Geyue(Grace) Sun

Geyue(Grace) Sun

she/her

PhD Candidate at George Washington University,Department of Economics

Posts by Collection

portfolio

publications

Impact of the Opening of Shanghai Free Trade Area on the Port Economy in Hong Kong

Published in China Circulation Economy, 2017

Investigated the impact of the Shanghai Free Trade Area (FTA) on Hong Kong’s port economy using a Difference-in-Differences (DID) approach and border analysis. The analysis revealed a crowding-out effect of the Shanghai FTA on Hong Kong’s import-export market, attributed to Hong Kong’s dependence on sales and import volume rather than production innovation and R&D capabilities.

Recommended citation: G. Sun. Impact of the Opening of Shanghai Free Trade Area on the Port Economy in Hong Kong China, Circulation Economy, 2017

Carry Trade and the Theoretical Framework of “Impossible Trinity” Theory: An Empirical Analysis Based on TVP-SV-VAR

Published in Finance Forum, 2022

This paper evaluated China’s capital account liberalization and exchange rate marketization by integrating carry trade into the Impossible Trinity framework. Built a theoretical model and using TVP-SV-VAR analysis to examine the short- and long-term impulse responses between carry trade, capital controls, exchange rate stability, and monetary policy independence. Findings revealed that carry trade significantly impacts the independence of monetary policy, supporting a phased approach to capital account liberalization, starting with Portfolio Investment, followed by Financial Derivatives and FDI accounts.

Recommended citation: 郭桂霞,and 孙歌悦."人民币套息交易与“三元悖论”理论框架——基于TVP-VAR模型的实证研究." 金融论坛 27.11(2022):10-20. doi:10.16529/j.cnki.11-4613/f.2022.11.003.
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Bypassing Capital Interventions: Carry Trades via Commodity Futures Market

Published in 2024 Southern Economics Association (SEA) 94th Annual Meeting, 2024

Advisor: Prof. Tomas Williams
This project investigates commodity carry trade in developing countries, testing two key hypotheses: 1. Commodity liquidity risk reduces carry trade returns (impact: -0.226) 2. Capital controls amplify the negative impact of liquidity risk
A Staggered-DID model is employed using 4,000 daily capital intervention events from the Global Trade Alert (GTA) dataset. A Large Language Model (LLM) extracts regional information from 25,035 commodity contracts (Refinitiv), which is merged with Bloomberg’s carry trade returns to analyze liquidity risk’s effect on returns. The project’s findings were presented at the 2024 SEA Annual Meeting, offering new insights into the commodity-carry trade market equilibrium.

Recommended citation: G. Sun. (2024). "Bypassing Capital Interventions: Carry Trades via Commodity Futures Market." 2024 Southern Economics Association (SEA) 94th Annual Meeting.
Download Paper | Download Slides

Learning to Regulate: A New Event-Level Dataset of Capital Control Measures

Published in arXiv:2505.23025 (Working Paper, The Journal of Finance and Data Science), 2025

Working paper of The Journal of Finance and Data Science - Constructed a global event-level dataset of 5,198 capital control measures (1999–2023, 196 countries) using LLM extraction and finetuning based on IMF AREAER reports. - Applied event-study methods and found strong cross-country heterogeneity in capital-flow sensitivity; global shocks (↑VIX, USD appreciation) raise the likelihood of inflow restrictions, while domestic FX pressure and current-account imbalances lead to outflow controls. - Develops a dataset and empirical framework to forecast capital-account interventions under different macro-financial environments, contributing to capital-flow management analysis.

Recommended citation: Sun, G., Liu, X., Williams, T., et al. (2025). "Learning to Regulate: A New Event-Level Dataset of Capital Control Measures." arXiv preprint arXiv:2505.23025.
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An LLM-based Survey of Stablecoin Podcasts

Published in SSRN, 2025

Applies large language models to transcribe and analyze thousands of stablecoin podcast episodes, constructing structured measures of sentiment, regulatory views, and key themes. Available at SSRN.

Recommended citation: Ahmed, R., Rebucci, A., and Sun, G. (2025). "An LLM-based Survey of Stablecoin Podcasts." SSRN, October 19, 2025. http://dx.doi.org/10.2139/ssrn.5628451
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Stablecoins: A Revolutionary Payment Technology with Financial Risks

Published in NBER Working Paper No. 34475, 2025

A study of stablecoins as an emerging payment technology and the financial-stability risks they introduce. Published as an NBER Working Paper (No. 34475).

Recommended citation: Ahmed, R., Clouse, J. A., Natalucci, F., Rebucci, A., and Sun, G. (2025). "Stablecoins: A Revolutionary Payment Technology with Financial Risks." NBER Working Paper No. 34475, National Bureau of Economic Research.
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Crypto Shadow Banking: Stablecoins, Crypto Assets and Capital Controls

Published in Job Market Paper, 2025

Job Market Paper - Developed a large-scale LLM- and text-based crypto exposure dataset from U.S. public firms’ financial filings (10-K, 8-K, and 20-F; 4,696 firm-year observations, 2015–2025) to detect hidden digital-asset exposure, stablecoin usage, and crypto-based capital-control bypassing behavior. - Using Probit/Logit regressions, showed that balance-sheet proxies such as intangible assets and inventories predict crypto-related corporate activities. These proxies increase significantly following capital-control and equity-market intervention shocks, revealing the rise of “crypto shadow banking” as a substitute channel for cross-border liquidity. - Provided a quantitative model assessing the impact of crypto shadow banking on the stability of the financial system.

Recommended citation: G. Sun. (2025). "Crypto Shadow Banking: Stablecoins, Crypto Assets and Capital Controls." Job Market Paper.

talks

Impact of the Shanghai Free Trade Area on Hong Kong’s Port Economy

Published:

Published in China Circulation Economy (2017)

  • Investigated the impact of the Shanghai Free Trade Area (FTA) on Hong Kong’s port economy using a Difference-in-Differences (DID) approach and border analysis.
  • Revealed a crowding-out effect of the Shanghai FTA on Hong Kong’s import-export market, attributed to Hong Kong’s dependence on sales and import volume rather than production innovation and R&D.

RMB Carry Trade and the Theoretical Framework of the Impossible Trinity

Published:

Published in Finance Forum (2022)  ·  with Prof. Guixia Guo

  • Evaluated China’s capital-account liberalization and exchange-rate marketization by integrating carry trade into the Impossible Trinity framework.
  • Built a theoretical model and used TVP-SV-VAR analysis of the short- and long-run impulse responses among carry trade, capital controls, exchange-rate stability, and monetary-policy independence.
  • Findings support a phased approach to capital-account liberalization, beginning with portfolio investment, followed by financial derivatives and FDI accounts.

Bypassing Capital Interventions: Carry Trades via Commodity Futures Market

Published:

Advisor: Prof. Tomas Williams

  • Conducted empirical research on commodity carry trade in developing countries, testing two key hypotheses:
    1. Commodity liquidity risk significantly reduces carry trade returns (estimated impact: -0.226).
    2. The negative impact of liquidity risk is amplified by capital controls.
  • Utilized a Staggered-DID model to investigate bypass mechanisms in response to diverse capital control policies. The analysis was based on daily capital intervention data (4,000 events) from the Global Trade Alert (GTA) dataset.
  • Built a Large Language Model (LLM) to extract regional information from 25,035 commodity contracts in the Refinitiv dataset. Merged this with Bloomberg’s daily carry trade returns to assess the influence of liquidity risk on carry trade returns and develop a quantitative model for the commodity-carry trade market equilibrium.
  • Presented at the 2024 Southern Economics Association (SEA) 94th Annual Meeting on November 2024.

Recommended citation: Sun, Geyue, Bypassing Capital Interventions: Carry Trades via Commodity Futures Market (December 30, 2024).
Download Paper | Download Slides

Learning to Regulate: A New Event-Level Dataset of Capital Control Measures Permalink

Published:

Working Paper — under review at The Journal of Finance and Data Science  ·  with X. Liu, T. Williams, et al.

  • Constructed a global event-level dataset of 5,198 capital control measures (1999–2023, 196 countries) using LLM extraction and finetuning based on IMF AREAER reports.
  • Applied event-study and time-series/ML methods: global shocks (↑VIX, USD appreciation) raise the likelihood of inflow restrictions, while domestic FX pressure and current-account imbalances lead to outflow controls.
  • Provides an empirical framework to forecast capital-account interventions under different macro-financial environments. Read on arXiv »

Decentralized Expectations and the AI Bubble

Published:

Working Paper (in progress)

  • Constructed a novel AI Belief Index (AIBI) using decentralized prediction-market data (Polymarket) to quantify collective expectations about AI-related technological, valuation, and regulatory events.
  • Combined on-chain probability data, LLM-based text sentiment (Refinitiv News), and market prices to measure real-time belief formation and speculative intensity during the 2023–2025 AI investment boom.
  • Employed VAR, bubble-detection (GSADF), and forecasting regressions to show that AIBI surges anticipate asset-price accelerations and correction phases in AI equities and ETFs, offering early-warning implications for financial stability.

An LLM-based Survey of Stablecoin Podcasts Permalink

Published:

SSRN Working Paper  ·  with R. Ahmed and A. Rebucci

  • Applied large language models to transcribe and analyze thousands of stablecoin podcast episodes, constructing structured measures of sentiment, regulatory views, and key themes in the stablecoin discourse.
  • Demonstrated how audio-native LLM pipelines can turn unstructured media into research-grade datasets for economics and finance.
  • Available at SSRN: http://dx.doi.org/10.2139/ssrn.5628451

Stablecoins: A Revolutionary Payment Technology with Financial Risks Permalink

Published:

NBER Working Paper No. 34475  ·  with R. Ahmed, J. A. Clouse, F. Natalucci, and A. Rebucci  ·  work conducted during my Economist Internship at the Andersen Institute of Finance and Economics

  • Studied stablecoins as an emerging payment technology and the financial-stability risks they introduce for monetary policy, cross-border flows, and market structure.
  • Designed and implemented an audio-input LLM pipeline (Whisper transcription + GPT-4o/Claude multi-prompt analysis + Python post-processing) to convert thousands of stablecoin podcast episodes into structured data on sentiment, regulatory views, and key themes.
  • Read the paper on NBER »

Crypto Shadow Banking: Stablecoins, Crypto Assets and Capital Controls Permalink

Published:

Job Market Paper  ·  Advisor: Prof. Tomas Williams

  • Developed a large-scale LLM- and text-based crypto exposure dataset from U.S. public firms’ financial filings (10-K, 8-K, and 20-F; 4,696 firm-year observations, 2015–2025) to detect hidden digital-asset exposure, stablecoin usage, and crypto-based capital-control bypassing behavior.
  • Using Probit/Logit regressions, showed that balance-sheet proxies such as intangible assets and inventories predict crypto-related corporate activity, and that these proxies rise significantly following capital-control and equity-market intervention shocks — revealing the rise of “crypto shadow banking” as a substitute channel for cross-border liquidity.
  • Built a quantitative model assessing the impact of crypto shadow banking on the stability of the financial system.

Daily Capital Control Index: Powered by Machine Learning

Joint work with: Prof. Roberto Samaniego This project focuses on the development of a high-frequency Daily Capital Control Index for 119 countries, spanning from January 1, 2000, to the present. The index tracks six categories of capital account interventions, offering a real-time tool for the tracking and analysis of global capital control policies. By providing timely and granular insights, the index serves as a critical resource for researchers, policymakers, and market participants seeking to understand cross-country differences in capital control measures. To ensure precision, the index employs machine learning techniques, including Linear Regression and LASSO, trained on the Ka-open Index from the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER). The project also includes the development of a comprehensive dataset and a user-friendly website that enables real-time updates and dynamic access to the dataset, facilitating seamless access to up-to-date capital control information.

teaching

Instructor

ECON1011 Principle of Macroeconomics, George Washington University, 2022

  • Delivered lectures independently: Taught four days per week during the summer session, with 90-minute classes each day, effectively managing course content and student engagement.
  • Created the syllabus and learning materials: Developed a comprehensive syllabus, prepared engaging learning materials, and conducted exams to provide a well-structured and complete learning experience.
  • Achieved a 100% student satisfaction rate: Students praised the clarity and engagement of lectures, as well as the effectiveness of the learning materials and the support provided throughout the session.

Teaching Assistant

ECON 8306 Macroeconomics II (PhD Level), George Washington University, 2023