何杰操 Jie-Cao He

何杰操 Jie-Cao He

浙江万里学院(Zhejiang Wanli University)

About Me

  • He Jie-Cao holds a Ph.D. in Finance (Financial Engineering) from the College of Commerce at National Chengchi University in Taiwan. He is recognized as a high-level talent in Ningbo and currently serves as a lecturer in the Department of Finance and Financial Engineering at the School of Business, Zhejiang Wanli University. His research areas mainly include the pricing of interest rate and exchange rate derivatives, interest rate models, stochastic processes, quantitative trading, machine learning, natural language processing (NLP), bank risk management, and audit quality analysis. During his doctoral studies, he participated in six provincial and ministerial projects and four horizontal projects. He has published three SSCI Q1 papers in journals such as Financial Innovation (IF 8.4), International Review of Financial Analysis (IF 8.2), and Pacific-Basin Finance Journal (IF 4.6). He primarily teaches courses such as Finance, Structured Products, Financial Data Analysis and Simulation, Derivatives, and Financial Engineering and Innovation.

📖 Educations

  • 2012.09 - 2016.07, Feng Chia University – Department of Finance – Bachelor
  • 2016.09 – 2018.07, National Chengchi University – Department of Money and Banking – Master
  • 2019.09 – 2023.12, National Chengchi University – Department of Money and Banking – Ph.D.

📝 Publications

🎖 Honors and Awards

  • 2019.09.01, Full Scholarship
    • 台湾政治大学光华奖学金(Kwang-Hua Scholarship).
  • 2020.12.28, Outstanding Article Award
    • 2020 New Futures – Futures Academic and Practical Exchange Conference.
  • 2021.07.02, Outstanding Article Award
    • 2020学年博士班论文研究发表营优秀论文奖
  • 2023.12.19, Academic Honor
    • AACSB Beta Gamma Sigma

💬 Lecture Courses

  • 2019.09 – 2020.01, Financial Engineering and Innovations (Master and Ph.D.)
  • 2020.09 – 2021.01, Financial Engineering and Innovations (Master and Ph.D.),Big Data Analysis and Financial Technology (Master and Ph.D.)
  • 2021.02 – 2021.06, Financial Engineering in Interest Rate and Credit Risk (Master and Ph.D.), Financial Derivatives (Bachelor)
  • 2021.09 – 2022.01, Financial Engineering and Innovations (Master and Ph.D.), Financial Institution Management (Bachelor)

💻 Participation in Research Programs

2017.09 – 2019.07, Evaluation of Conditional Bonds, Interest Rate Derivatives, and Credit Derivatives under the Dynamic Multivariate GARCH Model of Time-Varying Correlations

  • Project Type: Provincial and Ministerial-Level Research Project (Participation)
  • Collaborating Institution: Taiwan Science and Technology Council
  • I participated as a team member in a project led by my advisor. Given the increasing demand in the current market for conditional bonds, interest rate derivatives, and credit derivatives, the proposed model in this project aims to describe multiple risk factors, leading to more accurate evaluations of these financial instruments. By conducting sensitivity analysis, investors can assess their investment risks effectively, thus reducing risks and contributing to market stability. This project provides investors with effective risk management tools in response to the growing complexities of financial markets, ultimately promoting market stability and risk reduction.

2017.09 – 2019.07, Evaluation of Conditional Bonds, Interest Rate Derivatives, and Credit Derivatives under the Dynamic Multivariate GARCH Model of Time-Varying Correlations

  • Project Type: Provincial and Ministerial-Level Research Project (Participation)
  • Collaborating Institution: Taiwan Science and Technology Council
  • I participated as a team member in a project led by my advisor. Given the increasing demand in the current market for conditional bonds, interest rate derivatives, and credit derivatives, the proposed model in this project aims to describe multiple risk factors, leading to more accurate evaluations of these financial instruments. By conducting sensitivity analysis, investors can assess their investment risks effectively, thus reducing risks and contributing to market stability. This project provides investors with effective risk management tools in response to the growing complexities of financial markets, ultimately promoting market stability and risk reduction.

2019.06 – 2020.05, Cluster Intelligence Platform for Trading, Investing and Risk Management

  • Project Type: Provincial and Ministerial-Level Research Project (Participation)
  • Collaborating Institution: Taiwan Science and Technology Council
  • I participated as a team member in a project led by my advisor. This project is a novel financial technology service, aiming to develop financial market modules such as ETF strategy models, as well as providing investment, trading, and risk management platforms for investment banks. The societal and economic impact of this project is significant, as it assists individuals in navigating financial resources amidst regulatory changes, offers investment choices, and enhances market liquidity. It contributes to economic development by helping people seek financial opportunities within evolving regulations and facilitating investment and trading activities for increased market liquidity.

2019.08 – 2021.07, Liquidity Risk Premium in Stock Index Options and Volatility Index Options under Stochastic Volatility: An Analysis of Risk Neutrality and Perfect Competition Equilibrium

  • Project Type: Provincial and Ministerial-Level Research Project (Participation)
  • Collaborating Institution: Taiwan Science and Technology Council
  • I participated as a team member in a project led by my advisor. This project aimed to investigate three main aspects: (1) Integration of various models, including stochastic volatility, price and volatility jump synchronicity, dynamic random jump counts, liquidity dynamics, and liquidity jumps, to construct a comprehensive valuation model for improving dynamic asset allocation and evaluation methods. (2) Estimation of implied volatility surface parameters in the options market using particle filtering algorithms combined with the maximum likelihood estimation algorithm and joint estimation techniques. (3) Conducting financial empirical research to analyze the impact of calibrated variance risk premiums and liquidity risk premiums on the overall risk premium and explore the predictive power of risk premiums with different term structures on yield forecasting. The contribution of this project lies in providing improved valuation models and risk management methods to enhance the efficiency and stability of financial markets.

2019.09 – 2020.07, Evaluation of Callable CMS Spread Range Accrual Derivative Products using LFM Model

  • Project Type: Industry-Academia Collaboration (Participation)
  • Collaborating Institution: E.Sun Commercial Bank Co., Ltd.
  • I served as the research team leader for a project led by my advisor. This project aimed to develop an evaluation model for callable CMS spread range accrual derivative products using the Log-normal Forward Market Model (LFM) to assess their prices. In this project, I led the research team in estimating the forward LIBOR rates using yield curves. We then used Cap and Caplet pricing to estimate the volatility and correlation coefficients of forward rates. Finally, we employed the Least Squares Monte Carlo simulation method to calculate the prices of the derivative products. Through in-depth research and the application of the LFM model, we successfully developed a precise and effective evaluation model for callable CMS spread range accrual derivative products. This model has received high praise and recognition from E.Sun Commercial Bank.

2020.02 – 2020.11, Research on the Supervision and Management of Liquidity Risk in Banking - Focused on Pure Online Banks

  • Project Type: Industry-Academia Collaboration (Participation)
  • Collaborating Institution: Taiwan Central Deposit Insurance Corporation
  • I served as the research team leader for a project led by my advisor. This project aimed to evaluate short-term, medium-term, and long-term liquidity risks specific to pure online banks and establish a liquidity risk early warning model for regulatory authorities. The project was initiated by the Taiwan Central Deposit Insurance Corporation. Leading the team, we conducted an analysis based on regulatory guidelines and relevant literature from the banking industries in the United States and Europe, focusing on 33 local traditional banks in Taiwan. During the research process, we utilized BASEL III and Taiwan’s financial regulations as a foundation to assess and analyze the liquidity risks of 33 traditional commercial banks and 4 pure online banks in the short, medium, and long term. The goal was to provide a basis for regulatory authorities to establish a comprehensive liquidity risk early warning model.

2020.08 – 2021.07, Evaluation of Callable CMS Spread Range Accrual Derivative Products using LSM Model

  • Project Type: Industry-Academia Collaboration (Participation)
  • Collaborating Institution: E.Sun Commercial Bank Co., Ltd.
  • I served as the research team leader for a project led by my advisor. This project aimed to develop an evaluation model for callable CMS spread range accrual derivative products using the Log-normal Swap Market Model (LSM), addressing the challenges posed by the transition period during the discontinuation of LIBOR without a definitive replacement rate. In this project, I led the team in estimating the interest rate curves for swap rates using the yield curve and calibrating the CMS rates for 90-day, 2-year, and 10-year tenors based on Swaption data. Finally, the LSMC method was employed to calculate the prices of the derivative products. The evaluation model, which was rigorously tested, proves to efficiently and accurately assess the prices of callable CMS spread range accrual derivative products.

2021.02 – 2021.07, U.S. Securities and Exchange Commission Inquiry Letters: Determinants and Consequences of Financial Reporting Disclosure Behavior Changes

  • Project Type: Provincial and Ministerial-Level Research Project (Participation)
  • Collaborating Institution: Taiwan Science and Technology Council
  • I participated as a team member in a project led by Associate Professor Yu-Tzu Chang. This project explores the determinants of efforts made by publicly traded companies to address SEC inquiry letters and their consequences. The project comprises two research plans: Research Plan 1 emphasizes the determinants and consequences of companies’ efforts in addressing SEC inquiry letters. Research Plan 2 investigates the impact of inquiry letter disclosures on a company’s creditworthiness in the bond market. The contributions of this research project encompass three key aspects: 1. Positive influences of audit and corporate governance characteristics on the effectiveness of SEC annual report reviews. 2. The correlation between the level of effort exerted by companies in addressing SEC inquiry letters and investors’ perceptions of the quality of a company’s financial reporting. 3. The impact of inquiry letters on a company’s credit rating in the bond market.

2021.08 – 2022.12, Credit Scoring Models and Interest Rate Pricing in Peer-to-Peer (P2P) Lending - The Application of Machine Learning in Risk Management

  • Project Type: Provincial and Ministerial-Level Research Project (Participation)
  • Collaborating Institution: Taiwan Science and Technology Council
  • I served as the research team leader for a project led by my advisor. This research project aims to innovatively construct a risk management framework that combines credit scoring models and interest rate pricing to enhance the accuracy of risk assessment and effective control of credit risk on P2P lending platforms. Furthermore, the project employs machine learning techniques to extensively explore and identify hidden features contributing to the assessment of borrower creditworthiness. It also introduces a novel interest rate pricing model designed to more accurately reflect the borrower’s credit profile and expected returns. Finally, the project conducts empirical analyses of the application of machine learning in risk management, validating the effectiveness and practicality of the proposed strategies. This research offers fresh insights and solutions for risk management on P2P lending platforms, presenting a new perspective on how machine learning can be leveraged in this context.

2021.08 – 2023.07, Exploring the Relationship between PCAOB Inspection Reports and Audit Quality Using Machine Learning

  • Project Type: Provincial and Ministerial-Level Research Project (Participation)
  • Collaborating Institution: Taiwan Science and Technology Council
  • I participated in a project led by Associate Professor Yu-Tzu Chang. The project employed machine learning-based topic identification algorithms to analyze inspection reports issued by the Public Company Accounting Oversight Board (PCAOB) of the United States. The project consisted of two main research objectives: Research 1 aimed to investigate whether the content of the first part of the inspection reports provides additional information for distinguishing audit quality. Research 2 focused on assessing whether quality control deficiencies revealed in the second part of the inspection reports impact audit clients’ decisions to change their auditors.
  • This study’s contribution lies in the application of emerging machine learning algorithms for topic analysis of PCAOB inspection reports. The analysis is expected to uncover both substantive and perceptual impacts of PCAOB inspection reports on audit quality, providing valuable insights. This research enhances our understanding of audit quality and reveals the informational value of PCAOB inspection reports in this domain.

2022.03 – 2022.10, Supervisory Application of CAMELS Indicators in Assessing Credit Risk for Financial Institutions

  • Project Type: Industry-Academia Collaboration (Participation)
  • Collaborating Institution: Taiwan Depository & Clearing Corporation
  • I served as the research team leader for a project led by my advisor. The project aimed to explore a credit risk supervisory model applicable to Taiwanese securities firms and was conducted at the invitation of the Taiwan Depository & Clearing Corporation. Our team referenced relevant regulations and literature in the U.S. securities industry. Through principal component analysis and regression modeling, we established an early warning model comprising 38 indicators. Additionally, we achieved the objective of swiftly identifying the sources of risk through attribution analysis.