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讲座预告 | 基于阈值增强的异构自回归跳跃模型的能源期货波动预测

中山大学管理学院  · 公众号  ·  · 2019-06-06 19:01

正文

讲座题目

Forecasting Energy Futures Volatility with Threshold Augmented Heterogeneous Autoregressive Jump Models(基于阈值增强的异构自回归跳跃模型的能源期货波动预测)


主讲嘉宾

Fredj JAWADI教授  法国里尔大学


讲座时间

2019年6月13日10:00-11:30


讲座地点

管理学院善衡堂S221


主 持 人

韦立坚副教授


主办单位

中山大学管理学院

讲座语言

英语



嘉宾简介


Professor Fredj Jawadi has a PhD and a HDR (Habilitation to Supervise Research) in Economics. He is a Professor of Financial Economics at the University of Lille since September 2018. He is also an Associate Researcher at EconomiX, and a Deputy Director for CAC in France. In addition, Professor Jawadi is a Research Fellow at the Economic Research Forum (ERF), a Fellow of the Society for Economic Measurement since 2018, a Charter Fellow at the Institute for Nonlinear Dynamical Inference (INDI) in Russia since 2015. He has been a Visiting Scholar at the Emory University, University of Chicago, University of Kansas, University of Liverpool, University of Ljubljana, University of Greenwich, University of Shanghai, and University of Gent since 2009. He is a financial economist and econometrician with a special interest in financial markets and applied macroeconomics in the developed, emerging and developing countries. He has worked for Natixis as a consultant in charge of developing financial indicators. He has also worked at the Leader of Consortium CHARE (ADE, Particip, ECDPM, GIZ, MDF and PEM consult) and the European Commission, for the “Methodological and Knowledge Sharing support” Programme as an expert in charge of i) explaining “the Macroeconomic Risk for developing countries” and ii) analyzing the IMF reports for the National Delegations. He has also served as an expert for the French National Research Agency (ANR) and for several international Universities. He has published many papers and books on applied finance, econometrics and macroeconomics. He has won different Prizes and Grants.


讲座简介


This study forecasts the volatility of two energy futures markets (oil and gas), using high-frequency data. We, first, disentangle volatility into continuous volatility and jumps. Second, we apply wavelet analysis to study the relationship between volume and the volatility measures for different horizons. Third, we augment the heterogeneous autoregressive (HAR) model of Corsi (2009) by nonlinearly including both jumps and volume. We then propose different empirical extensions of the HAR model. Our study shows that oil and gas volatilities nonlinearly depend on public information (jumps), private information (continuous volatility), and trading volume. Moreover, our threshold augmented HAR model with heterogeneous jumps and continuous volatility outperforms HAR model in forecasting volatility.


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