学术报告报告题目：Modelling the Long-term Component of the Volatility in China’s Stock and Bond Market with Macro Factors: Based on GARCH-MIDAS
报 告 人：陈倩 博士（北京大学深圳研究生院）
报告时间：2018年12月9日(星期日)上午10：00-11：00
报告地点：4号楼318室
邀 请 人：李兵 教授
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数学学院
2018年12月7日
报告摘要：
This paper applies the GARCH-MIDAS-X models to China’s stock and bond market in attempt to examine the power of low-frequency macro factors in predicting high-frequency market volatility. The results confirm the significant relationship between the macro variables and the long run volatility. Specifically, the long-run component of GARCH-MIDAS model incorporatingindustrial added value growth rate (IP) accounts for around 30% of total conditional volatility of China’s stock market and bond market. The study also findsthat, industrial added value is a better predictor than theproducer price index, which may be due to the fact that the China’s economy is still in the developmentstage and the market is more sensitive to economic growth than inflation. The out-of-sample forecasts of GARCH-MIDAS-X models improve with longer horizons. Though for the stock market, GARCH-MIDAS-RV stillperforms best in semi-annual horizon; for bond, GARCH-MIDAS with IP volatility outperformsother models in semi-annual horizon. DCC-MIDAS-X is also applied to study the relationship between the macro factors and the stock–bond correlation. The results suggest a weaker effect of the macro factors, which may be due to theabsence of inter-market macro-strategy investors in China.
报告人简介：
陈倩博士，北京大学汇丰商学院助理教授，2011年于澳洲悉尼大学商学院运筹及计量学专业取得博士学位，2012年开始在北京大学深圳研究生院从事科研教学工作。陈倩博士具有深厚的统计和计量理论功底，专长用贝叶斯理论及方法研究金融市场计量模型及其在风险预测，信息传递等领域的应用。具体涉及时间序列模型，风险控制模型，波动率预测，数据的不对称特性，混合频率模型等。结合中国这一新兴金融市场的特性，将金融计量经济学以及数据分析领域的最前沿的理论，方法和模型结合到实际应用中。其研究结果为投资者，金融风险管理者，金融市场监管等角度做出有效而良性的决策提供有意义的参考。其论文发表于《International Journal of Forecasting》，《Computational Statistics & Data Analysis》，《Emerging Market,Finance and Trade》及《Quantitative Finance》。