Blockchain and Crypto Market Risks

 

In recent years, blockchain technology and cryptocurrencies have experienced significant growth. With Bitcoin price reaching its new high over $100,000, the global cryptocurrency market capitalization reached a record $3.5 trillion in 2024. The number of cryptocurrency users also doubled in 2 years, rising from 300 million in 2021 to 600 million in 2024. The crypto market reshapes the financial landscape and sparks interests from both academic and industry.

While blockchain offers great benefits such as decentralization, transparency, and immutability, it is not without its own vulnerabilities. Compared to traditional centralized financial systems, the cryptocurrency market is sometimes described as “a highly inefficient database secured by collective distrust”, with its distinctive risks including extreme price volatility, systemic risks from protocol failures, susceptibility to market manipulation, and regulatory uncertainties.

This project aims to explore the financial risks inherent to the cryptocurrency market. Throughout the semester, we will start by reviewing a selection of research papers to identify the key aspects of crypto-related risks and uncover gaps the existing literature. Building on this, we will then investigate the mechanisms of blockchain technology to understand how its structure contributes to both its strengths and vulnerabilities. Finally, we will use the crypto market data and crypto insurance data gathered in the previous semester for an empirical analysis to test our hypotheses and propose recommendations for mitigating risks in the crypto space.

Supervisor: Xiaochen Jing

Graduate Supervisor: Zhonghe Wan

Data Discovery and Consolidation

 

As data-driven decision-making becomes increasingly vital in addressing climate-related challenges, it is crucial to build a comprehensive understanding of datasets that support research and innovation in climate risk assessment and mitigation. This project aims to conduct an extensive data discovery process within UIUC, focusing on identifying and cataloging datasets across units, research centers, and government entities, with a special emphasis on climate risk, insurance, and actuarial applications. By consolidating these datasets into a centralized repository, we aim to create a unified platform that empowers researchers, students, and faculty members to analyze and model climate risks more effectively.

The project aligns with SOA’s objectives by supporting the development of data-driven tools and methodologies for assessing risks associated with extreme weather. The repository will include datasets relevant to climate science, risk modeling, and financial impact analysis, bridging the gap between meteorological data and actuarial applications. We anticipate that this initiative will involve dedicated personnel for database management, data visualization, and relational database creation.

Supervisor: Zhiyu Quan

Graduate Supervisor: Jiayi Guo

PCMI Warranty Data Innovation Project

Overview

This project engages students from the University of Illinois in a practical, hands-on experience with PCMI, a leader in software solutions for warranties. The project aims to explore new data product opportunities, develop a business case, and support the design and development of innovative data-driven products.

Goals

  1. Identify New Data Product Opportunities: Students will investigate three potential areas for data product innovation:
    1. Enhancing existing products by integrating analytics directly into them.
    2. Developing new standalone data products for current customers.
    3. Creating new standalone data products targeted at new market segments.
  2. Develop a Business Case: Each team will establish a business case to justify the investment in their proposed data products. This includes market analysis, potential ROI, and strategic alignment with PCMI’s objectives.
  3. P&L Model Development: Over the course of the project, students will build a profit and loss (P&L) model for the proposed data products, detailing anticipated revenue, cost, and margin scenarios.
  4. Identify Data Augmentation Partners: Teams will research and propose potential partners for data augmentation to enhance the value of PCMI’s data products.
  5. Requirements and Solution Design: Students will develop detailed requirements for the approved new data products and collaborate with the PCMI RDR (Research, Development, and Requirements) team on the solution design.
  6. Development Backlog Creation: Following Minimum Viable Product (MVP) best practices, students will create a development backlog for a PCMI development team to execute.

Expected Outcomes

  • A portfolio of new data product ideas fully explored and documented.
  • Comprehensive business cases supporting further investment in new products.
  • A robust P&L model for each proposed product.

 

Deliverables

  • Final report detailing research findings, business cases, P&L models, and product requirements.
  • Presentation to PCMI stakeholders showcasing proposed data products and strategic insights.
  • Development backlog ready for implementation by PCMI’s development team.

 

Evaluation

  • Quality and innovation of data product ideas.
  • Feasibility and thoroughness of the business cases.
  • Practicality and detail in the P&L models and development plans.
  • Engagement with potential data augmentation partners and early adopters.

 

This project proposal offers a unique opportunity for students to apply their academic knowledge to real-world challenges, fostering innovation and strategic thinking in the development of new data products at PCMI.

Supervisor: Zhiyu Quan

Pricing of Weather Derivatives

By pursuing these projects, we aim to investigate and develop models that can aid in managing the multifaceted risks associated with climate change. The demand for weather derivatives in the US and Europe has significantly in- creased in response to extreme weather risks and their impact on all economic sectors. According to a report published in May 2024 by the Chicago Mercantile Exchange (CME) Group, trading volumes rose by 260% in 2023, accompanied by a 48% increase in outstanding contracts in 2024. Weather derivatives are financial products that hedge weather risk and are commonly designed as vanilla options, swaps, and futures, that are based on different underlying weather indices. The most popularly used indices are heating degree-days, cooling degree-days, and the cumulative average temperature. In the literature on pricing weather derivatives, the underlying process, the temperature, is often described as an affine model. This project aims to price temperature and rain- fall derivatives using the stochastic volatility model implemented in Alfonsi and Vadillo (2024) with a focus on the US.

Supervisor: Tolulope Fadina

RGA Financial Models

This R&D project explores the development and assessment of various data-driven financial cash models to manage RGA’s global financial service products including longevity swaps, asset-intensive transactions, and pension risk transfers. Transaction-specific data runs the spectrum from voluminous to sparse. Transactions typically range from $100 million to $10+ billion. Participants will apply actuarial science and data science to develop various proxy models and assess underlying assumptions. Models are used to monitor performance, risk profiles, manage capital, and provide insights into business management.

The iRisk team will collaborate with RGA Global Financial Services (GFS) Valuation Team Europe-Middle-East-Africa (EMEA) actuaries led by former ARMS instructor, Tim Cardinal. Fall 2024 will focus on learning the business, models, and data, developing problem definitions, and building prototypes. Spring 2025 will focus on operationalizing prototypes and learning insights into models’ strengths and weaknesses as they are applied to recent/new transactions. Without going into details, the work will be engaging, challenging, and exciting.

Students will be required to sign a non-disclosure agreement (NDA). In your application, please indicate your interest in internships and co-ops. Ideally, we would like 1-3 of the iRisk student team to intern during summer 2025 and 1-2 students to co-op in fall and/or spring 2025.

RGA is one of the largest global life and health reinsurers and is #223 on the Fortune 500 with offices in dozens of countries around the world from St. Louis and Toronto to London, Dublin, Paris, and Milan to Hong Kong, Shanghai, Beijing, Tokyo, Mubai, and Dubai.

Supervisors: Zhiyu (Frank) Quan, Xiaochen Jing, Tim Cardinal, RGA GFS ValuaHon EMEA team

Testing Portfolio Management Theories

 

Theories of portfolio management are central to investment strategies and risk management in actuarial science and finance. This project aims to test portfolio management theories by collecting and analyzing real-world data. Students will use Python to assess the validity of these theories during different market conditions and gain insights into their practical applications.

Students will explore prominent portfolio management theories and test their predictive and practical value using real market data. The project will consist of three main phases:

  1. Introduction to Theories and Data Collection
    1. Study foundational portfolio management theories, including Modern Portfolio Theory (MPT), negative beta during recessions, StatArb, and passive vs. active management strategies.
    2. Learn methods of data collection, focusing on acquiring historical stock and market data from sources such as Yahoo Finance.
    3. Example theories to test:
      1. Does a low or negative beta protect portfolios during market crashes?
      2. Is passive management as effective as active management in achieving higher returns?
  2. Data Analysis and Hypothesis Testing
    1. Apply statistical techniques and visualization tools to analyze collected data. Statics used will include percent return, standard deviation, semi-variance, beta, alpha, correlation, and cointegration.
    2. Develop and test hypotheses on past data. For example:
      1. Compare portfolio performance during crash periods based on beta values.
      2. Analyze returns from actively managed funds versus index funds over multiple time periods.
      3. We will create a tool in Python that will give us statistics for a portfolio technique using past data.

  3. Results Interpretation and Reporting

    1. Summarize findings and evaluate the efficacy of each tested theory.
    2. Discuss implications for portfolio management practices and actuarial risk management.
    3. Use the Investopedia Simulator API to create portfolios for validation.

    This project offers an opportunity for students to bridge theoretical knowledge with practical applications in portfolio management, enhancing their understanding of investment strategies and their implications for risk management.

    Supervisor: Eric Icaza