动聚集性的本质，久期、交易量是产生波动聚集的原因．
통과용구기래조정수익솔，파비등거수거등거화，구건ACD-GARCH모형래반영고빈파동특정．병첨가미관결구변량，구건료ACD-GARCH-M모형，용이분석구기、교역량여수익솔화파동솔적관계．결과표명：교장적구기시유우신식결핍소치，구기대수익솔적영향불현저，단구기화개격적파동성부상관．교역량화개격적파동성정상관．재가입료미관해석변량적ACD-GARCH-M모형중，GARCH효응대대감약료，설명ACD-GARCH-M모형능교호지반영고빈파동취집성적본질，구기、교역량시산생파동취집적원인．
Before the building of ACD-GRACH model to research the high frequency volatility, it adjusted the yield by duration to equal the distance of data. It added micro-structural variables to build the ACD-GARCH-M models and analyze the relationship among duration, volume, yield and volatility. The results reveal that long duration results from the lack of information, and there are insignificant impacts on yield from duration; negative relationship between duration and price volatility; and positive relationship between volume and price volatility. ACD-GARCH-M model which contains explanatory variables demonstrate less GARCH effects implies that it could describe the volatility clustering of high frequency data better, and duration and volume can explain the volatility clustering to a large extent.