This study revises the Financial Risk Meter (FRM) framework, which is based on Lasso quantile regression, to assess systemic risk and tail co-movement in Taiwan’s high market capitalization electronic and financial sectors.
After the global financial crises and the COVID-19 pandemic, understanding and predicting systemic risk has become increasingly crucial. As a global semiconductor and electronics manufacturing hub, Taiwan’s stock market stability directly affects the global supply chain and economy. This study revises the Financial Risk Meter (FRM) framework, which is based on Lasso quantile regression, to assess systemic risk and tail co-movement in Taiwan’s high market capitalization electronic and financial sectors.
To effectively identify and predict the upcoming recessions, we incorporated machine learning techniques into the FRM, validated by comparing it with the Business Cycle Indicators furnished by Taiwan’s National Development Council. Additionally, the FRM is contrasted with other risk indicators such as VIXTWN and Google SVI to furnish a comprehensive understanding of systemic risk in Taiwan’s electronic and financial landscape. The contributions of this paper lie in offering a localized lens for systemic risk assessment, providing valuable insights for both policymakers and investors.
Wolfgang Karl HÄRDLE attained his Dr. rer. nat. in Mathematics at Universität Heidelberg in 1982 and in 1988 his habilitation at Universität Bonn. He is Ladislaus von Bortkiewicz Professor of Statistics at Humboldt-Universität zu Berlin and the director of the Sino German Graduate School (洪堡大学 + 厦门大学) IRTG1792 on “High dimensional non stationary time series analysis”. He also serves as head of the joint BRC blockchain-research-center.com (with U Zürich). He is guest professor at WISE, Xiamen U, SMU, Singapore, NYCU, Hsinchu TW, Charles U, Prague CZ.
His research focuses on data sciences, dimension reduction and quantitative finance. He has published over 30 books and more than 300 papers in top statistical, econometrics and finance journals. He is highly ranked and cited on Google Scholar, REPEC and SSRN. He has professional experience in financial engineering, SMART (Specific, Measurable, Achievable, Relevant, Timely) data analytics, machine learning and cryptocurrency markets. He has created the www.quantlet.com platform, a financial risk meter, FRM hu.berlin/frm, a cryptocurrency index, CRIX www.royalton-crix.com and organises regularly blockchainnights.com . He is 玉山学者 （Yushan Scholar), web page hu.berlin/wkh