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On the Spectral Density of Fractional Ornstein-Uhlenbeck Processes

作者: 发布时间:2024-09-23 点击数:
主讲人:Jun Yu
主讲人简介:
Professor Jun Yu received a Ph.D. in economics at the University of Western Ontario in 1998. He taught at the Business School of the University of Auckland between 1998 and 2003 and Singapore Management University (SMU) between 2004 and 2023. He is currently UMDF chair Professor of Finance and Economics at the University of Macau and Dean of the Faculty of Business Administration at the University of Macau. Before that, he was Lee Kong Chian Professor of Economics and Finance at SMU, director of Sim Kee Boon Institute for Financial Economics at SMU, and the lead principal investigator of the Centre for Research on the Economics of Ageing at SMU. He was a Changjiang Scholar (长江学者) between 2017 and 2019.
 
Professor Yu has published about 100 papers. Many of these publications are in leading journals in finance and economics. His articles for detecting the presence of asset price bubbles and estimating their origination and termination dates have initiated a new area of research on the econometric analysis of bubbles in financial assets and real estate. Many researchers have been attracted to work in this area using the methods developed in these articles. Many central banks have used these techniques for early warning signals. Several computer software packages have been written to implement these methods. In 2020, his co-authored textbook, titled “Financial Econometric Modeling”, was published by Oxford University Press. In honor of Professor Yu, an edited book titled “Financial Econometrics: Theory and Application” is forthcoming at the Cambridge University Press.
 
Professor Yu is an inaugural fellow of the Society of Financial Econometrics and a fellow of the Journal of Econometrics. He serves as Associate Editor of the Journal of Econometrics and Econometric Theory.
主持人:Xiaoyi Han
讲座简介:

This paper introduces a novel and easy-to-implement method for accurately approximating the spectral density of discretely sampled fractional Ornstein-Uhlenbeck (fOU) processes. The method offers a substantial reduction in approximation error, particularly within the rough region of the fractional parameter $H\in(0,0.5)$. This approximate spectral density has the potential to enhance the performance of estimation methods and hypothesis testing that make use of spectral densities. We introduce the approximate Whittle maximum likelihood (AWML) method for discretely sampled fOU processes, utilising the approximate spectral density, and demonstrate that the AWML estimator exhibits properties of consistency and asymptotic normality when $H\in(0,1)$, akin to the conventional Whittle maximum likelihood method. Through extensive simulation studies, we show that AWML outperforms existing methods in terms of estimation accuracy in finite samples. We then apply the AWML method to the trading volume of 40 financial assets. Our empirical findings reveal that the estimated Hurst parameters for these assets fall within the range of 0.10 to 0.21, indicating a rough dynamic.

时间:2024-10-11 (Friday) 16:40-18:00
地点:Room N302, Economics Building
讲座语言:English
主办单位:威尼斯37266、王亚南经济研究院、邹至庄经济研究院
承办单位:
期数:南强学术讲座1290讲
联系人信息:许老师,电话:2182991,邮箱:ysxu@xmu.edu.cn
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