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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
| AI in Digital Finance — Online Seminars | When Does a Curve Really Change? |
Published:
This post summarizes an online seminar from the AI in Digital Finance — Online Seminars series on how to detect economically meaningful shifts in functional time series, rather than changes that are only statistically significant.
Development of the Digital Financial Competitiveness Evaluation Index for Small and Medium Commercial Banks
Published:
This post introduces the Digital Financial Competitiveness Evaluation Index for Small and Medium Commercial Banks, a project completed in August 2024 after four years of research and data analysis.
portfolio
Self-Normalized Inference for Relevance-Based Functional Granger Causality
Revise and resubmit at the Journal of Financial Econometrics. This working paper develops tuning-free, self-normalized tests for economically relevant functional Granger causality in Hilbert-space time series, with an application to volatility spillovers between Bitcoin options and spot markets.
Monitoring Relevant Shifts in Functional Time Series
This working paper develops adjusted-range self-normalized inference for relevant change-point detection in weakly dependent functional time series, delivering tuning-free procedures and an empirical application to Bitcoin options implied-volatility smiles.
Online Monitoring of Loading-Space Instability in Large Factor Models via Self-Normalized DFT Spectral Scores
This working paper develops tuning-lean, self-normalized sequential monitors based on discrete Fourier transform (DFT) spectral scores to detect factor-loading instability in large approximate factor models, delivering stable false-alarm control and faster detection of structural change.
Online Monitoring of Distributional Granger Causality
This working paper develops a unified framework for online monitoring of distributional Granger causality using self-normalized score detectors and anytime-valid quantile e-processes, with an empirical application to BTC downside-risk monitoring using Deribit option signals.
Mathematical Sciences for Trustworthy AI Corpora
This working paper develops a mathematical-sciences framework for trustworthy AI corpora, organizing corpus trustworthiness around representativeness, consistency, fairness, privacy, and robustness across the lifecycle of collection, cleaning, augmentation, and auditing.
GLMY Path Homology Reveals Structural Reorganization in Cryptocurrency Options Markets
This working paper applies GLMY path homology to transaction-level Bitcoin and Ethereum options data, measuring higher-order directed topology in implied-volatility spillovers and signed trading-pressure networks.
When to Refresh a Functional Representation: Online Monitoring of Structural Change in Functional Time Series
This working paper studies online model-maintenance rules for functional representations, comparing HAC, Shao-type self-normalization, and adjusted-range self-normalization after training-anchored FPCA compression.
Using Machine Learning for Prediction and Policy Analysis in Economics
This working paper reviews how machine learning contributes to economics across prediction, measurement, causal inference, policy learning, structural computation, finance, and operational deployment.
Testing for Omitted Variables in the Conditional Variance Function in a Heteroscedastic Regression Model
This working paper develops a consistent nonparametric test for whether a subset of conditioning variables can be omitted from the conditional variance function in a heteroscedastic regression model, with asymptotic theory, Monte Carlo evidence, and a Canadian interest-rate illustration.
Membership Rules and the Pricing of Local Public Goods: Evidence from China’s Hukou System
This paper models Hukou as a residency-contingent membership rule that prices access to local public goods, and uses an original survey from Beijing, Shanghai, and Guangzhou to quantify impacts on spending, subsidies, and satisfaction.
Megaregion Enlargement and the Geography of Gains: Evidence from the Yangtze River Delta
Using city-specific synthetic controls for 99 prefectures (2004–2017), this paper studies the 2013 Yangtze River Delta enlargement and documents a ‘Winner-Takes-More’ pattern with limited cross-boundary spillovers.
Online Monitoring of Structural Change with Adjusted-Range Self-Normalization
This working paper proposes an adjusted-range-based self-normalized sequential monitoring scheme (RSMS) for online change-point detection, offering significant improvements in performance, especially in the presence of structural shifts. The method avoids tuning parameter choices, making it more robust and efficient for high-frequency data.
publications
A bootstrap test for comparing two variances: simulation of size and power in small samples
Published in Journal of Biopharmaceutical Statistics, 2011
This paper develops a bootstrap test for comparing two variances, focusing on its simulation of size and power in small samples.
Recommended citation: Sun, J., Chernick, M. R., & LaBudde, R. A. (2011). "A bootstrap test for comparing two variances: simulation of size and power in small samples." Journal of Biopharmaceutical Statistics, 21(6), 1079-1093.
Publisher / DOI page | BibTeX
An information diffusion-based model of oil futures price
Published in Energy Economics, 2013
This paper proposes an information diffusion-based model for predicting oil futures prices, addressing the dynamics of information spread in financial markets.
Recommended citation: Li, Z., Sun, J., & Wang, S. (2013). "An information diffusion-based model of oil futures price." Energy Economics, 36, 518-525.
Publisher / DOI page | BibTeX
Mechanisms and performance of Chinese bear markets and policy suggestions
Published in Palgrave Macmillan, London, UK, 2015
In this chapter, which concerned the 2006–2007 bull market, we analyzed and discussed a number of features related to bull markets in China. Specifically, their formation mechanism, performance characteristics, relevant empirical observations related to index design, as well as their terminal signals. This chapter is structured as follows. In the first section, we will detect the bearish/bullish phases of Chinese stock markets and provide some background information and historical facts. In the second section, we will analyze the ‘risk-return’ trade-off relationships in Chinese stock markets with a focus on the differences of these relationship between bearish and bullish phases. In the third section, we will derive an asset pricing model based on information diffusion; and establish a tri-variate trade-off relationship in terms of amplitude, duration and volatility persistence.1 Finally, we will analyze some institutional problems of Chinese stock markets that have emerged during their adjustment phases.
Recommended citation: Li, Z., Sun, J., & Cole, M. (2015). Mechanisms and performance of Chinese bear markets and policy suggestions. In Li, Z., & Cheng, S. (Eds.), The Chinese Stock Market Volume II (pp. 125–189). Palgrave Macmillan.
Mechanisms and performance of Chinese bear markets and policy suggestions
Published in Science Press, Beijing, China, 2015
This book chapter examines Chinese bear market episodes and their underlying mechanisms, and provides policy-oriented suggestions based on China’s market environment. The discussion connects market behavior with institutional settings and regulatory context, offering a structured reference for understanding downside market dynamics.
Recommended citation: Li, Z., & Sun, J. (2015). Mechanisms and performance of Chinese bear markets and policy suggestions. In Li, Z., & Cheng, S. (Eds.), China's Stock Market Review and Outlook: 2002–2013. Science Press.
An RKHS-based approach to double-penalized regression in high-dimensional partially linear models
Published in Journal of Multivariate Analysis, 2018
This paper introduces an RKHS-based approach for double-penalized regression in high-dimensional partially linear models.
Recommended citation: Cui, W., Cheng, H., & Sun, J. (2018). "An RKHS-based approach to double-penalized regression in high-dimensional partially linear models." Journal of Multivariate Analysis, 168, 201-210.
Publisher / DOI page | BibTeX
Chinese leadership: Provincial perspectives on promotion and performance
Published in Environment and Planning C: Politics and Space, 2018
This paper investigates Chinese leadership practices, focusing on provincial perspectives on promotion and performance.
Recommended citation: Sun, J., Cole, M., Huang, Z., & Wang, S. (2018). "Chinese leadership: Provincial perspectives on promotion and performance." Environment and Planning C: Politics and Space, 37(4), 750-772.
Publisher / DOI page | BibTeX
基于Katz分布的计数数据自回归模型以及其在预测呼吸系统患病人数中的应用
Published in 应用概率统计, 2020
本论文提出了一种基于Katz分布的计数数据自回归模型，并将其应用于预测呼吸系统疾病的发病人数。该方法在处理高维数据时表现出了较好的预测能力。
Recommended citation: 孙佳婧（第一作者、通讯作者）等 (2020). "基于Katz分布的计数数据自回归模型以及其在预测呼吸系统患病人数中的应用." 应用概率统计, 36(6): 551-568.
基于调整样本值域的自正则结构性变化的检验
Published in 统计研究, 2022
本论文提出了一种基于调整样本值域的自正则方法，用于检验时间序列中的结构性变化。该方法在强依赖性和重尾分布下表现良好，能够提升结构性变化检验的准确性。
Recommended citation: 孙佳婧（通讯作者）等 (2022). "基于调整样本值域的自正则结构性变化的检验." 统计研究 (Statistical Research), 39(04): 122-133.
Adjusted-range self-normalized confidence interval construction for censored dependent data
Published in Economics Letters, 2022
This paper proposes a self-normalized method for constructing confidence intervals for censored dependent data, improving the robustness and accuracy of inference.
Recommended citation: Sun, J., Hong, Y., Linton, O., & Zhao, X. (2022). "Adjusted-range self-normalized confidence interval construction for censored dependent data." Economics Letters, 110873.
Publisher / DOI page | BibTeX
A score statistic for testing the presence of a stochastic trend in conditional variances
Published in Economics Letters, 2022
This paper develops a score statistic for testing the presence of a stochastic trend in the context of conditional variances in time series data.
Recommended citation: Hong, Y., Linton, O., McCabe, B., & Sun, J. (2022). "A score statistic for testing the presence of a stochastic trend in conditional variances." Economics Letters, 213, 110394.
Publisher / DOI page | BibTeX
Innovation, carbon emissions, and the pollution haven hypothesis: Climate capitalism and global reinterpretations
Published in Journal of Environmental Management, 2022
This paper examines the relationship between innovation, carbon emissions, and the pollution haven hypothesis in the context of climate capitalism.
Recommended citation: Jiang, W., Cole, M., Sun, J., & Wang, S. (2022). "Innovation, carbon emissions, and the pollution haven hypothesis: Climate capitalism and global reinterpretations." Journal of Environmental Management, 307, 114465.
Publisher / DOI page | BibTeX
Social media interactions between government and the public: A Chinese case study of government WeChat official accounts on information related to COVID-19
Published in Frontiers in Psychology, 2022
This paper analyzes social media interactions between the Chinese government and the public using WeChat official accounts during the COVID-19 pandemic.
Recommended citation: Shao, C., Guan, X., Sun, J., Cole, M., & Liu, G. (2022). "Social media interactions between government and the public: A Chinese case study of government WeChat official accounts on information related to COVID-19." Frontiers in Psychology, 13, 955376.
Publisher / DOI page | BibTeX
Governmental Capabilities and Responsiveness: Global Investigations into CO2 Emissions and Decarbonization
Published in International Journal of Public Administration, 2024
This paper investigates the relationship between governmental capabilities and CO2 emissions in the context of global decarbonization efforts.
Recommended citation: Cole, M., Sun, J., Jiang, W., & Zang, L. (2024). "Governmental Capabilities and Responsiveness: Global Investigations into CO2 Emissions and Decarbonization." International Journal of Public Administration, 1-19.
Publisher / DOI page | BibTeX
Kolmogorov-Smirnov type testing for structural breaks: A new adjusted-range-based self-normalization approach
Published in Journal of Econometrics, 2024
This paper introduces a novel method for structural break testing using the Kolmogorov-Smirnov type test, enhanced by an adjusted-range-based self-normalization approach.
Recommended citation: Hong, Y., Linton, O., McCabe, B., Sun, J., & Wang, S. (2024). "Kolmogorov-Smirnov type testing for structural breaks: A new adjusted-range-based self-normalization approach." Journal of Econometrics, 238(2), 105603.
Publisher / DOI page | BibTeX
Structural stability of functional data: A new adjusted-range-based self-normalization approach
Published in Economics Letters, 2025
We propose an adjusted-range-based self-normalization method for testing structural stability in functional data. This approach improves computational efficiency and performs well in empirical applications to temperature data from New York Central Park Weather Station.
Recommended citation: Sun, J., Hong, Y., Lin, Z., & Xu, W. (2025). "Structural stability of functional data: A new adjusted-range-based self-normalization approach." Economics Letters, 253, 112350.
Publisher / DOI page | BibTeX
Adjusted-range-based self-normalized autocorrelation tests
Published in Economics Letters, 2025
This paper develops an adjusted-range-based self-normalized method for autocorrelation tests in time series analysis. The approach enhances statistical power while maintaining robustness against model misspecification.
Recommended citation: Sun, J., Zhu, M., & Linton, O. (2025). "Adjusted-range-based self-normalized autocorrelation tests." Economics Letters, 251, 112315.
Publisher / DOI page | BibTeX
金融计量经济学：理论、案例与 R 语言 (Financial Econometrics: Theory, Cases, and R Language)
Forthcoming from 高等教育出版社（Higher Education Press）, 2026
This is a Chinese-language textbook on financial econometrics.
Recommended citation: Sun, J., Hong, Y., & Linton, O. (forthcoming 2026). 金融计量经济学：理论、案例与 R 语言 (Financial Econometrics: Theory, Cases, and R). Higher Education Press.
金融科技学 (FinTech)
Forthcoming from 高等教育出版社（Higher Education Press）, 2026
This is a Chinese-language textbook on financial technology.
Recommended citation: Sun, J., Hong, Y., Wang, S., & Yang, Y. (forthcoming 2026). 金融科技学 (FinTech). Higher Education Press.
Econometrics and Time Series Methods: Theory, Applications, and R Implementation
Forthcoming with Springer, 2026
“Econometrics and Time Series Methods: Theory, Applications, and R Implementation” covers a wide range of topics including regression models, univariate and multivariate time series, volatility modeling, nonparametric and semiparametric methods, HAR inference, autoregressive filtering and state space models, nonstationary processes, continuous-time finance, and machine learning. The book emphasizes hands-on implementation in R, with extensive examples based on real financial and macroeconomic data, aiming to integrate theory, methods, empirical applications, and computation in a unified way.
Recommended citation: Hong, Y., Linton, O., & Sun, J. (2026 forthcoming). "Econometrics and Time Series Methods: Theory, Applications, and R Implementation." Springer.
Stablecoins, Risk Transmission and Systemic Reconfiguration in a Fragmented USD Access System: Evidence from Quantile Time-Frequency Analysis
Published in Systems, 2026
This article studies Argentina as a fragmented USD access system and uses quantile time-frequency connectedness analysis to examine risk transmission among official, parallel, and stablecoin-based dollar channels.
Recommended citation: Wu, J., Sun, J., Feng, H., & Long, F. (2026). "Stablecoins, Risk Transmission and Systemic Reconfiguration in a Fragmented USD Access System: Evidence from Quantile Time-Frequency Analysis." Systems, 14(5), 562.
Publisher / DOI page | BibTeX
Regression with R and Python: Description, Prediction, and Causal Inference
Publishing agreement signed with Springer, 2026
This forthcoming textbook introduces regression analysis through three connected goals: description, prediction, and causal inference. It combines statistical foundations with practical implementation in both R and Python, and is designed for readers who want to connect regression theory with modern empirical practice.
Recommended citation: Johansson, P., & Sun, J. (forthcoming 2026). "Regression with R and Python: Description, Prediction, and Causal Inference." Springer.
Virtual Water in Global Supply Chains: Trade Structure, Industrial Composition, and Policy Levers
Published in Ecological Economics, 2026
This paper uses Eora MRIO data for 189 countries (2010-2021) to map green, blue, and grey embodied-water flows and examine how virtual-water positions relate to industrial structure, trade openness, water withdrawal capacity, and growth outcomes.
Recommended citation: Jiang, W., Sun, J., Cole, M., & Zhang, Y. (2026). "Virtual water in global supply chains: Trade structure, industrial composition, and policy levers." Ecological Economics, 248, 109068.
Publisher / DOI page | BibTeX
talks
IEEE Symposium on Analytics and Risk
Published:
This talk discusses Commodity shocks and the macroeconomy, presented at the IEEE Symposium on Analytics and Risk.
9th ERCIM WG on Computational and Methodological Statistics; 10th International Conference on Computational and Financial Econometrics
Published:
This talk discusses RKHS-based approach to SCAD-penalized regression in high-dimensional partially linear models, presented at the 9th ERCIM WG on Computational and Methodological Statistics; 10th International Conference on Computational and Financial Econometrics.
2019 Chinese Economists Society (CES) China Conference
Published:
This talk discusses The Subjective Well-Being of Internal Migrants: Lessons from China, presented at the 2019 CES China Conference.
2019 World Econometric Society Asian Meeting (2019 AMES)
Published:
This talk discusses Model Averaging of Integer-Valued Autoregressive Model with Covariates, presented at the 2019 Asian Meeting of the Econometric Society (2019 AMES).
Royal Statistical Society Conference 2019
Published:
This talk discusses Model Averaging of Integer-Valued Autoregressive Model with Covariates, presented at the Royal Statistical Society Conference 2019.
12th Econometric Society World Congress 2020
Published:
This talk discusses Model Averaging of Integer-Valued Autoregressive Model with Covariates, presented at the 12th Econometric Society World Congress 2020.
13th International Conference of the ERCIM WG on Computational and Methodological Statistics
Published:
This talk discusses Testing Structural Breaks - A New Self-Normalization Approach Based on the Adjusted Sample Range, presented at the 13th International Conference of the ERCIM WG on Computational and Methodological Statistics.
The 4th China Econometric Scholars Forum
Published:
This talk discusses Testing Structural Breaks - A New Self-Normalization Approach Based on the Adjusted Sample Range, presented at the 第四届中国计量经济学者论坛.
Thirteenth Annual SoFiE Conference (North America)
Published:
This talk discusses A Score Statistic on Testing the Presence of Stochastic Trend in Conditional Variance, presented at the Thirteenth Annual SoFiE Conference (North America).
North American Summer Meeting of the Econometric Society
Published:
This talk discusses Testing for Structural Breaks - A New Self-Normalization Approach Based on the Adjusted Sample Range, presented at the North American Summer Meeting of the Econometric Society.
Australian Meeting of the Econometric Society
Published:
This talk discusses Testing for Structural Breaks - A New Self-Normalization Approach Based on the Adjusted Sample Range, presented at the Australian Meeting of the Econometric Society.
Tsinghua University Young Scholars Forum on Statistics and Data Science
Published:
This talk discusses Confidence Interval Construction - A New Self-Normalization Approach Based on Adjusted-Range, presented at the 清华大学统计学与数据科学青年学者论坛.
CFE-CMStatistics 2021
Published:
This talk discusses Confidence Interval Construction - A New Self-Normalization Approach Based on Adjusted-Range, presented at CFE-CMStatistics 2021.
Chow’s Young Scholars Forum
Published:
This talk discusses Kolmogorov-Smirnov Type Testing for Structural Breaks: A New Adjusted-Range Based Self-Normalization Approach, presented at the 邹至庄讲座青年学者论坛.
Advanced Techniques in Time Series Analysis: From Structural Changes to Time-Varying Networks
Published:
This talk discusses Sequential Change Point Detection for Time Series – An Adjusted-Range Based Approach, presented at the Advanced Techniques in Time Series Analysis: From Structural Changes to Time-Varying Networks workshop.
2024 Asian Meeting of the Econometric Society
Published:
This talk discusses Sequential Change Point Detection for Time Series – An Adjusted-Range Based Approach, presented at the 2024 Asian Meeting of the Econometric Society.
75th European Meeting of the Econometric Society
Published:
This talk discusses Confidence Interval Construction Based on the Adjusted-Range Self-Normalization Approach, presented at the 75th European Meeting of the Econometric Society.
Sequential Change-Point Detection in Time Series: An Adjusted-Range-Based Self-Normalization Approach
Published:
This talk discusses Sequential Change-Point Detection in Time Series: An Adjusted-Range-Based Self-Normalization Approach, presented at an invited seminar at the University of Leicester.
Sequential Change-Point Detection in Time Series: An Adjusted-Range-Based Self-Normalization Approach
Published:
This talk discusses Sequential Change-Point Detection in Time Series: An Adjusted-Range-Based Self-Normalization Approach, presented at an academic seminar at Renmin University of China.
2025 The 17th CEA (Europe) & 36th CEA (UK) Annual Conference
Published:
This talk discusses Sequential Change Point Detection for Time Series – An Adjusted-Range Based Approach, presented at the 17th CEA (Europe) & 36th CEA (UK) Annual Conference.
Global Virtual Water Flows and a Water Resource Curse?
Published:
This talk discusses Global Virtual Water Flows and a Water Resource Curse?, presented at an online seminar jointly organized by the IAEE Asia-Oceania Research Committee, the Institute for Global Governance and Innovation (IGGI), City University of Hong Kong, and the Energy Economics and Environmental Management Research Laboratory (E3M).
4th IMA Conference on Mathematics of Finance and Insurance
Published:
This talk discusses Online Monitoring of Financial Functional Time Series, presented at the 4th IMA Conference on Mathematics of Finance and Insurance.
teaching
Introduction to Finance
Undergraduate course, University of Liverpool, Management School, 2011
As the Module Leader for Introduction to Finance, I delivered the course to undergraduate students, covering essential topics in financial markets, investment theory, and financial decision-making. I guided students through fundamental finance principles, including time value of money, capital budgeting, and risk management.
Corporate Finance (MBA)
MBA course, University of Chinese Academy of Sciences, School of Economics and Management, 2013
This MBA course covers the fundamental principles of corporate finance, including capital budgeting, financing decisions, and the cost of capital. As the lead instructor, I guided students through the application of these concepts to real-world corporate situations, helping them understand the financial decision-making process within firms. Topics such as financial leverage, dividend policy, and merger and acquisition (M&A) were also explored in-depth.
English for Finance (MBA)
MBA course, University of Chinese Academy of Sciences, School of Economics and Management, 2013
This MBA course is designed to improve students’ proficiency in business English, with a specific focus on financial terminology and business communication. The course covered key topics such as financial reports, investment strategies, and economic policy discussions. As the lead instructor, I focused on helping students use English in professional finance contexts, enabling them to engage with global financial literature and communicate effectively in international financial markets.
Guest Lecturer in Financial Time Series Analysis
Guest lecture, China University of Political Science and Law, Department of Economics, 2013
As a guest lecturer in the Financial Time Series Analysis course, I delivered lectures on the key methods for analyzing financial data, including autoregressive models (AR), GARCH models, and techniques for modeling market volatility. I guided students through real-world examples and provided insights into how econometric tools are applied in financial economics.
Financial Economics
Postgraduate course, University of Chinese Academy of Sciences, School of Economics and Management, 2014
This course introduces the core theory of financial economics, with a focus on key topics such as asset pricing, market efficiency, and investment theory. As the lead instructor, I guided students through financial models and provided practical exercises in analyzing financial markets.
Financial Econometrics
Postgraduate course, University of Chinese Academy of Sciences, School of Economics and Management, 2014
This course focuses on financial econometrics, covering the core theories and methodologies used to model and analyze financial data. As the lead instructor, I provided lectures on the key econometric techniques and their applications in finance, including asset pricing models, market efficiency, volatility forecasting, and derivative pricing.
Financial Economics
Postgraduate course, University of Chinese Academy of Sciences, School of Economics and Management, 2015
This course introduces the core theory of modern financial economics and financial management. It covers aspects such as portfolio choice, systems of financial markets, arbitrage, option pricing, credit rationing, deposit contracts, banking, and regulation of banks.
Financial Statistics and Econometrics
Postgraduate course, University of Chinese Academy of Sciences, School of Economics and Management, 2015
This course focuses on financial statistics and econometrics, designed for postgraduate students. The aim is to equip students with the skills needed to analyze and model financial data using advanced econometric and time series methods. As the lead instructor, I guided students in applying these techniques to real-world financial datasets.
Financial Statistics and Econometrics
Postgraduate course, University of Chinese Academy of Sciences, School of Economics and Management, 2017
This course focuses on financial statistics and econometrics, designed for postgraduate students. The aim is to equip students with the skills needed to analyze and model financial data using advanced econometric and time series methods. As the lead instructor, I guided students in applying these techniques to real-world financial datasets.
Financial Statistics and Econometrics
Postgraduate course, University of Chinese Academy of Sciences, School of Economics and Management, 2018
This course focuses on financial statistics and econometrics, aimed at developing students’ ability to analyze and model financial data using both traditional econometric methods and time series models. As the lead instructor, I guided students through topics such as asset pricing models, market efficiency, volatility modeling (e.g., ARCH/GARCH), and the pricing of financial derivatives.
Financial Data Modeling and Analysis
Postgraduate course, University of Chinese Academy of Sciences, School of Economics and Management, 2019
This course explores methods used to model and analyze financial data, focusing on techniques such as time series modeling, ARIMA, and GARCH models. As the lead instructor, I guided students in analyzing real financial data and implementing modeling techniques using R and MATLAB.
Financial Economics
Postgraduate course, University of Chinese Academy of Sciences, School of Economics and Management, 2019
This course provides an introduction to the field of financial economics, focusing on asset pricing, financial markets, and investment theory. As the lead instructor, I delivered lectures on economic models for understanding market behavior and portfolio management. The course also covered key concepts in financial decision-making.
Financial Data Modeling and Analysis
Postgraduate course, University of Chinese Academy of Sciences, School of Economics and Management, 2020
This course explores methods used to model and analyze financial data, focusing on techniques such as time series modeling, ARIMA, and GARCH models. As the lead instructor, I guided students in analyzing real financial data and implementing modeling techniques using R and MATLAB.
Financial Econometrics
Postgraduate course, University of Chinese Academy of Sciences, School of Economics and Management, 2020
In this course, I taught financial econometrics with a focus on asset pricing models and volatility forecasting. Students were introduced to advanced econometric tools such as GARCH models, copulas, and machine learning methods. The course covers key topics like time series modeling, ARCH/GARCH models, nonparametric methods, asset pricing, volatility modeling, and risk management. As the lead instructor, I guided students through real-world applications using R programming.
Nonparametric Statistics
Postgraduate course, University of Chinese Academy of Sciences, School of Economics and Management, 2020
This course covers nonparametric statistical methods, focusing on the application of these methods to real-world data. As the lead instructor, I was responsible for delivering lectures, preparing materials, and guiding students through key concepts such as rank-based methods, the Wilcoxon test, and kernel density estimation.
Nonparametric Statistics
Postgraduate course, University of Chinese Academy of Sciences, School of Economics and Management, 2021
As the lead instructor for this course, I taught nonparametric statistical methods, including hypothesis testing, the Mann-Whitney U test, and Kruskal-Wallis test. Students were trained in applying these methods to real data, with a focus on understanding their applications in economics and finance.
Financial Econometrics
Postgraduate course, University of Chinese Academy of Sciences, School of Economics and Management, 2022
In this course, I taught financial econometrics with a focus on asset pricing models and volatility forecasting. Students were introduced to advanced econometric tools such as GARCH models, copulas, and machine learning methods. The course covers key topics like time series modeling, ARCH/GARCH models, nonparametric methods, asset pricing, volatility modeling, and risk management. As the lead instructor, I guided students through real-world applications using R programming.
Nonparametric Statistics
Postgraduate course, University of Chinese Academy of Sciences, School of Economics and Management, 2023
This course focused on nonparametric statistics, including methods for rank tests, kernel density estimation, and bootstrap methods. I led lectures, assignments, and guided students through real data analysis, emphasizing the application of these methods in econometrics.
Advanced Time Series Analysis
Postgraduate course, University of Chinese Academy of Sciences, School of Economics and Management, 2024
This course focuses on advanced time series analysis techniques, including cointegration, VAR models, and state-space models. The course also covers regression models, univariate and multivariate time series, volatility modeling, nonparametric and semiparametric methods, HAR inference, autoregressive filtering, and continuous-time finance, with an emphasis on real-world financial and macroeconomic data. As the lead instructor, I provided students with the skills to model complex financial and economic time series data.
Advanced Time Series Analysis
Postgraduate course, University of Chinese Academy of Sciences, School of Economics and Management, 2025
This course focuses on advanced time series analysis techniques, including cointegration, VAR models, and state-space models. The course also covers regression models, univariate and multivariate time series, volatility modeling, nonparametric and semiparametric methods, HAR inference, autoregressive filtering, and continuous-time finance, with an emphasis on real-world financial and macroeconomic data. As the lead instructor, I provided students with the skills to model complex financial and economic time series data.
Advanced Time Series Analysis
Postgraduate course, University of Chinese Academy of Sciences, School of Economics and Management, 2026
This course focuses on advanced time series analysis techniques, including cointegration, VAR models, and state-space models. The course also covers regression models, univariate and multivariate time series, volatility modeling, nonparametric and semiparametric methods, HAR inference, autoregressive filtering, and continuous-time finance, with an emphasis on real-world financial and macroeconomic data. As the lead instructor, I provided students with the skills to model complex financial and economic time series data.