Fuli Yang

Fuli Yang, Ph.D.
Quantitative Finance & Financial Econometrics
GARCH & Volatility Modeling
*All views are personal, not investment advice.

[Fuli Yang @DrFuliYang]

https://t.co/zKKslP1wlH

[Fuli Yang @DrFuliYang]

https://t.co/U9pojxiJQF

[Fuli Yang @DrFuliYang]

Corporate separations offer a useful lens on investor protection. They expose how ownership, decision rights, and economic benefits are redistributed within a transaction—and how the rules differ across markets.

[Bayes Atlas @BayesAtlas]

Who gets the shares, who gets a vote, and who keeps control? Corporate splits put investor protection to a practical test. Different markets draw the lines differently, with real consequences for shareholders. https://t.co/jjalGsp8e0

[Fuli Yang @DrFuliYang]

https://t.co/7nGzSTUa3H

[Fuli Yang @DrFuliYang]

https://t.co/4XMVUPxBjo

[Fuli Yang @DrFuliYang]

New paper: investor protection in a cross-market spin-off is not just about whether an entitlement exists, but who can actually exercise it. I develop a shareholder–market framework for legal eligibility, custody routes, cash substitution, and shareholder voice.
https://t.co/1J5CA5mnlY

[Fuli Yang @DrFuliYang]

On 21 September 2026, HKEX proposed a major redesign of Practice Note 15: qualifying listed parents could self-assess spin-offs, the moratorium could fall from three years to one, assured entitlement could be removed, and the shareholder-approval threshold could rise from 25% to 50%.

New working paper examines whether board certification, disclosure and ex post accountability can preserve investor protection.

https://t.co/4xAdErUxzh

[Fuli Yang @DrFuliYang]

Understand whether accurate AI traders could still create fragile markets. My new video explores how correlated beliefs become synchronized trades in a controlled simulation.

Watch the full 13-minute video:
https://t.co/ByAxETzZtk

[Fuli Yang @DrFuliYang]

https://t.co/SxLE53IqIF

[Fuli Yang @DrFuliYang]

A forecast can be statistically different and still produce exactly the same portfolio.

And when models suddenly disagree, that disagreement may reveal fragility before the next outcome is observed.

Companion videos:

Different Volatility Forecasts, Same Portfolio? Decision Equivalence Explained
https://t.co/moXvaK4Yli
Can Model Disagreement Warn of Volatility Forecast Failure?
https://t.co/Q8fbGjIMdT

Papers:
Decision-Equivalent Volatility Forecasts
https://t.co/Nuzyal2Nlw

Forecast Disagreement and Conditional Model Fragility
https://t.co/5zQ70ar7Mz

[Fuli Yang @DrFuliYang]

https://t.co/OclwCXNumN

[Fuli Yang @DrFuliYang]

https://t.co/RY5JQa3iLz

[Fuli Yang @DrFuliYang]

https://t.co/MolFsCGZwA

[Fuli Yang @DrFuliYang]

https://t.co/mRnSfq0VUj

[Fuli Yang @DrFuliYang]

https://t.co/m2K5F8qdaa

[Fuli Yang @DrFuliYang]

https://t.co/v5tKkMpbJK

[Fuli Yang @DrFuliYang]

Agreed — treating volatility as latent means any ranking is conditional on the proxy and the loss.

That’s why the paper evaluates both statistical losses and joint VaR–ES: the preferred model can shift once the objective moves from variance tracking to tail-risk usefulness.

Your point on portfolio-level filters is especially useful. Turnover, overlap, concentration and capacity only become visible when signals are used jointly rather than evaluated in isolation. Extending the framework in that direction seems a natural next step — would be interested in how you would incorporate them in practice.

Thanks for the thoughtful comment.

[Fuli Yang @DrFuliYang]

https://t.co/wNMsSCPc7a

[Fuli Yang @DrFuliYang]

https://t.co/6npMQDPXA6

[Fuli Yang @DrFuliYang]

https://t.co/rZIRCZOxu1

[Fuli Yang @DrFuliYang]

https://t.co/bjZtltRM60