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主题:Dynamic Risk Preference and Tail Risk Forecast with a Regime-Switching Generalized Autoregressive Conditional Skewness Model                           

(条件偏度的区制转换模型视角下动态风险偏好与重尾风险预测)

主讲:陈倩深圳技术大学副教授

主持:高翔

时间:2020年03月27日(周五)下午14:10 --- 15:30

地点:钉钉群直播,添加钉钉号gapt418入群

内容提要:

This paper proposes a two-regime GARCH type skewness model with asymmetric Laplace conditional return distribution to directly model the time-varying and persistent features in the conditional skewness of financial return series. It isolates the dynamics in the third moment (skewness) from that in the second moment (variance) of financial return series. The model is realized with six real stock index returns and five commodity and FX asset returns. Significant short-term and long-term effect of innovation on the conditional skewness has been detected. Conditional skewness also demonstrates very different behavior from volatility---either "clustering'' or "mean (zero)-reverting'' persistence. And generally, the persistence of skewness is less consistent across different assets, especially during downside market. The proposed models are able to provide sufficient risk coverage by effectively forecasting VaR and ES, compared to a number of competing models.

本文基于GARCH框架提出了一种新型非对称、两区制模型,可以用于直接处理金融资产回报这一时间序列数据中所存在的分布偏度时变和惯性特点。本模型的优点在于可以将分布中偏度与方差的不同动态特征分离出来。在将其应用于六种股指和五种大宗商品与外汇指数的数据后,作者发现:条件偏度不仅包含了短期和长期的新定价信息,也展现出了与方差完全不同的特性。

演讲者简介:

陈倩,深圳技术大学副教授,高级认证预测师(ACPF),悉尼大学经济学博士。曾任北京大学汇丰商学院助教授,有丰富的研究生和MBA项目教学经验。陈博士从事统计模型、数据分析和动态信息研究工作数十年,开发的模型被业界广泛用于金融风险管理、金融机构资本监管和商业预测。

主办单位:上海商学院财金研究所

承办单位:上海商学院财金研究所

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